Data Science with Generative AI
Master Data Science & Unlock the Power of Generative AI for Real-World Solutions
Course Overview
Unlock your future in tech with our cutting-edge Data Science with Generative AI program. This comprehensive course is designed to equip students, freshers, and working professionals with the full-stack skillset needed to excel in today’s data-driven world. From Python programming and statistical foundations to advanced Machine Learning, Deep Learning, and state-of-the-art Generative AI (ChatGPT, LLMs, LangChain), this course offers everything you need to become a job-ready Data Scientist.
What you’ll learn
- Core Programming (Python), SQL & Statistics
- Data Wrangling, Visualization & Business Analytics
- Machine Learning & Natural Language Processing
- Deep Learning with TensorFlow and PyTorch
- Generative AI Applications: Chatbots, Content Creators, AI Assistants
Key Highlights
- 5 Industry-Aligned Phases with Progressive Complexity
- Hands-on Projects, Capstone, and Real-Time Applications
- Access to Generative AI Tools (OpenAI, HuggingFace, LangChain)
- 1:1 Mentorship, Weekend Assignments & Doubt Solving
Course Curriculum
PHASE 1: Core Python, SQL & Statistics (Months 1–2) ; Objective: Build strong foundations in programming, data manipulation, and statistical reasoning.
Month 1: Python Programming
- Python Basics: Syntax, Data Types, Variables
- Control Structures: Conditions, Loops
- Functions and Lambda Expressions
- Data Structures: Lists, Tuples, Sets, Dictionaries
- File Handling, Error Handling
- Object-Oriented Programming
Assignments: Real-world Python exercises
Mini Project: Bank Management System / Student Report Generator
Month 2: Statistics and SQL
- Descriptive Statistics: Mean, Median, Mode, Standard Deviation
- Probability Theory, Distributions, Hypothesis Testing
- SQL Basics: SELECT, WHERE, JOINS, GROUP BY
- Advanced SQL: Window Functions, CTEs, Subqueries
Assignments: SQL queries with business use cases
Mini Project: Sales Data Analysis using SQL + Python
PHASE 2: Exploratory Data Analysis & Visualization (Months 3–4) ; Objective: Master data cleaning, exploration, and visualization to derive actionable insights.
Month 3: Data Manipulation with Pandas and NumPy
- NumPy Arrays and Operations
- Pandas: DataFrames, Series, Indexing, Filtering
- Handling Missing Data, Grouping, Merging
- Feature Engineering Techniques
Mini Project: IPL / Movie Dataset Analysis (EDA)
Month 4: Data Visualization & Business Analytics
- Data Visualization: Matplotlib, Seaborn
- Interactive Dashboards: Plotly, Dash
- Business Analytics Metrics: CAC, Churn, Retention, CLTV
- KPI Reporting and Automation
Project: Retail Sales Dashboard with Plotly & Python
PHASE 3: Machine Learning & Natural Language Processing (Months 5–6) ; Objective: Implement classical machine learning algorithms and NLP techniques for intelligent automation.
Month 5: Supervised and Unsupervised Learning
- Regression Models: Linear, Logistic
- Classification: Decision Tree, SVM, Random Forest
- Clustering: KMeans, Hierarchical, DBSCAN
- Dimensionality Reduction: PCA, t-SNE
- Model Evaluation Metrics
Assignments: Titanic, Iris, Loan Prediction Problems
Project: Customer Segmentation using KMeans
Month 6: Natural Language Processing (NLP)
- Text Preprocessing, Tokenization, Stop Words
- Bag of Words, TF-IDF, N-grams
- Word Embeddings: Word2Vec, GloVe
- Text Classification, Named Entity Recognition
Project: Twitter Sentiment Analyzer
Mini Tool: NLP-powered Resume Scanner
PHASE 4: Deep Learning & Generative AI (Months 7–8) ; Objective: Understand neural networks and build generative AI applications using large language models.
Month 7: Deep Learning Foundations
- Neural Networks, Activation Functions
- Backpropagation, Gradient Descent
- CNNs for Image Recognition
- RNNs and LSTMs for Sequence Modeling
Project: Digit Classifier / Facial Expression Recognition
Month 8: Generative AI & LLMs
- Transformer Architecture and Attention Mechanism
- Prompt Engineering for GPT Models
- Working with Hugging Face and OpenAI APIs
- LangChain, Streamlit Apps
- Applications: Chatbot, Text Generator, Image-to-Text, Code Generator
Capstone Project: AI Virtual Assistant for Education/Business
PHASE 5: Career Readiness & Placement Preparation (Months 9–10) ; Objective: Prepare students for job placement through resume building, mock interviews, and technical preparation.
Month 9: Soft Skills and Technical Interview Prep
- Resume Building and ATS Optimization
- LinkedIn Profile Enhancement
- GitHub Project Portfolio Setup
- Aptitude: Logical, Quantitative, Verbal
- Python + SQL Interview Questions
Month 10: Mock Interviews & Job Preparation
- Mock Technical and HR Interviews
- Group Discussions and Role Plays
- Case Study Presentations
- Career Counseling and Referrals
Final Deliverables:
- Resume and LinkedIn Profile
- GitHub with 5+ Projects
- Course completion Certificate
- 4 Mock Interviews with Detailed Feedback
> Hands-on Experience: 6+ Full Stack Projects
> Coding Expertise: 500+ Leetcode Problems
> FAANG-Level Readiness: System Design & Advanced DSA
> Guaranteed Placement Assistance
Program Outcomes –
Students will become FAANG-ready full-stack developers – Hands-on experience with real-world projects & 500+ Leetcode problems – Master Java, DSA, React, Spring Boot, and System Design – Strong resume, LinkedIn profile, and mock interview experience.
Rajesh Kumar
Senior Java Developer & Educator
8 Courses
4500 Students
Rajesh has over 12 years of experience in Java development, working with companies like Infosys, IBM, and Microsoft. He has led multiple enterprise Java projects and has been teaching programming for the past 6 years. His teaching methodology focuses on practical implementation and industry best practices.
₹14,999/-
₹35,000/-
632 Enrolled Students
45 Weeks total duration
220 Lessons
Certificate of Completion