# Gradus India

Gradus India (managed by MDM Madhubani Technologies Private Limited) bridges India's skills gap crisis by providing intensive, industry-grade, and project-based training programs. We offer flagship career acceleration courses in software development, agentic AI, backend engineering, data science, cybersecurity, and more.

## Brand Information
- **Parent Company**: MDM MADHUBANI TECHNOLOGIES PRIVATE LIMITED
- **Official Brands**: Gradus India | Gradus
- **Websites**: https://www.gradusindia.in | https://www.gradus.live
- **Support Contact**: contact@gradusindia.in
- **Toll-Free Helpline**: 1800-569-3838 (Monday - Saturday, 10:00 AM - 07:00 PM IST)

---

## About Us & Pedagogical Approach

### Our Mission
To transform traditional educational systems into fast-tracked, industry-aligned learning tracks that emphasize speed-to-skill and tangible proof-of-work.

### Five Pillars of Excellence
1. **Webinars**: Expert-led sessions on cutting-edge tech.
2. **Masterclasses**: Deep-dive workshops with industry leaders.
3. **College Partnerships**: Direct integration, seminars, and drives.
4. **Summer/Winter Camps**: Intensive bootcamps for rapid skill building.
5. **Hackathons**: Real-world problem-solving competitions.

### Our Approach
- **Speed to Skill**: Minimizing the transition time from learning to practical implementation.
- **Proof Over Theory**: Every track is centered around building real-world projects and robust portfolios.
- **Hiring Embedded**: Career outcomes, mock interviews, and placements are integrated directly into the curriculum.
- **Data-Backed Guidance**: Utilising metrics and tracking to ensure and optimize learner success.

---

## Frequently Asked Questions (FAQs)

### Who can apply for the program?
Any college student currently enrolled in a recognized institution can apply. No prior technical experience is required for beginner-level tracks.

### What is the schedule?
Programs run with flexible daily sessions designed to accommodate college schedules, combining live lectures, workshops, and project time.

### Is there a fee?
Program fees vary based on the track and batch. Affordable student pricing and flexible payment options are available.

### Will I get a certificate?
Yes, you will receive an industry-recognized Gradus Training Certificate upon successful completion of the program and capstone project.

### What if I miss a session?
All sessions are recorded and uploaded within 24 hours.

### Is placement guaranteed?
We provide 100% placement assistance, resume reviews, mock interviews, and introductions to hiring partners. Final placement depends on performance and market conditions.

---

## Flagship Programs Catalog

### Data Science & Machine Learning
- **URL**: https://www.gradus.live/course/data-science-machine-learning
- **Subtitle**: Master the Complete Data Science Stack — From Fundamentals to Production-Grade AI
- **Target Audience**: Fresh graduates, working professionals, career-switchers, and anyone from any educational background.
- **Duration**: 6 Months · 144+ Hours · 4 Capstone
- **Level**: Beginner to Advanced
- **Mode**: Online
- **Language**: English
- **Rating**: 4.7 (72 reviews)
- **Average Salary Hike**: 40–70%
- **Pricing**: Original: ₹99,000
- **Overview**: The Gradus Flagship Data Science & AI Program is a 6-month, career-focused intensive designed to transform you into a job-ready data science professional. Unlike short bootcamps that give you a taste, this program goes deep — covering the full spectrum from programming fundamentals and statistics to advanced machine learning, deep learning, natural language processing, computer vision, generative AI, and MLOps deployment. Every module is built around real-world industry scenarios, hands-on projects, and datasets sourced from actual business problems. You'll build 4+ capstone projects across different domains, prepare for technical interviews with structured mock sessions, optimize your LinkedIn presence, create a professional GitHub portfolio, and receive dedicated career support from our placement team working with 300+ industry partners.

#### Prerequisites
- No prior programming experience required
- Basic comfort with using software and browsing the internet
- Laptop with 8GB RAM (Windows/Mac/Linux)
- High school level math
- Commitment of 8-10 hours/week

#### Skills Gained
- Python Programming
- Advanced Statistics
- Machine Learning
- Deep Learning
- Generative AI
- NLP
- Computer Vision
- SQL Mastery
- Data Visualization
- MLOps
- Big Data
- A/B Testing
- Feature Engineering
- Storytelling
- Interview Prep

#### Tools & Frameworks
- Pandas
- NumPy
- Scikit-learn
- TensorFlow
- PyTorch
- PostgreSQL
- MongoDB
- Docker
- AWS
- GCP
- Apache Spark
- MLflow
- LangChain
- OpenAI API
- Hugging Face

#### Learning Outcomes
- Write production-quality Python code for data manipulation and automation
- Apply statistical methods and probability theory to solve business problems
- Build end-to-end machine learning pipelines from ingestion to deployment
- Design and train deep learning models using TensorFlow and PyTorch
- Implement NLP systems including BERT and fine-tuned LLMs
- Understand Generative AI concepts: Prompt Engineering, RAG, and Agents
- Deploy models using Docker, FastAPI, and Cloud (AWS/GCP)
- Master Advanced SQL: Window Functions, CTEs, and Query Optimization
- Build interactive dashboards using Power BI, Tableau, and Plotly Dash
- Create a professional GitHub portfolio with 4+ industry capstones

#### Career Outcomes
- Qualify for Data Scientist, ML Engineer, and AI Engineer roles
- Build a production-grade portfolio with 4+ industry projects
- Crack technical and HR interviews with recorded mock feedback
- Establish professional presence with LinkedIn Profile Optimization
- Access 300+ hiring partners and dedicated placement support
- Understand full lifecycle from raw data to monitored production models

#### Capstone Project
4 Grand Capstone Projects: E-Commerce Behaviour Analytics, Credit Risk scoring, AI-Powered document intelligence, and a production-grade Healthcare Analytics Platform.

#### Target Job Roles & Packages
- **Data Scientist**: ₹8–20 LPA
- **Machine Learning Engineer**: ₹10–25 LPA
- **Data Analyst (Senior)**: ₹6–14 LPA
- **NLP / AI Engineer**: ₹10–22 LPA
- **Generative AI Engineer**: ₹12–30 LPA
- **Business Intelligence Analyst**: ₹6–15 LPA
- **Data Engineer (Entry–Mid)**: ₹8–18 LPA
- **MLOps Engineer**: ₹10–22 LPA
- **Analytics Consultant**: ₹8–18 LPA
- **Research Analyst (AI/ML Focus)**: ₹6–14 LPA

#### Syllabus Modules
- **Module **: Phase 1: Foundations
  *Type*: chapter | *Estimated Hours*: N/A
- **Module **: Capstone 1: E-Commerce Behavioral Platform
  Analyze 100K+ transactions from an e-commerce company. Clean messy data, perform full EDA, build
SQL queries for segment analysis, create an interactive Plotly Dash dashboard, and present business
recommendations with visualized KPIs — average order value, customer lifetime value, repeat purchase
rate, and revenue by segment.
  *Type*: project | *Estimated Hours*: N/A
- **Module **: Phase 2: Machine Learning
  *Type*: chapter | *Estimated Hours*: N/A
- **Module **: Capstone 2: Credit Risk & Segment Engine
  Build a dual ML system for a banking client: 
(1) A classification model that predicts loan default risk using
XGBoost with SHAP-based interpretability
(2) A customer segmentation engine using K-Means clustering on RFM features. Deliver results via an interactive Streamlit app with risk scores and segment profiles
  *Type*: project | *Estimated Hours*: N/A
- **Module **: Phase 3: Deep Learning, NLP & CV
  *Type*: chapter | *Estimated Hours*: N/A
- **Module **: Capstone 3: Document Intelligence System
  Build an end-to-end NLP pipeline that ingests business documents (contracts, invoices, reports), extracts
key entities using NER, classifies documents by type using a fine-tuned BERT model, generates
automated summaries, and serves results via a Streamlit web interface. Includes model evaluation, error
analysis, and a deployment-ready architecture.
  *Type*: project | *Estimated Hours*: N/A
- **Module **: Phase 4: GenAI, MLOps & Career
  *Type*: chapter | *Estimated Hours*: N/A
- **Module **: Capstone 4: Full-Stack AI-Powered Healthcare Analytics & Prediction Platform
  Design and deploy a production-grade data science platform for a healthcare provider. The system
includes: 
(1) EDA and dashboard for patient demographics and treatment outcomes
(2) ML classification model predicting hospital readmission risk (XGBoost + SHAP)
(3) NLP module extracting key information from clinical notes using fine-tuned BERT
(4) RAG-based Q&A; chatbot for internal medical guidelines
(5) Full deployment on AWS with FastAPI backend, Streamlit frontend, Docker containerization, MLflow tracking, and data drift monitoring. Presented as a final portfolio project.
  *Type*: project | *Estimated Hours*: N/A
- **Module 1**: Python Programming Mastery
  ● Python fundamentals: variables, data types, operators, type casting
● Control flow: conditionals, loops, nested logic, iteration patterns
● Data structures deep dive: lists, tuples, sets, dictionaries, nested structures
● Functions: parameters, return values, scope, recursion, *args, **kwargs
● List comprehensions, lambda functions, map, filter, reduce
● Object-Oriented Programming: classes, objects, inheritance, encapsulation
● File handling: CSV, JSON, text — reading, writing, and parsing
● Error handling: try-except, custom exceptions, debugging techniques
● Python best practices: PEP 8, code documentation, virtual environments
● Mini Project: Build a command-line data processing tool
  *Type*: lesson | *Estimated Hours*: 12
- **Module 2**: Mathematics & Statistics for Data Science
  ● Linear algebra essentials: vectors, matrices, dot products, eigenvalues
● Calculus basics: derivatives, gradients, chain rule (intuition for ML)
● Descriptive statistics: central tendency, dispersion, skewness, kurtosis
● Probability theory: Bayes' theorem, conditional probability, independence
● Probability distributions: Normal, Binomial, Poisson, Uniform, Exponential
● Inferential statistics: sampling, CLT, confidence intervals, margin of error
● Hypothesis testing: z-test, t-test, chi-square, ANOVA, p-values
● Correlation, covariance, and regression foundations
● Hands-on: Statistical analysis on real-world survey and business datasets
  *Type*: lesson | *Estimated Hours*: 6
- **Module 3**: Data Wrangling & Analysis with Pandas
  ● NumPy foundations: arrays, broadcasting, vectorized operations, reshaping
● Pandas deep dive: Series, DataFrames, MultiIndex, data I/O (CSV, Excel, JSON, SQL, Parquet)
● Data cleaning: missing values (detection, imputation strategies — mean, median, KNN, forward/backward
fill)
● Data transformation: encoding, binning, normalization, log transforms
● GroupBy operations, aggregations, pivot tables, cross-tabulations
● Merging, joining, and concatenating DataFrames (inner, outer, left, right, cross)
● Working with date/time data: parsing, resampling, rolling windows, time series basics
● Regular expressions for data cleaning and text extraction
● Advanced Pandas: apply, pipe, query, eval, memory optimization
● Mini Project: Clean and analyze a real e-commerce transactions dataset (50K+ rows)
  *Type*: lesson | *Estimated Hours*: 8
- **Module 4**: Data Visualization & Storytelling
  ● Visualization theory: principles of visual encoding, Gestalt principles, chart selection
● Matplotlib mastery: subplots, annotations, styling, custom themes, publication-quality plots
● Seaborn for statistical visualization: distribution plots, regression plots, categorical plots, pair plots,
heatmaps
● Plotly for interactive visualizations: scatter, bar, line, sunburst, treemap, choropleth maps
● Dashboard building with Plotly Dash: layouts, callbacks, multi-page apps
● Introduction to Power BI / Tableau: connecting data, building dashboards, publishing reports
● Storytelling with data: structuring a narrative, audience awareness, executive presentations
● Advanced Excel for analytics: pivot tables, VLOOKUP, INDEX-MATCH, conditional formatting, macros
● Mini Project: Build an interactive business dashboard from a retail dataset

  *Type*: lesson | *Estimated Hours*: 8
- **Module 5**: SQL & Database Fundamentals
  ● Relational database concepts: tables, schemas, normalization (1NF, 2NF, 3NF), ER diagrams
● SQL fundamentals: SELECT, WHERE, ORDER BY, LIMIT, DISTINCT, aliases
● Aggregations: GROUP BY, HAVING, COUNT, SUM, AVG, MIN, MAX
● JOINs mastery: INNER, LEFT, RIGHT, FULL OUTER, SELF, CROSS joins
● Subqueries: scalar, correlated, EXISTS, IN — and when to use each
● Common Table Expressions (CTEs) and recursive queries
● Window functions: ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD, NTILE, running totals
● Query optimization: indexing, execution plans, query profiling
● Introduction to NoSQL: MongoDB basics — documents, collections, CRUD, aggregation pipeline
● Mini Project: Solve 30 business SQL problems on a simulated company database

  *Type*: lesson | *Estimated Hours*: 6
- **Module 6**: EDA & Feature Engineering
  ● The EDA framework: structured approach to data investigation
● Univariate analysis: distributions, outlier detection (IQR, Z-score, Isolation Forest)
● Bivariate and multivariate analysis: scatter matrices, correlation analysis, ANOVA
● Feature engineering: creating new features, polynomial features, domain-based feature creation
● Encoding categorical variables: Label, One-Hot, Ordinal, Target, Frequency encoding
● Feature scaling: StandardScaler, MinMaxScaler, RobustScaler — when to use which
● Feature selection: correlation-based, mutual information, RFE, L1 regularization
● Handling imbalanced datasets: SMOTE, ADASYN, class weights, undersampling strategies
● Dimensionality reduction: PCA (theory + implementation), t-SNE, UMAP visualization
● Hands-on: Complete feature engineering pipeline on a real banking dataset

  *Type*: lesson | *Estimated Hours*: 6
- **Module 7**: Supervised Learning — Regression
  ● Linear Regression: OLS, gradient descent, closed-form solution, assumptions and diagnostics
● Multiple Linear Regression: multicollinearity, VIF, adjusted R-squared
● Polynomial Regression and feature interaction terms
● Regularization: Ridge (L2), Lasso (L1), Elastic Net — theory, math, and tuning
● Decision Tree Regressor: splitting criteria, pruning, feature importance
● Random Forest Regressor: bagging, out-of-bag estimation, hyperparameter tuning
● Gradient Boosting Regression: XGBoost, LightGBM, CatBoost — theory and implementation
● Support Vector Regression (SVR): kernel trick, epsilon-insensitive loss
● Model evaluation: R-squared, Adjusted R-squared, MAE, MSE, RMSE, MAPE
● Cross-validation: K-Fold, Stratified K-Fold, Leave-One-Out, time-series split
● Hyperparameter tuning: GridSearchCV, RandomizedSearchCV, Bayesian Optimization
● Hands-on: Predict property prices using real Bangalore housing data

  *Type*: lesson | *Estimated Hours*: 8
- **Module 8**: Supervised Learning — Classification
  ● Logistic Regression: sigmoid function, decision boundary, log-loss, multiclass (OVR, softmax)
● K-Nearest Neighbors: distance metrics, curse of dimensionality, optimal K selection
● Naive Bayes: Gaussian, Multinomial, Bernoulli — when and why it works
● Support Vector Machines: linear kernel, RBF, polynomial, kernel trick, margin maximization
● Decision Trees for classification: Gini impurity, entropy, information gain
● Random Forest Classifier: feature importance, out-of-bag score, ensemble power
● Gradient Boosting: XGBoost, LightGBM, CatBoost for classification
● Evaluation: Accuracy, Precision, Recall, F1, AUC-ROC, Precision-Recall curves, log-loss
● Confusion matrix analysis, classification reports, threshold tuning
● Model interpretation: SHAP values, LIME, permutation importance
● Hands-on: Build a credit risk scoring model on a real banking dataset
  *Type*: lesson | *Estimated Hours*: 8
- **Module 9**: Unsupervised Learning
  ● Clustering: K-Means (elbow method, silhouette score), K-Medoids
● Hierarchical clustering: agglomerative, dendrograms, linkage methods
● DBSCAN: density-based clustering, epsilon-neighborhood, handling noise
● Gaussian Mixture Models: soft clustering, EM algorithm
● Dimensionality reduction: PCA deep dive, explained variance, scree plots
● t-SNE and UMAP for high-dimensional visualization
● Association Rule Mining: Apriori, support, confidence, lift
● Anomaly detection: Isolation Forest, Local Outlier Factor, One-Class SVM
● Hands-on: Customer segmentation for a retail company (RFM analysis + clustering)

  *Type*: lesson | *Estimated Hours*: 6
- **Module 10**: End-to-End ML Pipeline
  ● Scikit-learn pipelines: ColumnTransformer, Pipeline, FeatureUnion
● Automated feature preprocessing and model training pipelines
● Ensemble methods: Stacking, Blending, Voting classifiers
● Time series fundamentals: trend, seasonality, stationarity (ADF test)
● Time series models: ARIMA, SARIMA, Prophet, exponential smoothing
● Recommender systems: collaborative filtering, content-based, hybrid approaches
● A/B testing: statistical significance, effect size, power analysis, Bayesian A/B
● ML model serialization: Pickle, Joblib, ONNX
● Introduction to ML experiment tracking with MLflow
● Hands-on: Build and deploy a complete ML pipeline with automated preprocessing
  *Type*: lesson | *Estimated Hours*: 8
- **Module 11**: Deep Learning Foundations
  ● Neural network architecture: perceptrons, activation functions (ReLU, sigmoid, tanh, softmax)
● Forward propagation and backpropagation — the math and intuition
● Loss functions: MSE, cross-entropy, binary cross-entropy, focal loss
● Optimizers: SGD, Adam, RMSprop, learning rate scheduling, warm-up
● Regularization for neural nets: dropout, batch normalization, early stopping, L2 regularization
● Building neural networks with TensorFlow/Keras: Sequential and Functional API
● Introduction to PyTorch: tensors, autograd, nn.Module, DataLoader
● Convolutional Neural Networks (CNNs): convolution, pooling, filters, architectures (VGG, ResNet)
● Recurrent Neural Networks (RNNs): vanishing gradients, LSTM, GRU
● Transfer learning: using pre-trained models (ImageNet), fine-tuning strategies
● GPU training setup: Google Colab Pro, CUDA basics
● Hands-on: Build an image classifier using CNN with transfer learning (ResNet50)

  *Type*: lesson | *Estimated Hours*: 8
- **Module 12**: Natural Language Processing (NLP)
  ● Text preprocessing: tokenization, stemming, lemmatization, stop words, regex cleaning
● Text representation: Bag of Words, TF-IDF, N-grams
● Word embeddings: Word2Vec (CBOW, Skip-gram), GloVe, FastText
● Text classification: sentiment analysis, spam detection, topic categorization
● Named Entity Recognition (NER) with spaCy and custom models
● Sequence models for text: LSTM, Bi-LSTM, GRU for text generation and classification
● Attention mechanism: self-attention, multi-head attention — the foundation of Transformers
● Transformer architecture: encoder-decoder, positional encoding, layer normalization
● BERT: pre-training (MLM, NSP), fine-tuning for downstream tasks
● Hugging Face Transformers library: model hub, tokenizers, pipelines, fine-tuning
● Text summarization, question answering, and text generation with pre-trained models
● Hands-on: Build a multi-class news article classifier using fine-tuned BERT

  *Type*: lesson | *Estimated Hours*: 8
- **Module 13**: Computer Vision & Advanced DL
  ● Image fundamentals: pixels, channels, color spaces, image transformations
● Image augmentation: flipping, rotation, cropping, color jittering, Albumentations library
● Advanced CNN architectures: Inception, EfficientNet, MobileNet for edge deployment
● Object detection fundamentals: YOLO overview, bounding boxes, IoU, NMS
● Image segmentation: semantic vs instance segmentation, U-Net architecture
● Generative Adversarial Networks (GANs): generator, discriminator, training dynamics
● Autoencoders and Variational Autoencoders (VAEs) for anomaly detection
● Model optimization: quantization, pruning, knowledge distillation
● Hands-on: Build a medical image classification system using EfficientNet + transfer learning
  *Type*: lesson | *Estimated Hours*: 6
- **Module 14**: Generative AI & LLM Apps
  ● The generative AI landscape: GPT, Claude, Gemini, Llama, Mistral — architecture overview
● How LLMs work: tokenization, attention, context windows, temperature, top-k/top-p sampling
● Prompt engineering: zero-shot, few-shot, chain-of-thought, system prompts, prompt templates
● OpenAI API / Anthropic API: building applications with structured outputs
● Embeddings: text-to-vector, similarity search, vector databases (FAISS, Pinecone, ChromaDB)
● Retrieval-Augmented Generation (RAG): architecture, chunking, retrieval, re-ranking
● Building RAG applications with LangChain: document loaders, text splitters, chains, memory
● Fine-tuning LLMs: LoRA, QLoRA, PEFT — when and how to fine-tune vs prompt engineering
● AI safety and responsible AI: hallucination detection, guardrails, bias mitigation
● Hands-on: Build a RAG-powered Q&A; system over custom company documents using LangChain

  *Type*: lesson | *Estimated Hours*: 8
- **Module 15**: MLOps & Production Deployment
  ● MLOps lifecycle: development, staging, production, monitoring, retraining
● Experiment tracking and model registry with MLflow
● Model serving with FastAPI: building REST APIs for ML models
● Containerization with Docker: Dockerfiles, images, containers, Docker Compose
● Cloud deployment: AWS (SageMaker, EC2, S3, Lambda), GCP (Vertex AI, Cloud Run)
● CI/CD for ML: GitHub Actions, automated testing, model validation pipelines
● Model monitoring: data drift detection, performance degradation, alerting
● Streamlit and Gradio for rapid prototyping and internal tools
● Git workflows for data science: branching, code reviews, collaboration best practices
● Hands-on: Deploy a complete ML model to AWS with monitoring and automated retraining triggers

  *Type*: lesson | *Estimated Hours*: 6
- **Module 16**: Big Data & Advanced Analytics
  ● Big data ecosystem overview: Hadoop, HDFS, MapReduce concepts
● Apache Spark: RDDs, DataFrames, Spark SQL, PySpark for large-scale data processing
● Data pipelines: ETL vs ELT, batch vs streaming, Apache Airflow basics
● Data warehousing concepts: star schema, snowflake schema, OLAP vs OLTP
● Cloud data platforms: AWS Redshift, Google BigQuery, Snowflake overview
● Real-time analytics: streaming data concepts, Kafka overview
● Hands-on: Process and analyze a 10M+ row dataset using PySpark on Colab/Databricks

  *Type*: lesson | *Estimated Hours*: 4

---

### Cloud Computing & DevOps Mastery
- **URL**: https://www.gradus.live/course/cloud-computing-devops-mastery
- **Subtitle**: Master Multi-Cloud Architecture & DevOps Engineering — AWS, Azure, Google Cloud, Kubernetes, Terraform & CI/CD Pipelines
- **Target Audience**: Fresh graduates, working professionals, career-switchers, and anyone from any educational background.
- **Duration**: 6 Months · 144+ Hours · 4 Capstone 
- **Level**: Beginner to Advanced
- **Mode**: Online
- **Language**: English
- **Rating**: 4.8 (47 reviews)
- **Average Salary Hike**: 50–100%
- **Pricing**: Original: ₹99,000
- **Overview**: The Gradus Flagship Cloud Computing & DevOps Mastery Program is a 6-month, career-focused intensive designed to transform you into a job-ready cloud and DevOps professional. Unlike short bootcamps that give you a taste, this program goes deep — covering the full spectrum from Linux, networking, and shell scripting to multi-cloud architecture across AWS, Azure, and Google Cloud, containerization with Docker and Kubernetes, infrastructure as code with Terraform and Ansible, CI/CD pipeline design with Jenkins, GitHub Actions, and GitLab CI, observability with Prometheus, Grafana, and ELK Stack, and cloud security best practices. Every module is built around real-world industry scenarios, hands-on labs, and production-grade deployments. Includes 4+ capstone projects, certification preparation (AWS/Azure/GCP/CKA), and dedicated placement support with 300+ hiring partners.

#### Prerequisites
- No prior cloud or DevOps experience required — we start from Linux basics and build up
- Basic comfort with using a computer, web browser, and installing software
- A working laptop with at least 8 GB RAM and stable internet connection
- Basic logical thinking — we teach the programming, scripting, and cloud concepts from scratch
- Curiosity, discipline, and willingness to commit 8–10 hours per week for 6 months
- Open to students from any background (Commerce, Arts, Science, Engineering, or other)
- No age restriction — designed for both freshers and professionals

#### Skills Gained
- Linux & Shell Scripting
- Computer Networking
- AWS Cloud Services
- Microsoft Azure
- Google Cloud Platform
- Docker & Containerization
- Kubernetes & Orchestration
- Terraform & IaC
- Ansible & Configuration Mgmt
- CI/CD Pipelines
- GitOps & ArgoCD
- Monitoring & Observability
- Cloud Security & IAM
- Python for DevOps
- Certification Preparation
- Portfolio & Personal Branding

#### Tools & Frameworks
- Linux (Ubuntu/CentOS)
- Bash & Shell Scripting
- Python
- Git & GitHub
- AWS
- Microsoft Azure
- Google Cloud (GCP)
- Docker
- Kubernetes
- Helm
- Terraform
- Ansible
- Jenkins
- GitHub Actions
- GitLab CI/CD
- ArgoCD
- Prometheus
- Grafana
- ELK Stack
- CloudWatch / Azure Monitor
- Nginx / Apache
- HashiCorp Vault
- SonarQube
- Trivy / Snyk

#### Learning Outcomes
- Navigate Linux systems confidently — command line, file systems, permissions, shell scripting, and process management
- Understand computer networking fundamentals — TCP/IP, DNS, HTTP/HTTPS, load balancing, firewalls, and VPNs
- Architect and deploy scalable infrastructure on AWS — EC2, S3, VPC, RDS, Lambda, CloudFormation, and IAM
- Build cloud solutions on Microsoft Azure — VMs, App Services, Azure DevOps, AKS, and Azure Active Directory
- Design and manage services on Google Cloud Platform — Compute Engine, GKE, Cloud Functions, and BigQuery
- Containerize applications with Docker — images, Dockerfiles, multi-stage builds, Docker Compose, and registries
- Orchestrate containers at scale with Kubernetes — pods, deployments, services, Helm charts, and autoscaling
- Implement Infrastructure as Code with Terraform — modules, state management, multi-cloud provisioning
- Automate configuration management with Ansible — playbooks, roles, inventories, and dynamic provisioning
- Design CI/CD pipelines with Jenkins, GitHub Actions, GitLab CI/CD, and ArgoCD for GitOps workflows
- Set up production-grade monitoring and observability — Prometheus, Grafana, ELK Stack, CloudWatch, and alerting
- Implement cloud security best practices — IAM policies, secrets management, network security, and compliance
- Write Python and Bash scripts for cloud automation — Boto3, Azure SDK, gcloud CLI, and custom tooling
- Prepare for industry certifications — AWS Solutions Architect, Azure Administrator, GCP Associate Cloud Engineer
- Crack DevOps and cloud interviews with structured preparation — system design, scenario-based, and coding rounds

#### Career Outcomes
- Qualify for mid-to-senior level DevOps Engineer, Cloud Architect, SRE, and Platform Engineer roles
- Build a production-grade portfolio with 4+ industry capstone projects on GitHub
- Prepare for and clear AWS, Azure, and GCP certification exams with structured study plans
- Crack technical and HR interviews with structured mock preparation and feedback
- Establish a professional presence on LinkedIn with optimized profile and content strategy
- Develop skills valued across IT, BFSI, healthcare, e-commerce, consulting, and government
- Understand the full DevOps lifecycle — from code commit to production deployment with monitoring
- Work with modern cloud-native tools — Kubernetes, Terraform, GitOps, Prometheus — that define 2026 hiring demand
- Access the Gradus career ecosystem with 300+ hiring partners and dedicated placement support
- Position yourself for a 50–100% salary hike when entering or transitioning into cloud/DevOps roles

#### Capstone Project
Deploy and automate a scalable, production-ready application on the cloud using CI/CD pipelines (Jenkins/GitHub Actions), container orchestration with Kubernetes, and Infrastructure as Code using Terraform. Deliverables include a fully automated deployment pipeline and monitoring dashboard.

#### Target Job Roles & Packages
- **DevOps Engineer**: ₹10–25 LPA
- **Cloud Solutions Architect**: ₹15–35 LPA
- **Site Reliability Engineer (SRE)**: ₹12–30 LPA
- **Platform Engineer**: ₹12–28 LPA
- **Cloud Engineer**: ₹8–22 LPA
- **Infrastructure Engineer**: ₹8–20 LPA
- **Release / Build Engineer**: ₹8–18 LPA
- **Cloud Security Engineer**: ₹12–28 LPA
- **MLOps Engineer**: ₹12–28 LPA
- **Systems Administrator (Cloud)**: ₹6–15 LPA

#### Syllabus Modules
- **Module **: Phase 1: Foundations
  *Type*: chapter | *Estimated Hours*: N/A
- **Module **: Capstone Project 1: Automated Multi-Server Infrastructure Setup & Monitoring
  Design and automate the provisioning of a multi-server Linux environment using shell scripts and Python. Configure
networking, firewall rules, user access, and SSH hardening. Implement a health monitoring system with automated alerts,
log rotation, and a Bash-based dashboard that tracks CPU, memory, disk, and network metrics across all servers.
Document the architecture and present a runbook for the operations team.
  *Type*: project | *Estimated Hours*: N/A
- **Module **: Phase 2: Multi-Cloud Architecture
  *Type*: chapter | *Estimated Hours*: N/A
- **Module **: Capstone Project 2:Multi-Cloud E-Commerce Platform Architecture
  Architect and deploy a scalable e-commerce platform: 
(1) Primary infrastructure on AWS with VPC, EC2 Auto Scaling, RDS
Multi-AZ, S3, and CloudFront CDN
(2) Disaster recovery replica on Azure with VMs, Azure SQL, and Blob Storage
(3) Analytics pipeline on GCP BigQuery. Implement IAM policies, network security, SSL certificates, and cost optimization
across all three clouds. Present a multi-cloud architecture document with cost projections.
  *Type*: project | *Estimated Hours*: N/A
- **Module **: Phase 3: Containerization, IaC & CI/CD
  *Type*: chapter | *Estimated Hours*: N/A
- **Module **: Capstone Project 3: Production Kubernetes Platform with GitOps & IaC
  Build a production-grade Kubernetes platform: 
(1) Provision EKS cluster on AWS using Terraform with VPC, node groups, and IAM roles
(2) Deploy a microservices application (3+ services) using Helm charts 
(3) Configure Ingress with SSL termination, HPA autoscaling, and persistent storage 
(4) Set up Ansible playbooks for node configuration and security
hardening. Present architecture documentation with deployment runbook and disaster recovery procedures.
  *Type*: project | *Estimated Hours*: N/A
- **Module **: Phase 4: CI/CD, Monitoring, Security & Career
  *Type*: chapter | *Estimated Hours*: N/A
- **Module **: Capstone Project 4: GRAND CAPSTONE - Full-Stack Production Cloud & DevOps Platform
  Design and deploy a production-grade DevOps platform for a SaaS company. The system includes: 
(1) Multi-environment infrastructure on AWS provisioned with Terraform (dev, staging, production)
(2) Containerized microservices deployed on EKS with Helm charts and Ingress
(3) CI/CD pipeline with GitHub Actions triggering ArgoCD GitOps deployments 
(4) Full observability stack with Prometheus, Grafana dashboards, and ELK log management 
(5) Security hardening with IAM policies, Vault secrets, network policies, and container scanning. Presented as a final portfolio project with architecture documentation, runbooks, and a live demo.
  *Type*: project | *Estimated Hours*: N/A
- **Module 1**: Module 1: Linux Administration & Shell Scripting
  • Linux fundamentals: file system hierarchy, navigation, permissions (chmod, chown, ACLs)
• Essential commands: grep, sed, awk, find, xargs, sort, cut, pipes, and redirection
• User and group management, sudo, PAM, and access control policies
• Process management: ps, top, htop, systemctl, journalctl, cron jobs, and daemons
• Package management: apt, yum/dnf, snap — installing, updating, and dependency resolution
• Disk management: partitioning, LVM, filesystem types (ext4, XFS), mount, df, du
• Shell scripting deep dive: variables, loops, conditionals, functions, arrays, error handling
• Advanced scripting: regex patterns, text processing pipelines, log parsing automation
• SSH configuration, key-based authentication, SCP, rsync, and remote server management
• Mini Project: Build an automated server health monitoring script with email alerts

  *Type*: lesson | *Estimated Hours*: 12
- **Module 2**: Module 2: Computer Networking & Security Fundamentals
  • OSI and TCP/IP model: layers, protocols, data encapsulation, and packet flow
• IP addressing: IPv4, IPv6, subnetting, CIDR notation, public vs private IPs, NAT
• DNS deep dive: record types (A, AAAA, CNAME, MX, TXT), resolution process, DNS caching
• HTTP/HTTPS: request-response cycle, status codes, headers, SSL/TLS handshake, certificates
• Load balancing concepts: Layer 4 vs Layer 7, round-robin, least connections, health checks
• Firewalls and network security: iptables, security groups, NACLs, WAF concepts
• VPN and tunneling: site-to-site, client VPN, IPSec, WireGuard overview
• Proxy servers: forward proxy, reverse proxy (Nginx), caching proxy concepts
• Network troubleshooting tools: ping, traceroute, nslookup, dig, netstat, tcpdump, Wireshark
• Mini Project: Design a secure network architecture diagram for a 3-tier web application

  *Type*: lesson | *Estimated Hours*: 10
- **Module 3**: Module 3: Version Control with Git & Collaboration
  • Git fundamentals: init, add, commit, status, log, diff, .gitignore, and repository structure
• Branching strategies: feature branches, Git Flow, trunk-based development, release branches
• Merging and rebasing: fast-forward, 3-way merge, interactive rebase, conflict resolution
• Remote repositories: clone, push, pull, fetch, upstream tracking, GitHub/GitLab workflows
• Pull requests and code reviews: best practices, review checklists, approval workflows
• Git hooks: pre-commit, pre-push, commit-msg hooks for automation and quality gates
• Advanced Git: cherry-pick, stash, bisect, reflog, submodules, and monorepo strategies
• Mini Project: Set up a team Git workflow with branch protection, PR templates, and hooks

  *Type*: lesson | *Estimated Hours*: 6
- **Module 4**: Module 4: Python for DevOps & Cloud Automation
  • Python fundamentals: data types, control flow, functions, modules, error handling
• File operations: reading/writing files, CSV/JSON/YAML parsing, log file processing
Gradus.live — Flagship Program Cloud Computing & DevOps Mastery · 6 Months · Job Assurance Page 5
• Working with APIs: requests library, REST API consumption, authentication, pagination
• Boto3 for AWS automation: EC2, S3, IAM, Lambda, and CloudFormation operations
• Azure SDK for Python: resource management, VM operations, blob storage, Key Vault
• Google Cloud client libraries: Compute Engine, Cloud Storage, BigQuery automation
• Infrastructure scripting: automated provisioning, cleanup scripts, cost optimization tools
• Building CLI tools with Click/argparse for custom DevOps utilities
• Mini Project: Build a multi-cloud resource inventory and cost reporting tool in Python

  *Type*: lesson | *Estimated Hours*: 8
- **Module 5**: Module 5: AWS Cloud Architecture & Services
  • AWS Global Infrastructure: regions, availability zones, edge locations, and service endpoints
• IAM deep dive: users, groups, roles, policies, MFA, cross-account access, and least privilege
• Compute services: EC2 (instance types, AMIs, key pairs, security groups), Auto Scaling, ELB
• Storage services: S3 (buckets, versioning, lifecycle, replication), EBS, EFS, S3 Glacier
• Networking: VPC (subnets, route tables, internet gateway, NAT gateway), peering, Transit Gateway
• Database services: RDS (Multi-AZ, read replicas), DynamoDB, ElastiCache, Aurora overview
• Serverless: Lambda (triggers, layers, concurrency), API Gateway, Step Functions, EventBridge
• Infrastructure as Code: CloudFormation templates, stacks, change sets, nested stacks
• Messaging and decoupling: SQS, SNS, Kinesis overview
• Cost management: Cost Explorer, Budgets, Reserved Instances, Savings Plans, tagging strategies
• AWS Well-Architected Framework: reliability, security, cost optimization, performance, operational excellence
• Certification prep: AWS Solutions Architect Associate — exam strategy, practice questions, key domains
• Hands-on Lab: Deploy a highly available 3-tier web application on AWS with Auto Scaling and RDS
  *Type*: lesson | *Estimated Hours*: 14
- **Module 6**: Module 6: Microsoft Azure Cloud Services
  • Azure fundamentals: subscriptions, resource groups, management groups, Azure portal & CLI
• Azure Active Directory (Entra ID): tenants, users, groups, RBAC, conditional access, MFA
• Compute: Virtual Machines (availability sets, scale sets), App Services, Azure Functions
• Storage: Blob Storage (tiers), Azure Files, Table Storage, Queue Storage, Data Lake
• Networking: Virtual Networks (VNets), subnets, NSGs, Azure Load Balancer, Application Gateway, Azure DNS
• Database services: Azure SQL Database, Cosmos DB, Azure Database for PostgreSQL/MySQL
• Azure DevOps: Boards, Repos, Pipelines, Artifacts, Test Plans — end-to-end project management
• Azure Kubernetes Service (AKS): cluster creation, deployment, scaling, and monitoring
• Azure Monitor, Log Analytics, Application Insights for observability
• Certification prep: Azure Administrator Associate (AZ-104) — exam strategy and practice questions
• Hands-on Lab: Deploy a containerized application on AKS with Azure DevOps CI/CD pipeline
  *Type*: lesson | *Estimated Hours*: 10
- **Module 7**: Module 7: Google Cloud Platform (GCP)
  • GCP fundamentals: projects, billing accounts, IAM, Cloud Shell, gcloud CLI
• Compute: Compute Engine (instances, templates, managed instance groups), Cloud Run, Cloud Functions
• Storage: Cloud Storage (buckets, classes, lifecycle), Persistent Disks, Filestore
• Networking: VPC, subnets, firewall rules, Cloud Load Balancing, Cloud CDN, Cloud DNS
• Database services: Cloud SQL, Cloud Spanner, Firestore, Bigtable, BigQuery for analytics
• Google Kubernetes Engine (GKE): cluster creation, workload deployment, Autopilot mode
• CI/CD on GCP: Cloud Build, Cloud Deploy, Artifact Registry
• Monitoring: Cloud Monitoring, Cloud Logging, Error Reporting, Cloud Trace
• Certification prep: GCP Associate Cloud Engineer — exam strategy and practice questions
• Hands-on Lab: Deploy a microservices application on GKE with Cloud Build CI/CD

  *Type*: lesson | *Estimated Hours*: 8
- **Module 8**: Module 8: Multi-Cloud Strategy & Cost Optimization
  • Multi-cloud vs hybrid cloud: when and why to use multiple cloud providers
• Service mapping across AWS, Azure, and GCP: compute, storage, networking, databases, serverless
• Cloud cost optimization strategies: rightsizing, reserved capacity, spot/preemptible instances, auto-shutdown
• Cloud migration strategies: lift-and-shift, re-platform, re-architect, and the 6 R’s framework
• Disaster recovery patterns: pilot light, warm standby, multi-site active-active
• Hands-on: Design a multi-cloud architecture for a fintech company with DR across AWS and Azure

  *Type*: lesson | *Estimated Hours*: 4
- **Module 9**: Module 9: Docker & Containerization
  • Container fundamentals: containers vs VMs, namespaces, cgroups, container runtime architecture
• Docker deep dive: images, containers, Dockerfiles, layers, caching, and build context
• Multi-stage builds: optimizing image size, build vs runtime stages, security scanning
• Docker networking: bridge, host, overlay, custom networks, container-to-container communication
• Docker volumes and persistent storage: bind mounts, named volumes, volume drivers
• Docker Compose: multi-container applications, service dependencies, environment variables, profiles
• Docker registries: Docker Hub, AWS ECR, Azure ACR, GCP Artifact Registry — push, pull, tagging
• Container security: image scanning with Trivy, running as non-root, secrets management, read-only filesystems
• Docker best practices: .dockerignore, health checks, logging drivers, resource limits
• Hands-on: Containerize a full-stack application (React + Node.js + PostgreSQL) with Docker Compose
  *Type*: lesson | *Estimated Hours*: 10
- **Module 10**: Module 10: Kubernetes & Container Orchestration
  • Kubernetes architecture: control plane (API server, etcd, scheduler, controller manager), worker nodes (kubelet,
kube-proxy)
• Core objects: Pods, ReplicaSets, Deployments, Services (ClusterIP, NodePort, LoadBalancer), Namespaces
• Configuration: ConfigMaps, Secrets, environment variables, volume mounts
• Storage: PersistentVolumes, PersistentVolumeClaims, StorageClasses, dynamic provisioning
• Advanced workloads: StatefulSets, DaemonSets, Jobs, CronJobs
• Networking: Ingress controllers (Nginx), service mesh concepts (Istio overview), network policies
• Autoscaling: Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler (VPA), Cluster Autoscaler
• Helm: charts, values, templating, repositories, chart lifecycle management, and custom charts
• Managed Kubernetes: EKS (AWS), AKS (Azure), GKE (GCP) — setup, node pools, and upgrades
• Kubernetes security: RBAC, pod security standards, network policies, OPA/Gatekeeper
• Debugging: kubectl debugging, logs, events, describe, exec, resource quotas and limits
• Hands-on: Deploy a microservices application on Kubernetes with Helm, Ingress, HPA, and monitoring
  *Type*: lesson | *Estimated Hours*: 12
- **Module 11**: Module 11: Infrastructure as Code — Terraform & Ansible
  • IaC philosophy: declarative vs imperative, idempotency, state management, drift detection
• Terraform fundamentals: providers, resources, data sources, variables, outputs, locals
• Terraform state: remote backends (S3, Azure Blob, GCS), state locking, workspaces
• Advanced Terraform: modules, dynamic blocks, for_each, count, conditional expressions
• Terraform for multi-cloud: provisioning infrastructure across AWS, Azure, and GCP in one project
• Terraform best practices: directory structure, naming conventions, code review, plan/apply workflow
• Ansible fundamentals: inventory, playbooks, modules, handlers, variables, templates (Jinja2)
• Ansible roles: directory structure, dependencies, Galaxy roles, custom role creation
• Ansible for configuration management: package installation, service management, file templating
• Terraform + Ansible integration: provision with Terraform, configure with Ansible
• Hands-on: Provision a complete multi-tier infrastructure on AWS with Terraform and configure it with Ansible

  *Type*: lesson | *Estimated Hours*: 10
- **Module 12**: Module 12: CI/CD Pipelines & GitOps
  • CI/CD concepts: continuous integration, continuous delivery, continuous deployment, trunk-based development
• Jenkins: installation, Jenkinsfile (declarative & scripted), agents, shared libraries, Blue Ocean UI
• GitHub Actions: workflows, triggers, jobs, steps, matrix builds, secrets, custom actions, reusable workflows
• GitLab CI/CD: .gitlab-ci.yml, stages, jobs, runners, artifacts, environments, review apps
• ArgoCD for GitOps: application CRDs, sync policies, automated rollbacks, app-of-apps pattern
• Pipeline security: secrets management, SAST/DAST integration, image scanning in pipelines
• Artifact management: JFrog Artifactory, Nexus, GitHub Packages, container registries
• Testing in CI: unit tests, integration tests, smoke tests, and quality gate enforcement (SonarQube)
• Deployment strategies: blue-green, canary, rolling updates, feature flags
• Hands-on: Build an end-to-end CI/CD pipeline with Jenkins + ArgoCD deploying to Kubernetes
  *Type*: lesson | *Estimated Hours*: 10
- **Module 13**: Module 13: Monitoring, Observability & Logging
  • Observability pillars: metrics, logs, traces — and how they work together
• Prometheus: architecture, PromQL, service discovery, recording rules, alerting rules
• Grafana: dashboard creation, data sources, panels, variables, alerting, and templating
• ELK Stack: Elasticsearch (indexing, querying), Logstash (pipelines, filters), Kibana (visualizations)
• Cloud-native monitoring: AWS CloudWatch, Azure Monitor, GCP Cloud Monitoring — metrics, alarms, dashboards
• Distributed tracing: Jaeger / OpenTelemetry overview for microservices debugging
• Alerting strategies: PagerDuty / Opsgenie integration, on-call rotation, incident response
• SLIs, SLOs, and SLAs: defining and measuring service reliability
• Hands-on: Set up a full observability stack (Prometheus + Grafana + ELK) for a Kubernetes cluster
  *Type*: lesson | *Estimated Hours*: 6
- **Module 14**: Module 14: Cloud Security & Compliance
  • Cloud security fundamentals: shared responsibility model, defense in depth, zero trust principles
• Identity and access management: IAM best practices across AWS, Azure, and GCP
• Secrets management: HashiCorp Vault, AWS Secrets Manager, Azure Key Vault, GCP Secret Manager
• Network security: VPC security groups, NACLs, WAF, DDoS protection, private endpoints
• Container security: image scanning, runtime protection, pod security standards, supply chain security
• Compliance frameworks overview: SOC 2, HIPAA, PCI-DSS, GDPR — cloud compliance tools
• Security automation: AWS Config, Azure Policy, GCP Organization Policies, drift detection
• Incident response: runbooks, post-mortem process, blameless culture, and lessons learned
• Hands-on: Implement a security hardening checklist for a production cloud environment

  *Type*: lesson | *Estimated Hours*: 6

---

### Software Development
- **URL**: https://www.gradus.live/course/software-development
- **Subtitle**: Master Data Structures, Algorithms & Full-Stack Development — From Programming Fundamentals to System Design & Interview Mastery
- **Target Audience**: Coding Enthusiasts, Software Developers, and Career-Switchers from any background (Tech + Non-Tech welcome).
- **Duration**: 6 Months · 144+ Hours · 4 Capstone
- **Level**: Beginner to Intermediate
- **Mode**: Online / Hybrid
- **Language**: English
- **Rating**: 4.8 (258 reviews)
- **Average Salary Hike**: 50–100%
- **Pricing**: Original: ₹99,000
- **Overview**: The Gradus Flagship Software Development Program is a 6-month, career-focused intensive designed to transform you into a job-ready software developer who can crack interviews at top product companies and build production-grade applications. Unlike generic coding bootcamps, this program puts Data Structures & Algorithms at the center — the single most important skill for technical hiring at companies like Google, Amazon, Microsoft, Flipkart, and hundreds of startups. You’ll master every major DSA topic from arrays and strings to graphs and dynamic programming, solve 400+ carefully curated problems with increasing difficulty, learn to analyze time and space complexity, and develop the pattern-recognition skills that interviewers look for.

#### Prerequisites
- No prior programming experience required — we start from absolute zero and build up
- Basic comfort with using a computer, web browser, and installing software
- A working laptop with at least 8 GB RAM and stable internet connection
- High school level math (arithmetic, algebra, basic logic) — we teach the rest
- Curiosity, discipline, and willingness to commit 10–12 hours per week for 6 months (including practice)
- Open to fresh graduates, working professionals, career-switchers, and anyone from any educational background
- No age restriction — if you can learn, we can teach

#### Skills Gained
- Programming & OOP
- Arrays, Strings & Hashing
- Linked Lists, Stacks & Queues
- Trees & Binary Search Trees
- Graphs & Graph Algorithms
- Sorting & Searching
- Recursion & Backtracking
- Dynamic Programming
- Greedy Algorithms
- Bit Manipulation & Math
- System Design Fundamentals
- Full-Stack Web Development
- SQL & Database Design
- Git, Docker & Deployment
- Interview Preparation
- Portfolio & Personal Branding

#### Tools & Frameworks
- C++ / Java / Python
- JavaScript / TypeScript
- React.js
- Node.js / Express
- HTML & CSS / Tailwind
- PostgreSQL / MySQL
- MongoDB
- Redis
- Git & GitHub
- Docker
- AWS (EC2, S3, RDS)
- Nginx
- Postman
- Jest / Mocha
- LeetCode
- Codeforces
- VS Code
- Linux / Terminal
- Figma (Basics)
- JWT / OAuth
- Webpack / Vite
- Vercel
- Render
- Swagger

#### Learning Outcomes
- Write clean, efficient code in C++/Java/Python with strong OOP principles and design patterns
- Master all core data structures — arrays, linked lists, stacks, queues, trees, graphs, heaps, tries, and hash maps
- Implement and analyze sorting algorithms — merge sort, quick sort, heap sort, counting sort, and radix sort
- Solve problems with advanced algorithmic techniques — two pointers, sliding window, binary search, greedy, divide and conquer
- Master recursion and backtracking — permutations, combinations, N-Queens, Sudoku solver, and constraint satisfaction
- Build expertise in dynamic programming — memoization, tabulation, state optimization, and classic DP patterns
- Navigate graph algorithms — BFS, DFS, Dijkstra, Bellman-Ford, topological sort, MST, and shortest path problems
- Understand tree algorithms — BST operations, AVL trees, segment trees, Fenwick trees, and LCA queries
- Analyze time and space complexity for every solution — Big-O, Big-Omega, Big-Theta, amortized analysis
- Build full-stack web applications with React.js frontend and Node.js/Express backend
- Design and query relational databases with SQL, PostgreSQL, indexing, and query optimization
- Understand system design fundamentals — scalability, load balancing, caching, databases, and microservices
- Deploy applications to production with Git, Docker, and cloud platforms (AWS basics)
- Solve 400+ coding problems across LeetCode, Codeforces, and custom problem sets
- Crack technical interviews with structured preparation — DSA rounds, system design, and behavioral

#### Career Outcomes
- Qualify for SDE-1 and SDE-2 roles at top product companies, startups, and service firms
- Solve 400+ coding problems and build strong profiles on LeetCode and Codeforces
- Build a production-grade full-stack portfolio with 4+ industry capstone projects on GitHub
- Crack DSA, system design, and behavioral interview rounds with structured mock preparation and feedback
- Establish a professional presence on LinkedIn with optimized profile and content strategy
- Develop skills valued across every major tech company — FAANG, unicorns, funded startups, and enterprises
- Understand the complete software development lifecycle — from data structures to deployed applications
- Master system design fundamentals that separate junior developers from senior engineers
- Access the Gradus career ecosystem with 300+ hiring partners and dedicated placement support
- Position yourself for a 50–100% salary hike when entering or transitioning into software development roles

#### Capstone Project
Design and build a complete full-stack web application from scratch — from architecture to deployment. Integration includes Front-end using React, Backend API with Node.js and Express, Database design using MongoDB, and deployment on cloud platforms like Render/AWS/Vercel.

#### Target Job Roles & Packages
- **Software Development Engineer (SDE)**: ₹8–25 LPA
- **Full-Stack Developer**: ₹8–22 LPA
- **Backend Engineer**: ₹8–25 LPA
- **Frontend Engineer**: ₹7–20 LPA
- **SDE Intern → SDE-1**: ₹5–12 LPA
- **Application Developer**: ₹7–18 LPA
- **API / Platform Engineer**: ₹10–22 LPA
- **QA / SDET Engineer**: ₹6–18 LPA
- **Solutions Engineer**: ₹8–20 LPA
- **Freelance / Contract Developer**: $20–80/hr

#### Syllabus Modules
- **Module **: Phase 1: Programming & DSA Foundations
  *Type*: chapter | *Estimated Hours*: N/A
- **Module **: Capstone 1: DSA Problem Solving Portfolio & Competitive Profile
  Solve 150+ curated problems. Maintain GitHub repo with clean solutions & time/space complexity annotations. Build active profiles on LeetCode & Codeforces.
  *Type*: project | *Estimated Hours*: N/A
- **Module **: Phase 2: Advanced Data Structures & Algorithms
  *Type*: chapter | *Estimated Hours*: N/A
- **Module **: Capstone 2: Advanced DSA Mastery & Contest Performance
  Achieve 300+ solved problems on LeetCode. Participate in 5+ rated contests. Build comprehensive GitHub repository with 100+ annotated solutions.
  *Type*: project | *Estimated Hours*: N/A
- **Module **: Phase 3: Full-Stack Development & Databases
  *Type*: chapter | *Estimated Hours*: N/A
- **Module **: Capstone 3: Full-Stack Web Application — E-Commerce / Social Platform
  Build React + Node + PostgreSQL + Redis application. Docker containerization & AWS deployment. Full Git workflow.
  *Type*: project | *Estimated Hours*: N/A
- **Module **: Phase 4: System Design, Interview Prep & Career Launch
  *Type*: chapter | *Estimated Hours*: N/A
- **Module **: Capstone 4: GRAND CAPSTONE - Production Full-Stack Application
  Design and deploy production-grade app with system design documentation, HLD/LLD diagrams, and AWS CI/CD pipeline.
  *Type*: project | *Estimated Hours*: N/A
- **Module 1**: Module 1: Programming Fundamentals & OOP
  • Language setup and environment: choosing C++/Java/Python, IDE, debugging\n• Core programming: variables, types, operators, type casting, input/output\n• Control flow: conditionals, loops (for, while, do-while), nested logic\n• Functions: parameters, return types, pass by value vs reference, overloading\n• OOP deep dive: classes, objects, constructors, destructors, this pointer\n• OOP principles: inheritance, polymorphism, abstraction, encapsulation\n• Memory management: stack vs heap, pointers/references, garbage collection\n• Exception handling: try-catch, custom exceptions, defensive programming\n• Code quality: naming conventions & readable documentation\n• Problem solving: 30+ practice problems on arrays, strings, and basic math
  *Type*: lesson | *Estimated Hours*: 12
- **Module 2**: Module 2: Complexity Analysis & Basic Data Structures
  • Time complexity: Big-O notation, best/average/worst case scenarios\n• Space complexity: auxiliary space, in-place algorithms, memory trade-offs\n• Amortized analysis: aggregate and potential methods intuition\n• Linked lists: singly, doubly, circular — insertion, deletion, reversal, cycle detection\n• Stacks: array/linked list implementation, infix/postfix, min stack\n• Queues: circular queue, deque, priority queue basics\n• Hashing: hash functions, collision handling (chain/open), maps, sets\n• STL/Collections: vector, map, set, unordered_map, priority_queue\n• Problem patterns: frequency counting, prefix sums, two-pointer basics\n• Practice: 50+ problems on linked lists, stacks, queues, and hashing
  *Type*: lesson | *Estimated Hours*: 10
- **Module 3**: Module 3: Sorting, Searching & Two Pointers
  • Sorting algorithms: bubble, selection, insertion, merge, quick, heap sort\n• Non-comparison sorts: counting, radix, bucket sort & applications\n• Custom comparators: multi-key sorting & stable vs unstable sorts\n• Binary search: boundary finding, minimizing maximum, binary search on answer\n• Two pointers technique: pair sum, three sum, container with most water\n• Sliding window: fixed/variable window, maximum/minimum subarray problems\n• Merge intervals: overlapping intervals, meeting rooms, insert interval\n• Practice: 60+ problems on sorting, binary search, and sliding window
  *Type*: lesson | *Estimated Hours*: 10
- **Module 4**: Module 4: Recursion, Backtracking & Bit Manipulation
  • Recursion deep dive: recursive tree, call stack, base case, return values\n• Recursion patterns: divide & conquer, subset/permutation generation\n• Backtracking: N-Queens, Sudoku, rat in a maze, word search, pruning\n• Bit manipulation: AND, OR, XOR, NOT, shifts, bit tricks, bitmasking\n• Number theory: GCD (Euclidean), sieve of Eratosthenes, fast exponentiation\n• Practice: 40+ problems on recursion, backtracking, and bit manipulation
  *Type*: lesson | *Estimated Hours*: 10
- **Module 5**: Module 5: Trees & Binary Search Trees
  • Binary tree traversals: inorder, preorder, postorder, level order (BFS)\n• Tree problems: height, diameter, path sum, LCA, serialization\n• BST: insertion, deletion, validation, inorder successor/predecessor\n• Balanced BSTs: AVL rotations, Red-Black tree intuition\n• Heaps: min/max-heap, top-K problems, median finding\n• Advanced: Trie (prefix tree), Segment tree (range queries), Fenwick tree\n• Practice: 50+ problems on trees, heaps, and tries
  *Type*: lesson | *Estimated Hours*: 10
- **Module 6**: Module 6: Graphs & Graph Algorithms
  • Graph representation: adjacency list/matrix, weighted/directed graphs\n• Traversals: BFS (shortest path in unweighted), DFS (connected components)\n• Cycle detection: DFS coloring, Kahn’s algorithm (BFS)\n• Topological sort: course scheduling & dependency resolution\n• Shortest path: Dijkstra (non-negative), Bellman-Ford (negative), Floyd-Warshall\n• MST: Kruskal’s (union-find), Prim’s algorithm\n• Union-Find (DSU): union by rank, path compression, connected components\n• Advanced: bipartite check, bridges, articulation points, Tarjan’s/Kosaraju’s\n• Practice: 60+ problems on graph traversal, shortest path, and MST
  *Type*: lesson | *Estimated Hours*: 14
- **Module 7**: Module 7: Dynamic Programming
  • DP fundamentals: memoization vs tabulation, overlapping subproblems\n• 1D DP: climbing stairs, house robber, max subarray, coin change, LIS\n• 2D DP: unique paths, min path sum, edit distance, LCS, 0/1 knapsack\n• String DP: palindrome partitioning, word break, wildcard matching\n• Interval DP: matrix chain multiplication, burst balloons\n• DP on trees & grids: max path sum, dungeon game, maximal square\n• State optimization: space-optimized DP & Bitmask DP (TSP)\n• Practice: 70+ problems on classic interview DP scenarios
  *Type*: lesson | *Estimated Hours*: 14
- **Module 8**: Module 8: Frontend Development with React
  • HTML & CSS: Flexbox, Grid, Responsive design, Tailwind CSS\n• JavaScript ES6+: closures, promises, async/await, event loop\n• TypeScript: types, interfaces, generics, type-safe development\n• React fundamentals: components, props, state, useState, useEffect\n• Advanced React: custom hooks, Context API, Redux Toolkit/Zustand\n• API integration: fetching data, React Query, loading states\n• Testing: unit testing with Jest & React Testing Library\n• Hands-on: Build task management app with React Router & global state
  *Type*: lesson | *Estimated Hours*: 10
- **Module 9**: Module 9: Backend Development with Node.js & Express
  • Node.js: event loop, modules, fs, streams, npm ecosystem\n• Express.js: routing, middleware, request/response, static files\n• RESTful API: methods, status codes, pagination, versioning\n• Auth: JWT tokens, bcrypt, sessions, OAuth 2.0 basics\n• Validation & Security: Zod/Joi, rate limiting, SQLi/XSS prevention\n• Real-time: WebSockets (Socket.io), notifications, chat functionality\n• Hands-on: Build social media backend with auth, posts, & comments
  *Type*: lesson | *Estimated Hours*: 10
- **Module 10**: Module 10: Databases & SQL Mastery
  • Relational design: normalization (1NF–3NF), ER diagrams, schemas\n• SQL mastery: aggregations, GROUP BY, JOINs, Window functions, CTEs\n• Query optimization: indexing (B-tree/hash), execution plans, EXPLAIN\n• NoSQL with MongoDB: document model, Mongoose ODM, aggregations\n• Scaling: read replicas, sharding concepts, connection pooling, Redis\n• Hands-on: Design e-commerce database with optimized queries
  *Type*: lesson | *Estimated Hours*: 8
- **Module 11**: Module 11: System Design Fundamentals
  • Scalability: horizontal/vertical scaling, Load balancing (L4/L7), CDN\n• Caching: write-through/back, Redis invalidation, CAP theorem\n• DB scaling: replication, sharding, partitioning, consistency models\n• Message queues: Kafka, RabbitMQ, SQS, event-driven architecture\n• Microservices: service communication (gRPC), API gateways\n• Low-level design (LLD): class diagrams, SOLID principles, design patterns\n• Exercises: design TinyURL, rate limiter, chat system, news feed
  *Type*: lesson | *Estimated Hours*: 8
- **Module 12**: Module 12: DevOps & Deployment Essentials
  • Git advanced: branching, rebasing, cherry-pick, PR workflows\n• Docker: images, containers, Docker Compose, multi-container apps\n• CI/CD: GitHub Actions, automated testing, deployment triggers\n• AWS: EC2, S3, RDS, Route 53, Elastic Beanstalk deployment\n• Linux basics: file system, permissions, processes, environment variables
  *Type*: lesson | *Estimated Hours*: 4
- **Module 13**: Module 13: Interview Mastery & Competitive Programming
  • Interview strategy: coding, system design, & behavioral rounds\n• Coding interview patterns: top 15 patterns for FAANG interviews\n• LLD interview: object modeling & pattern implementation\n• Behavioral interview: STAR method & project discussion\n• Company specific preparation: Google, Amazon, Microsoft, Flipkart patterns\n• Hands-on: Full mock interview simulation (Coding + Design + Behavioral)
  *Type*: lesson | *Estimated Hours*: 6

---

### Agentic AI Engineering 
- **URL**: https://www.gradus.live/course/agentic-ai-engineering
- **Subtitle**: Master the Complete AI Engineering Stack — From Python & LLMs to Multi-Agent Systems, RAG, MCP & Production Deployment
- **Target Audience**: AI Enthusiasts, Machine Learning Engineers, Software Developers, and Career-Switchers from any background.
- **Duration**: 6 Months · 144+ Hours · 4 Capstone
- **Level**: Intermediate to Advanced
- **Mode**: Online / Hybrid
- **Language**: English
- **Rating**: 4.9 (176 reviews)
- **Average Salary Hike**: 60–120%
- **Pricing**: Original: ₹1,80,000
- **Overview**: The Gradus Flagship Agentic AI Engineering Program is a 6-month, career-focused intensive designed to transform you into a job-ready AI engineer specializing in the fastest-growing domain in tech — autonomous AI agents. Unlike short bootcamps that cover only prompt engineering, this program goes deep — covering the full spectrum from Python programming, API design, and LLM fundamentals to advanced RAG architectures, vector databases, single-agent and multi-agent systems, tool use, memory management, planning and reasoning, Model Context Protocol (MCP), guardrails, evaluation frameworks, and production deployment on cloud platforms.

#### Prerequisites
- No prior AI or machine learning experience required — we start from Python basics and build up
- Basic comfort with using a computer, web browser, and installing software
- A working laptop with at least 8 GB RAM and stable internet connection
- Logical thinking and problem-solving aptitude — we teach programming and AI concepts from scratch
- Curiosity, discipline, and willingness to commit 8–10 hours per week for 6 months
- Open to fresh graduates, working professionals, career-switchers, and anyone from any educational background
- No age restriction — designed for those ready to learn the 2026 tech stack

#### Skills Gained
- Python & Async Programming
- LLM Fundamentals & Internals
- Prompt Engineering
- RAG Architecture & Vector DBs
- AI Agent Design Patterns
- Multi-Agent Orchestration
- Tool Use & Function Calling
- Agent Memory & State Mgmt
- Model Context Protocol (MCP)
- Guardrails & AI Safety
- LLM Evaluation & Observability
- Fine-Tuning (LoRA/QLoRA)
- Production Deployment
- Open-Source Models & Inference
- Interview Preparation
- Portfolio & Personal Branding

#### Tools & Frameworks
- Python
- FastAPI
- LangChain
- LangGraph
- CrewAI
- OpenAI Agents SDK
- Autogen
- OpenAI API
- Anthropic API
- Google Gemini API
- Ollama
- vLLM
- Hugging Face
- FAISS
- Pinecone
- ChromaDB
- LangSmith
- Streamlit / Gradio
- Docker
- AWS / GCP
- Git & GitHub
- Jupyter & Colab
- Pydantic
- n8n / Zapier

#### Learning Outcomes
- Write production-quality Python code with async programming, API integration, and modular architecture
- Understand LLM internals — transformer architecture, tokenization, attention, context windows, and inference
- Master prompt engineering — zero-shot, few-shot, chain-of-thought, system prompts, and structured outputs
- Build production RAG systems — chunking, embeddings, vector databases, retrieval, re-ranking, and evaluation
- Design and implement autonomous AI agents with tool use, memory, planning, and reasoning loops
- Build multi-agent systems — supervisor, hierarchical, and network architectures with role-based collaboration
- Work with leading agent frameworks — LangChain, LangGraph, CrewAI, OpenAI Agents SDK, and Autogen
- Implement Model Context Protocol (MCP) for standardized tool and data source integration
- Build custom tools and function calling — APIs, databases, web scraping, code execution, and file operations
- Implement agent memory systems — short-term, long-term, episodic, and shared memory architectures
- Design guardrails, safety layers, and human-in-the-loop workflows for reliable AI systems
- Evaluate agent performance — faithfulness, relevance, toxicity, cost tracking, and observability with LangSmith
- Deploy AI applications to production — FastAPI, Docker, cloud platforms (AWS/GCP), and CI/CD pipelines
- Fine-tune LLMs with LoRA/QLoRA and run open-source models with Ollama and vLLM
- Crack AI engineering interviews — system design, LLM internals, coding, and scenario-based rounds

#### Career Outcomes
- Qualify for mid-to-senior level AI Engineer, LLM Engineer, and Agentic AI Developer roles
- Build a production-grade portfolio with 4+ industry capstone projects on GitHub
- Establish a professional presence on LinkedIn with optimized profile and content strategy
- Understand the full AI engineering lifecycle — from LLM API calls to production agent platforms
- Access the Gradus career ecosystem with 300+ hiring partners and dedicated placement support
- Position yourself for a 60–120% salary hike when entering or transitioning into AI engineering roles
- Gain certificates valued across IT, BFSI, healthcare, e-commerce, consulting, and SaaS

#### Capstone Project
Build a fully autonomous Multi-Agent System that can plan, execute, and verify complex tasks. You will deploy this system as a production-ready API service, integrating vector search and external tool calling capabilities.

#### Target Job Roles & Packages
- **AI Engineer**: ₹12–30 LPA
- **Agentic AI Developer**: ₹15–35 LPA
- **LLM Engineer**: ₹12–30 LPA
- **Generative AI Engineer**: ₹12–28 LPA
- **ML Engineer (AI Applications)**: ₹10–25 LPA
- **Prompt Engineer**: ₹8–20 LPA
- **AI Solutions Architect**: ₹18–40 LPA
- **AI Product Engineer**: ₹12–28 LPA
- **MLOps / LLMOps Engineer**: ₹10–25 LPA
- **AI Consultant**: ₹10–22 LPA

#### Syllabus Modules
- **Module **: Phase 1: Foundations
  *Type*: chapter | *Estimated Hours*: N/A
- **Module **: Capstone 1: AI-Powered Document Intelligence Platform
  Build a production-grade RAG platform that ingests business documents (contracts, reports, SOPs), processes them
through an intelligent chunking and embedding pipeline, stores in a vector database, and provides a conversational Q&A;
interface with source citations. Includes advanced retrieval with re-ranking, multimodal support for tables and images,
evaluation metrics dashboard, and a Streamlit frontend. Deployed with FastAPI backend and Docker containerization.
  *Type*: project | *Estimated Hours*: N/A
- **Module **: Phase 2: AI Agent Engineering
  *Type*: chapter | *Estimated Hours*: N/A
- **Module **: Capstone 2: Autonomous Research & Generation Platform
  Multi-agent AI system (Researcher, Analyst, Writer) with LangGraph orchestration, MCP tools, and human-in-the-loop checkpoints.
  *Type*: project | *Estimated Hours*: N/A
- **Module **: Phase 3: Advanced AI Engineering
  *Type*: chapter | *Estimated Hours*: N/A
- **Module **: Capstone 3: Enterprise AI Agent with Safety & Observability
  Multi-turn conversational agent with RAG, MCP tool integration, comprehensive Guardrails, and full LangSmith tracing.
  *Type*: project | *Estimated Hours*: N/A
- **Module **: Phase 4: Ship to Production
  *Type*: chapter | *Estimated Hours*: N/A
- **Module **: Capstone 4: GRAND CAPSTONE - Full-Stack AI Platform
  Multi-agent supervisor platform with advanced RAG, MCP tool integration, full guardrails, observability, and AWS Cloud deployment.
  *Type*: project | *Estimated Hours*: N/A
- **Module 1**: Module 1: Python for AI Engineering
  • Python fundamentals: data types, control flow, functions, OOP, error handling, decorators
• Data structures deep dive: lists, dicts, sets, tuples, collections module, custom classes
• Async programming: asyncio, async/await, aiohttp, concurrent.futures for parallel API calls
• File I/O: CSV, JSON, YAML, PDF, and text parsing — reading, writing, streaming
• HTTP and API fundamentals: requests, REST principles, authentication, rate limiting, pagination
• Building APIs with FastAPI: routes, Pydantic models, dependency injection, middleware, WebSockets
• Working with databases: SQLAlchemy, PostgreSQL basics, CRUD operations, connection pooling
• Python best practices: type hints, virtual environments, linting, testing with pytest
• Package management: pip, poetry, requirements.txt, Dockerizing Python applications
• Mini Project: Build a REST API backend with FastAPI, database integration, and async endpoints
  *Type*: lesson | *Estimated Hours*: 12
- **Module 2**: Module 2: LLM Fundamentals & Prompt Engineering
  • The AI landscape: GPT, Claude, Gemini, Llama, Mistral, Qwen — architecture overview and trade-offs
• Transformer architecture: self-attention, multi-head attention, positional encoding, feed-forward layers
• Tokenization deep dive: BPE, SentencePiece, tiktoken — token counting, context window management
• LLM inference: temperature, top-k, top-p, frequency/presence penalty, stop sequences, seed
• Prompt engineering mastery: zero-shot, few-shot, chain-of-thought, self-consistency, tree-of-thought
• System prompts and persona design: role definition, constraints, output formatting, guardrails
• Structured outputs: JSON mode, function calling schemas, Pydantic model extraction, XML parsing
• OpenAI API deep dive: chat completions, streaming, vision, audio, embeddings, moderation
• Anthropic API deep dive: messages API, system prompts, tool use, streaming, extended thinking
• Google Gemini API: multimodal inputs, grounding, function calling, context caching
• Cost optimization: token budgeting, caching strategies, model selection by task complexity
• Mini Project: Build an AI-powered document analyzer with structured extraction and multi-model routing
  *Type*: lesson | *Estimated Hours*: 10
- **Module 3**: Module 3: Embeddings, Vector DBs & Semantic Search
  • Text embeddings: what they are, how they work, embedding models (OpenAI, Cohere, open-source)
• Similarity metrics: cosine similarity, dot product, Euclidean distance — when to use which
• Vector database fundamentals: indexing (HNSW, IVF), metadata filtering, hybrid search
• FAISS: index types, building and querying, GPU acceleration, persistence
• ChromaDB: collections, embeddings, metadata, querying, integration with LangChain
• Pinecone: serverless indexes, namespaces, metadata filtering, upserts, and hybrid search
• Chunking strategies: fixed-size, recursive, semantic, document-structure-aware, overlap tuning
• Document processing: PDF, DOCX, HTML, Markdown — parsing, cleaning, and metadata extraction
• Evaluation: retrieval quality metrics — precision@k, recall@k, MRR, NDCG
• Mini Project: Build a semantic search engine over a document corpus with hybrid search and filtering

  *Type*: lesson | *Estimated Hours*: 8
- **Module 4**: Module 4: RAG Architecture & Production Patterns
  • RAG fundamentals: retrieval-augmented generation architecture, why RAG beats fine-tuning for knowledge
• Ingestion pipeline: document loading, splitting, embedding, indexing — end-to-end with LangChain
• Advanced retrieval: query rewriting, HyDE, multi-query retrieval, contextual compression
• Re-ranking: cross-encoder re-ranking, Cohere Rerank, reciprocal rank fusion
• Conversational RAG: chat history management, follow-up question handling, context windowing
• Multi-document RAG: handling multiple sources, source attribution, citation generation
• Multimodal RAG: processing images, tables, and charts alongside text — vision-language workflows
• RAG evaluation: faithfulness, answer relevancy, context precision, context recall (RAGAS framework)
• RAG failure modes: bad chunking, noisy retrieval, missing context, hallucination patterns, and fixes
• Context engineering: context window optimization, caching strategies (response cache, embedding cache)
• Hands-on: Build a production RAG system with ingestion pipeline, advanced retrieval, and evaluation
  *Type*: lesson | *Estimated Hours*: 10
- **Module 5**: Module 5: AI Agent Fundamentals & Tool Use
  • Agent patterns: ReAct, Plan-and-Execute, Reflection, Self-Critique\n• Custom tool building: API wrappers, DB queries, code execution\n• Structured outputs: Schema enforcement & Pydantic validation\n• Frameworks: LangChain agents, OpenAI Agents SDK, tool binding\n• Hands-on: Autonomous research agent with web search & reports
  *Type*: lesson | *Estimated Hours*: 10
- **Module 6**: Module 6: Agent Memory, State & Planning
  • Memory architectures: short-term, vector store, episodic\n• Shared memory for multi-agent: blackboard & shared state\n• Planning strategies: hierarchical planning & goal-directed loops\n• Reflection loops: agent strategy adjustment & stopping conditions
  *Type*: lesson | *Estimated Hours*: 8
- **Module 7**: Module 7: Multi-Agent Systems & Orchestration
  • LangGraph deep dive: nodes, edges, conditional routing, cycles\n• CrewAI: crews, task delegation, sequential/hierarchical process\n• Autogen: group chat, code execution agents, nested chats\n• Specialized roles: Planner, Coder, Reviewer, QA Validator\n• Error handling: retry logic & fallback agent protocols
  *Type*: lesson | *Estimated Hours*: 12
- **Module 8**: Module 8: Model Context Protocol (MCP) & Ecosystems
  • MCP Primitives: resources, tools, prompts, sampling\n• Building MCP servers: transport layers (stdio, SSE), Python SDK\n• Building MCP clients: tool discovery & multi-server architectures\n• MCP Security: input validation & sandboxed tool execution
  *Type*: lesson | *Estimated Hours*: 8
- **Module 9**: Module 9: Fine-Tuning & Open-Source LLMs
  • LoRA & QLoRA: parameter-efficient fine-tuning theory\n• Training: Hugging Face Transformers, PEFT, adapter ranks\n• Serving: vLLM, TGI, and quantization (GGUF, GPTQ, AWQ)\n• Local models: Ollama setup & hardware requirements\n• Evaluation: perplexity, BLEU, ROUGE, human evaluation patterns
  *Type*: lesson | *Estimated Hours*: 8
- **Module 10**: Module 10: Guardrails, Safety & AI Governance
  • Input guardrails: prompt injection & jailbreak prevention\n• Output guardrails: hallucination detection & toxicity filtering\n• Frameworks: Guardrails AI & NeMo Guardrails\n• Safety: action confirmation & permission boundaries\n• AI governance: bias detection, transparency, & GDPR basics
  *Type*: lesson | *Estimated Hours*: 8
- **Module 11**: Module 11: Evaluation, Observability & AgentOps
  • Metrics: faithfulness, relevance, coherence, groundedness\n• RAGAS framework: context recall & answer correctness\n• LangSmith: tracing, evaluation datasets, regression testing\n• AgentOps: versioning, feature flags, & canary deployments
  *Type*: lesson | *Estimated Hours*: 8
- **Module 12**: Module 12: Production Deployment & Infrastructure
  • FastAPI for production: middleware, rate limiting, background tasks\n• Docker for AI: GPU support & optimizing multi-stage images\n• Scaling: Horizontal scaling, load balancing, Celery/Redis\n• Cloud: AWS (Lambda, ECS) vs GCP (Cloud Run, Vertex AI)
  *Type*: lesson | *Estimated Hours*: 8
- **Module 13**: Module 13: Advanced Agent Patterns & Trends
  • Browser automation: navigation & data extraction\n• Code generation: sandboxed execution & autonomous deployment\n• Voice & Multimodal: OpenAI Realtime API & Twilio integration\n• Workflow automation: n8n, Zapier, and CRM integration
  *Type*: lesson | *Estimated Hours*: 6
- **Module 14**: Module 14: AI Engineering System Design
  • Sharding & caching for large-scale RAG systems\n• Multi-model strategy: model routing & fallback chains\n• Case studies: how top companies build agentic systems
  *Type*: lesson | *Estimated Hours*: 4

---

### Digital Marketing
- **URL**: https://www.gradus.live/course/digital-marketing
- **Subtitle**: Master the Complete Digital Marketing Stack — From SEO & Paid Ads to AI-Powered Marketing, Analytics & Growth Strategy
- **Target Audience**: Fresh Graduates, Working Professionals, Career-Switchers, Freelancers, and Business Owners from any background.
- **Duration**: 6 Months · 144+ Hours · 4 Capstone
- **Level**: Intermediate
- **Mode**: Hybrid
- **Language**: English/Hinglish
- **Rating**: 4.7 (0 reviews)
- **Average Salary Hike**: 40–80%
- **Pricing**: Original: ₹99,000
- **Overview**: The Gradus Flagship Digital Marketing Program is a 6-month, career-focused intensive designed to transform you into a job-ready digital marketing professional. Unlike short courses that give you a taste, this program goes deep — covering the full spectrum from marketing strategy, website building, and brand positioning to advanced SEO (on-page, off-page, technical), Google Ads (Search, Display, Shopping, YouTube), Meta Ads (Facebook & Instagram), social media marketing, content marketing, email marketing with automation, web analytics with GA4, and cutting-edge AI-powered marketing using ChatGPT, Claude, and generative AI tools for content creation, audience research, and campaign optimization. Every module is built around live campaign execution, real ad spend management, and actual business results.

#### Prerequisites
- No prior marketing or technical experience required — we start from fundamentals and build up
- Basic comfort with using a computer, web browser, and social media platforms
- A working laptop with at least 8 GB RAM and stable internet connection
- Basic English communication skills — we teach the marketing writing and copywriting from scratch
- Curiosity, discipline, and willingness to commit 8–10 hours per week for 6 months
- Open to fresh graduates, working professionals, career-switchers, and anyone from any educational background
- No age restriction — if you can learn, we can teach

#### Skills Gained
- Marketing Strategy & Funnels
- Website Building (WordPress)
- SEO (On-Page, Off-Page, Technical)
- Google Ads (Search, Display, Video)
- Meta Ads (Facebook & Instagram)
- Social Media Marketing
- Content Marketing & Copywriting
- Email Marketing & Automation
- Web Analytics (GA4 & GTM)
- AI-Powered Marketing
- Influencer & Affiliate Marketing
- E-Commerce Marketing
- Marketing Automation & CRM
- Video Marketing & YouTube
- Interview Preparation
- Portfolio & Personal Branding

#### Tools & Frameworks
- Google Ads
- Meta Business Suite
- Google Analytics 4
- Google Tag Manager
- Google Search Console
- SEMrush / Ahrefs
- WordPress
- Canva / Figma
- Mailchimp / HubSpot
- Hootsuite / Buffer
- ChatGPT / Claude
- Google Gemini
- YouTube Studio
- LinkedIn Ads
- Shopify (Basics)
- Notion / Trello
- CapCut / InShot
- Ubersuggest
- Hotjar / Clarity
- Zapier
- Elementor
- RankMath / Yoast
- WP Rocket
- Google Merchant Center

#### Learning Outcomes
- Develop a complete digital marketing strategy — from brand positioning and buyer personas to full-funnel campaign planning
- Build and optimize websites using WordPress — themes, plugins, landing pages, speed optimization, and mobile responsiveness
- Master SEO end-to-end — keyword research, on-page optimization, technical SEO, link building, and local SEO
- Run profitable Google Ads campaigns — Search, Display, Shopping, YouTube, and Performance Max with bid optimization
- Execute Meta Ads (Facebook & Instagram) — audience targeting, creative strategy, retargeting, and conversion tracking
- Build and grow social media presence across Instagram, LinkedIn, YouTube, Twitter/X, and emerging platforms
- Create high-converting content — blogs, video scripts, ad copy, email sequences, and social media calendars
- Design email marketing campaigns with automation — segmentation, drip sequences, A/B testing, and deliverability
- Analyze performance with Google Analytics 4 — event tracking, conversion funnels, attribution models, and custom reports
- Leverage AI tools for marketing — ChatGPT, Claude, and Gemini for content creation, research, SEO, and campaign optimization
- Implement marketing automation with HubSpot/Mailchimp — lead nurturing, CRM integration, and workflow automation
- Understand influencer marketing, affiliate marketing, and e-commerce marketing strategies
- Master Google Tag Manager for tracking implementation across websites and ad platforms
- Build a professional portfolio with 10+ live campaign case studies showing real ROI and business impact
- Crack digital marketing interviews with structured preparation — strategy rounds, case studies, and tool demonstrations

#### Career Outcomes
- Qualify for mid-to-senior level Digital Marketing Manager, SEO Specialist, PPC Manager, and Growth Marketer roles
- Build a professional portfolio with 10+ live campaign case studies showing real ROI and business results
- Earn industry certifications from Google, Meta, and HubSpot to validate your expertise
- Crack digital marketing interviews with structured mock preparation, strategy rounds, and case studies
- Establish a professional presence on LinkedIn with optimized profile and content strategy
- Develop skills valued across agencies, startups, D2C brands, e-commerce, and enterprise companies
- Master cutting-edge AI-powered marketing tools — ChatGPT, Claude, LangSmith — that define 2026 demand
- Access the Gradus career ecosystem with 300+ hiring partners and dedicated placement support
- Position yourself for a 40–80% salary hike when entering or transitioning into digital marketing roles

#### Capstone Project
Design and execute a comprehensive 360° digital marketing campaign for a real business. Includes market research, full SEO audit & fix, live Google & Meta Ads management, 30-day social content plan, email automation, and GA4+GTM ROI reporting via Looker Studio.

#### Target Job Roles & Packages
- **Digital Marketing Manager**: ₹6–18 LPA
- **SEO Specialist / Manager**: ₹5–15 LPA
- **PPC / Performance Marketing Manager**: ₹6–18 LPA
- **Social Media Manager**: ₹4–12 LPA
- **Content Marketing Manager**: ₹5–15 LPA
- **Growth Marketing Manager**: ₹8–20 LPA
- **Email Marketing & Automation Specialist**: ₹4–12 LPA
- **E-Commerce Marketing Manager**: ₹6–16 LPA
- **Digital Marketing Analyst**: ₹5–14 LPA
- **Freelance Digital Marketer / Consultant**: $15–60/hr

#### Syllabus Modules
- **Module **: Phase 1: Marketing Foundations & Website Building
  *Type*: chapter | *Estimated Hours*: N/A
- **Module **: Capstone 1: SEO & Content Marketing Campaign for a Real Business
  Execute complete campaign: keyword research, Technical audit, 10+ page optimization, pillar-cluster plan, 5 SEO blogs, and outreach.
  *Type*: project | *Estimated Hours*: N/A
- **Module **: Phase 2: Paid Advertising & Social Media Marketing
  *Type*: chapter | *Estimated Hours*: N/A
- **Module **: Capstone 2: Multi-Channel Paid Ads & Social Campaign
  Manage live Google Ads & Meta Ads campaigns with real budget. 4-week content calendar & 5-email automation sequence.
  *Type*: project | *Estimated Hours*: N/A
- **Module **: Phase 3: Analytics, AI & Advanced Marketing Channels
  *Type*: chapter | *Estimated Hours*: N/A
- **Module **: Capstone 3: AI-Powered Full-Funnel Marketing Campaign
  Execute campaign with AI content workflow, GA4 conversion setup, Looker Studio dashboard, and CRO heatmap analysis.
  *Type*: project | *Estimated Hours*: N/A
- **Module **: Phase 4: Growth Strategy, Certification & Career Launch
  *Type*: chapter | *Estimated Hours*: N/A
- **Module **: Capstone 4: GRAND CAPSTONE - 360° Digital Marketing Campaign
  Final Portfolio: Complete strategy, audit, live Ads management, Social calendar, Automation, and measurable ROI report.
  *Type*: project | *Estimated Hours*: N/A
- **Module 1**: Module 1: Digital Marketing Fundamentals & Strategy
  • Digital landscape: channels, trends, career paths 2026\n• Traditional vs digital: reach, targeting, measurability, ROI\n• Funnels: AIDA, TOFU/MOFU/BOFU customer journeys\n• Brand strategy: positioning, USP, brand voice & identity\n• Buyer personas: demographic, psychographic & behavioral\n• Competitive analysis: SWOT & benchmarking frameworks\n• Budgeting: channel allocation, CAC, LTV, & ROAS tracking\n• Business models: B2B vs B2C vs D2C channel selection\n• Mini Project: Develop strategy for a real brand
  *Type*: lesson | *Estimated Hours*: 8
- **Module 2**: Module 2: Website Building & WordPress Mastery
  • Web fundamentals: domains, hosting, SSL, mobile-first\n• WordPress: themes (Astra), page builders (Elementor), plugins\n• Landing pages: hero sections, CTAs, trust signals, CRO\n• Essential pages: Home, About, Blog, Contact structure\n• SEO setup: Yoast/RankMath, sitemap, robots.txt, schema\n• Optimization: image compression, WP Rocket, Core Web Vitals\n• Conversion: lead capture forms, chatbots, CRM integration\n• E-commerce: WooCommerce setup & payment gateways\n• Mini Project: Build complete business website & blog
  *Type*: lesson | *Estimated Hours*: 8
- **Module 3**: Module 3: Search Engine Optimization (SEO) Mastery
  • Search internals: Crawling, indexing, Google algorithms\n• Keyword research: long-tail, volume, intent mapping via Ahrefs\n• On-page SEO: title tags, headers, URL structure, image alt\n• Content optimization: NLP-friendly writing & snippet focus\n• Technical SEO: site architecture, XML sitemaps, canonical tags, hreflang\n• Core Web Vitals: LCP, FID/INP, CLS diagnosis & fixes\n• Off-page: backlink strategy, guest posting, digital PR\n• Local SEO: Google Business Profile optimization & local citations\n• GSO: Optimizing for AI search (SGE, Perplexity)\n• Mini Project: Full SEO audit & 90-day optimization roadmap
  *Type*: lesson | *Estimated Hours*: 14
- **Module 4**: Module 4: Content Marketing & Copywriting
  • Content strategy: pillar-cluster model & editorial calendar\n• Copywriting: AIDA, PAS, BAB frameworks & power words\n• Ad copy: hooks and descriptions for Google & Meta Ads\n• Video scripting: YouTube Scripts & Reels/Shorts frameworks\n• AI creation: using ChatGPT/Claude for ideation & drafting\n• Distribution: organic reach, syndication, community seeding\n• Mini Project: 30-day calendar with 10 blog outlines
  *Type*: lesson | *Estimated Hours*: 6
- **Module 5**: Module 5: Google Ads Mastery (SEM & PPC)
  • Structure: account, campaigns, ad groups, keyword matches\n• Creating ads: responsive search ads, extensions, A/B testing\n• Campaigns: Display, YouTube, Shopping, and Performance Max\n• Conversion tracking: GTM setup, enhanced conversions, attribution\n• Optimization: search term reports, bid adjustments, quality score\n• Certification Prep: Google Ads Search & Measurement exam practice\n• Hands-on: Manage live Search + Display + Video campaigns
  *Type*: lesson | *Estimated Hours*: 14
- **Module 6**: Module 6: Meta Ads (Facebook & Instagram Advertising)
  • Ads Manager: pixel installation, CAPI setup for iOS 14+\n• Objectives: leads, sales, awareness, & traffic funnels\n• Audiences: custom, lookalike, & interest-based targeting\n• Creative strategy: image, video, carousel, & scroll-stopping hooks\n• Retargeting strategy: sequential visitors-to-sales funnels\n• Performance: CBO vs ABO, scaling techniques, & ROAS tracking\n• Hands-on: Live Lead Gen & E-commerce ad campaigns
  *Type*: lesson | *Estimated Hours*: 10
- **Module 7**: Module 7: Social Media Marketing & Community Building
  • Platform strategy: IG, LinkedIn, YouTube, Twitter/X, Threads\n• Instagram: Reels strategy, Stories, carousels, algorithm\n• LinkedIn: personal branding & thought leadership content\n• Video: YouTube channel SEO, retention, & Shorts strategy\n• Content tools: Canva graphics & CapCut video editing\n• Community: response strategy & crisis management\n• Scheduling: content batching workflows via Hootsuite/Buffer
  *Type*: lesson | *Estimated Hours*: 8
- **Module 8**: Module 8: Email Marketing & Marketing Automation
  • Fundamentals: list building and CAN-SPAM/GDPR compliance\n• Automation: drip campaigns, nurture sequences, lead scoring\n• Copywriting: subject lines, preview text, CTAs & CTR optimization\n• Segmentation: behavioral & RFM strategy (Mailchimp/HubSpot)\n• Design: mobile-first responsive templates & branding\n• Hands-on: Build complete welcome & nurture automation
  *Type*: lesson | *Estimated Hours*: 6
- **Module 9**: Module 9: Web Analytics with GA4 & GTM
  • GA4: Data streams, custom events, user properties\n• GTM: Tags, triggers, variables, data layer debugging\n• Conversion: Attribution models & funnel visualization\n• Reporting: path explorations & Looker Studio dashboards\n• Advanced: audience remarketing & predictive audiences\n• Hands-on: Set up conversion tracking for live website
  *Type*: lesson | *Estimated Hours*: 10
- **Module 10**: Module 10: AI-Powered Marketing & Automation
  • Workflows: using ChatGPT/Claude for blogs, ad copy, & email\n• AI SEO: keyword research, Breifs, & metadata at scale\n• Prompt Engineering: crafting logic for analysis & strategy\n• Automation: CRM integration & chatbot lead qualification\n• AI Ethics: transparency and responsible AI guidelines\n• Hands-on: Build AI-powered automated content workflow
  *Type*: lesson | *Estimated Hours*: 8
- **Module 11**: Module 11: E-Commerce, Influencer & Affiliate Marketing
  • CRO: heatmaps (Hotjar), A/B tests, checkout optimization\n• Marketplace: Amazon/Flipkart Ads & Brand Store design\n• Influencer: identifying partners, briefs, outreach, & ROI\n• Affiliate: commission structures, tracking, & recruitment\n• Conversational: WhatsApp Business API commerce\n• Hands-on: E-commerce strategy with Influencer campaign
  *Type*: lesson | *Estimated Hours*: 8
- **Module 12**: Module 12: Growth Marketing & Performance Strategy
  • Growth loops: trial mechanics & viral experimentation\n• Retention: cohort analysis & reactivation campaigns\n• Integrated setup: SEO + Paid + Social messaging sync\n• Client side: SOW management, QBRs, and agency pricing\n• Hands-on: Develop 90-day growth roadmap for D2C brand
  *Type*: lesson | *Estimated Hours*: 8
- **Module 13**: Module 13: Industry Certifications & Advanced Topics
  • Guided completion: Google Ads, Meta Blueprint exams\n• HubSpot certifications: Inbound, Content, & Email\n• Trends: voice search, AR/VR marketing, & short video
  *Type*: lesson | *Estimated Hours*: 4
- **Module 14**: Module 14: Video Marketing & YouTube Growth
  • Strategy: pillars, thumbnails, retention, & end screens\n• SEO: YouTube keyword mapping & tag optimization\n• Monetization: Sponsorships, affiliate, & AdSense\n• Hands-on: Create and optimize 3 YouTube videos/Shorts
  *Type*: lesson | *Estimated Hours*: 4

---

### Data Analytics with Gen AI
- **URL**: https://www.gradus.live/course/data-analytics-with-gen-ai
- **Subtitle**: Master the Complete Data Analytics Stack — From Excel & SQL to AI-Powered Insights & Automated Reporting
- **Target Audience**: Fresh graduates, working professionals, career-switchers, and anyone from any educational background (Tech + Non-Tech).
- **Duration**: 6 Months · 144+ Hours · 3 Capstone
- **Level**: Beginner to Advanced
- **Mode**: Online
- **Language**: English
- **Rating**: 4.9 (32 reviews)
- **Average Salary Hike**: 30–60%
- **Pricing**: Original: ₹1,50,000
- **Overview**: The Gradus Flagship Data Analytics with Gen AI Program is a 4-month, career-focused intensive designed to transform you into a job-ready data analytics professional. Unlike short bootcamps, this program goes deep — covering the full spectrum from Excel mastery and SQL to advanced Python analytics, statistical modeling, business intelligence with Power BI and Tableau, and cutting-edge generative AI tools for automated reporting, data storytelling, and AI-assisted analysis.

#### Prerequisites
- No prior programming experience required — we start from absolute zero
- Basic comfort with using a computer, web browser, and installing software
- A working laptop with at least 8 GB RAM and stable internet connection
- High school level math (arithmetic, percentages, basic algebra)
- Willingness to commit 6–8 hours per week for 4 months

#### Skills Gained
- Advanced Excel
- SQL Mastery
- Python for Analytics
- Statistics & Probability
- Data Wrangling
- Data Visualization
- Business Intelligence
- Exploratory Data Analysis
- Generative AI
- Prompt Engineering
- A/B Testing
- ETL Pipelines
- Interview Preparation
- Portfolio Branding

#### Tools & Frameworks
- Excel (Advanced)
- Google Sheets
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Plotly
- Dash
- Power BI
- Tableau
- Looker Studio
- Streamlit
- Scikit-learn
- OpenAI API
- Claude API
- LangChain
- FAISS/ChromaDB
- Google BigQuery
- Apache Spark
- Docker
- Git

#### Learning Outcomes
- Build advanced Excel models with pivot tables, XLOOKUP, and macros
- Write production-quality SQL queries, window functions, and CTEs
- Automate data analysis using Python, Pandas, and NumPy
- Apply statistical methods and A/B testing to solve business problems
- Build interactive BI dashboards using Power BI and Tableau
- Understand and apply Generative AI (ChatGPT, Claude, Gemini) for analytics
- Build AI-powered workflows using prompt engineering and LLM APIs
- Implement RAG-based Q&A systems over internal business documents
- Perform customer segmentation, cohort analysis, and RFM modeling
- Design and implement ETL pipelines for data extraction and loading
- Create compelling data narratives that drive executive decision-making
- Work with cloud platforms like Google BigQuery for large-scale analytics
- Master causal inference and experimental design for product analytics
- Create a professional GitHub portfolio with 3+ industry-grade projects
- Crack interviews with structured prep for SQL and case studies

#### Career Outcomes
- Qualify for Data Analyst, BI Analyst, and AI-Powered Analyst roles
- Build a production-grade portfolio with 3+ industry capstones on GitHub
- Crack technical and HR interviews with recorded mock feedback
- Establish a professional presence on LinkedIn with optimized branding
- Access Gradus career ecosystem with 300+ hiring partners
- Understand the full data analytics lifecycle from raw data to AI insights
- Position yourself for a 30–60% salary hike in data analytics roles
- Gain certificates valued across IT, BFSI, Healthcare, and E-commerce

#### Capstone Project
3 Grand Capstones: E-commerce Business Performance Dashboard, Customer Churn Prediction & Retention System, and an AI-Powered Business Intelligence & Insights Platform.

#### Target Job Roles & Packages
- **Data Analyst**: ₹8–18 LPA
- **Business Intelligence Analyst**: ₹7–16 LPA
- **AI-Powered Analytics Specialist**: ₹10–22 LPA
- **Product Analyst**: ₹8–20 LPA
- **Analytics Engineer**: ₹10–22 LPA
- **Analytics Consultant**: ₹8–18 LPA
- **Marketing Analyst**: ₹6–15 LPA
- **Financial Analyst (Data-Driven)**: ₹8–18 LPA
- **Data Engineer (Entry–Mid)**: ₹8–18 LPA
- **Research Analyst (AI Focus)**: ₹6–14 LPA

#### Syllabus Modules
- **Module **: Phase 1: Foundations
  *Type*: chapter | *Estimated Hours*: N/A
- **Module **: Capstone 1: E-Commerce Business Performance Analytics Dashboard
  Analyze 100K+ transactions from an e-commerce company. Clean messy data using Python and Pandas, perform full EDA,
build advanced SQL queries for segment analysis, create an interactive dashboard in Power BI or Tableau, and present
business recommendations with visualized KPIs — average order value, customer lifetime value, repeat purchase rate, and
revenue by segment.
  *Type*: project | *Estimated Hours*: N/A
- **Module **: Phase 2: Advanced Analytics & BI
  *Type*: chapter | *Estimated Hours*: N/A
- **Module **: Capstone 2: Customer Churn Prediction & Retention Strategy System
  Build a dual analytics system for a telecom client: 
(1) A churn prediction model using XGBoost with SHAP-based
interpretability and business-friendly explanation of risk factors, and 
(2) A customer segmentation engine using K-Means
clustering on RFM features. Deliver results via an interactive Power BI dashboard with risk scores, segment profiles, and
actionable retention recommendations.
  *Type*: project | *Estimated Hours*: N/A
- **Module **: Phase 3: Generative AI for Analytics & Career Launch
  *Type*: chapter | *Estimated Hours*: N/A
- **Module **: Capstone 3: GRAND CAPSTONE - AI-Powered Business Intelligence & Insights Platform
  Design and deploy a production-grade analytics platform for a retail company. The system includes:
(1) EDA and interactive Power BI dashboard for sales, inventory, and customer demographics 
(2) ML classification model predicting customer
churn risk (XGBoost + SHAP)
(3) RAG-based Q&A; chatbot for internal business SOPs and reports using LangChain
(4) Automated weekly insights email with AI-generated executive summary
(5) Full deployment with Streamlit frontend, FastAPI backend, and Docker containerization. Presented as a final portfolio project.
  *Type*: project | *Estimated Hours*: N/A
- **Module 1**: Advanced Excel Mastery
  • Excel fundamentals: navigation, formatting, data entry, cell referencing (relative, absolute, mixed)
• Core functions: SUM, AVERAGE, COUNT, IF, SUMIF, COUNTIF, SUMIFS, COUNTIFS
• Lookup functions mastery: VLOOKUP, HLOOKUP, INDEX-MATCH, XLOOKUP
• Text and date functions: LEFT, RIGHT, MID, CONCATENATE, TEXT, DATEVALUE, NETWORKDAYS
• Data validation, conditional formatting, named ranges, and data protection
• Pivot tables deep dive: grouping, calculated fields, slicers, timelines, pivot charts
• Advanced charting: combo charts, sparklines, dynamic charts, dashboard layouts
• Dynamic arrays: FILTER, SORT, UNIQUE, SEQUENCE, SORTBY, XLOOKUP with arrays
• Introduction to VBA macros: recording, editing, basic automation scripts
• Google Sheets: collaboration, IMPORTDATA, IMPORTHTML, QUERY function, Apps Script basics
• Mini Project: Build a dynamic sales performance dashboard with automated KPI tracking
  *Type*: lesson | *Estimated Hours*: 8
- **Module 2**: SQL & Database Fundamentals
  • Relational database concepts: tables, schemas, normalization (1NF, 2NF, 3NF), ER diagrams
• SQL fundamentals: SELECT, WHERE, ORDER BY, LIMIT, DISTINCT, aliases
• Aggregations: GROUP BY, HAVING, COUNT, SUM, AVG, MIN, MAX
• JOINs mastery: INNER, LEFT, RIGHT, FULL OUTER, SELF, CROSS joins
• Subqueries: scalar, correlated, EXISTS, IN — and when to use each
• Common Table Expressions (CTEs) and recursive queries
• Window functions: ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD, NTILE, running totals
• Query optimization: indexing, execution plans, query profiling
• Mini Project: Solve 30 business SQL problems on a simulated retail company database
  *Type*: lesson | *Estimated Hours*: 10
- **Module 3**: Python for Analytics
  • Python fundamentals: variables, data types, operators, type casting
• Control flow: conditionals, loops, nested logic, iteration patterns
• Data structures deep dive: lists, tuples, sets, dictionaries, nested structures
• Functions: parameters, return values, scope, lambda functions, map, filter, reduce
• File handling: CSV, JSON, text — reading, writing, and parsing
• NumPy foundations: arrays, broadcasting, vectorized operations, reshaping
• Pandas deep dive: Series, DataFrames, data I/O (CSV, Excel, JSON, SQL, Parquet)
• Data cleaning: missing values (detection, imputation strategies — mean, median, forward/backward fill)
• Data transformation: encoding, binning, normalization, log transforms
• GroupBy operations, aggregations, pivot tables, merging, joining DataFrames
• Regular expressions for data cleaning and text extraction
• Mini Project: Clean and analyze a real e-commerce transactions dataset (50K+ rows)
  *Type*: lesson | *Estimated Hours*: 10
- **Module 4**: Statistics & Probability
  • Descriptive statistics: central tendency, dispersion, skewness, kurtosis
• Probability theory: Bayes’ theorem, conditional probability, independence
• Probability distributions: Normal, Binomial, Poisson, Uniform, Exponential
• Inferential statistics: sampling, Central Limit Theorem, confidence intervals, margin of error
• Hypothesis testing: z-test, t-test, chi-square, ANOVA, p-values, effect size
• Correlation and covariance analysis — Pearson, Spearman, point-biserial
• Regression foundations: simple linear regression, interpretation, residual analysis
• A/B testing fundamentals: experimental design, statistical significance, power analysis
• Hands-on: Statistical analysis on real-world survey and business datasets
  *Type*: lesson | *Estimated Hours*: 8
- **Module 5**: Data Visualization & Storytelling
  • Visualization theory: principles of visual encoding, Gestalt principles, chart selection frameworks
• Matplotlib mastery: subplots, annotations, styling, custom themes, publication-quality plots
• Seaborn for statistical visualization: distribution plots, regression plots, categorical plots, heatmaps
• Plotly for interactive visualizations: scatter, bar, line, sunburst, treemap, choropleth maps
• Dashboard building with Plotly Dash: layouts, callbacks, multi-page apps
• Storytelling with data: structuring a narrative, audience awareness, executive presentations
• Data journalism techniques: annotated charts, contextual visuals, and persuasive data narratives
• Mini Project: Build an interactive business dashboard from a retail dataset

  *Type*: lesson | *Estimated Hours*: 10
- **Module 6**: Business Intelligence with Power BI & Tableau
  • Power BI: connecting data sources, Power Query for ETL, data modeling (star schema), DAX fundamentals
• Building Power BI dashboards: visuals, slicers, drill-through, bookmarks, tooltips, row-level security
• Tableau fundamentals: connecting data, calculated fields, parameters, LOD expressions
• Advanced Tableau: dashboard actions, storytelling, publishing to Tableau Public / Server
• Google Looker Studio: data connectors, blended data, custom report building
• BI best practices: KPI design, dashboard layout, refresh scheduling, stakeholder alignment
• ETL pipeline concepts: data extraction, staging, transformation, loading, scheduling with cron/Airflow basics
• Mini Project: Build a multi-page executive BI dashboard for a SaaS company in Power BI

  *Type*: lesson | *Estimated Hours*: 10
- **Module 7**:  Exploratory Data Analysis & Feature Engineering
  • The EDA framework: structured approach to data investigation
• Univariate analysis: distributions, outlier detection (IQR, Z-score, box plots)
• Bivariate and multivariate analysis: scatter matrices, correlation analysis, cross-tabulations
• Customer analytics: cohort analysis, retention curves, funnel analysis, RFM segmentation
• Feature engineering: creating new features, binning, encoding categorical variables
• Feature scaling: StandardScaler, MinMaxScaler, RobustScaler — when to use which
• Handling imbalanced datasets: oversampling, undersampling, class weights
• Dimensionality reduction: PCA (theory + implementation), t-SNE for visualization
• Hands-on: Complete EDA and feature engineering pipeline on a real banking dataset
  *Type*: lesson | *Estimated Hours*: 8
- **Module 8**: Machine Learning for Analysts
  • ML concepts for analysts: supervised vs unsupervised, training/testing, overfitting, bias-variance
• Linear and logistic regression: building, interpreting, and communicating model results
• Decision trees and random forests: interpretability, feature importance, business applications
• Gradient boosting overview: XGBoost, LightGBM for prediction tasks
• Clustering: K-Means, hierarchical clustering, DBSCAN for customer segmentation
• Model evaluation: accuracy, precision, recall, F1, AUC-ROC, confusion matrix
• Model interpretation: SHAP values, feature importance plots, communicating ML to stakeholders
• Time series basics: trend, seasonality, moving averages, ARIMA/Prophet overview
• Recommender systems overview: collaborative filtering, content-based approaches
Gradus.live — Flagship Program Data Analytics with Gen AI · 4 Months · Job Assurance Page 7
• Hands-on: Build a customer churn prediction model and present findings to simulated stakeholders

  *Type*: lesson | *Estimated Hours*: 8
- **Module 9**: Generative AI Foundations for Analysts
  • The generative AI landscape: GPT, Claude, Gemini, Llama — capabilities and use cases for analytics
• How LLMs work: tokenization, attention, context windows, temperature — intuition for analysts
• Prompt engineering mastery: zero-shot, few-shot, chain-of-thought, system prompts, prompt templates
• AI-assisted data analysis: using ChatGPT/Claude for EDA, SQL generation, code debugging, data cleaning
• AI-powered report writing: automated insight generation, executive summary drafting, narrative creation
• Building structured outputs: getting LLMs to produce tables, JSON, formatted analyses consistently
• Limitations and hallucinations: when to trust AI outputs, fact-checking strategies, human-in-the-loop
• AI ethics for analysts: bias in AI-generated insights, responsible use, data privacy considerations
• Hands-on: Automate a complete weekly analytics report using prompt engineering workflows

  *Type*: lesson | *Estimated Hours*: 8
- **Module 10**: LLM APIs & AI-Powered Analytics Applications
  • OpenAI API and Anthropic API: authentication, endpoints, parameters, structured outputs
• Building AI-powered analytics tools: automated data profiling, anomaly detection narratives
• Embeddings: text-to-vector, similarity search, vector databases (FAISS, ChromaDB)
• Retrieval-Augmented Generation (RAG): architecture, chunking, retrieval, re-ranking
• Building RAG applications with LangChain: document loaders, text splitters, chains, memory
• AI-powered Q&A; over business documents: SOPs, financial reports, internal knowledge bases
• Automating analytics workflows: Python + LLM pipelines for scheduled insight generation
• Building Streamlit apps with AI backends: interactive AI-powered analytics tools
• Hands-on: Build a RAG-powered Q&A; system over custom company documents using LangChain

  *Type*: lesson | *Estimated Hours*: 8
- **Module 11**: Advanced Analytics & Production Workflows
  • A/B testing deep dive: experimental design, statistical significance, Bayesian A/B, multi-armed bandits
• Product analytics: funnel analysis, user journey mapping, event tracking, metric trees
• Causal inference basics: correlation vs causation, difference-in-differences, propensity score matching
• Automation with Python: scheduled reports, email alerts, Slack integrations, cron jobs
• Introduction to cloud analytics: Google BigQuery, AWS S3 + Athena for querying large datasets
• Data pipeline basics: ETL workflows with Python, scheduling with Airflow overview
• Introduction to Docker: containerizing analytics applications for reproducibility
• Model serving basics with FastAPI: building REST APIs for analytics tools
• Hands-on: Build an automated analytics pipeline with scheduled reporting and alerting
  *Type*: lesson | *Estimated Hours*: 8
- **Module 12**: Big Data Fundamentals & Advanced Topics
  • Big data ecosystem overview: Hadoop, HDFS, MapReduce concepts
• Apache Spark introduction: RDDs, DataFrames, Spark SQL, PySpark for large-scale analytics
• Data warehousing concepts: star schema, snowflake schema, OLAP vs OLTP
• Cloud data platforms: AWS Redshift, Google BigQuery, Snowflake overview
• Real-time analytics: streaming data concepts, Kafka overview
• Hands-on: Process and analyze a 5M+ row dataset using PySpark on Colab/Databricks
  *Type*: lesson | *Estimated Hours*: 4

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