Full Stack Data Science & GenAI Program
🎯 Who is this course for?
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Graduates, freshers, or working professionals looking to break into Data Science & AI
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Business analysts, software engineers, and domain experts upskilling into AI and automation
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Entrepreneurs & creators who want to use GenAI tools and data analytics for business growth
📘 Course Modules in Detail
🔹 Module 1: SQL for Data Analytics
Objective: Learn how to extract and manipulate structured data using SQL.
Topics:
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SQL Basics (SELECT, WHERE, JOIN, GROUP BY)
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Subqueries, CTEs, and Window Functions
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Data Cleaning & Transformation
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Case Studies: Sales Reports, Customer Insights
Outcome: Query and analyze data from any relational database like MySQL or PostgreSQL.
🔹 Module 2: Python for Data Science
Objective: Build a strong Python foundation for data analysis and ML.
Topics:
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Python Basics: Loops, Functions, Lists, Dictionaries
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Libraries: NumPy, Pandas for Data Manipulation
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Data Cleaning & EDA (Exploratory Data Analysis)
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Data Visualization with Matplotlib, Seaborn
Outcome: Use Python confidently for data preparation and exploration.
🔹 Module 3: Tableau & Power BI (BI Tools)
Objective: Build professional dashboards and data stories.
Topics:
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Tableau: Data Connections, Visuals, Filters, Dashboards
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Power BI: Power Query, DAX, Relationships, Drill-downs
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Real-World Use Cases: Sales, Marketing, Operations Dashboards
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Publishing, Sharing, RLS (Row Level Security)
Outcome: Create interactive dashboards that provide clear insights to stakeholders.
🔹 Module 4: Machine Learning (ML)
Objective: Learn how machines learn from data using supervised and unsupervised algorithms.
Topics:
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ML Pipeline: Data Prep, Model Building, Evaluation
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Algorithms: Linear Regression, Logistic Regression, Decision Trees, Random Forest, SVM
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Unsupervised Learning: Clustering (KMeans), Dimensionality Reduction
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Model Evaluation Metrics (RMSE, Accuracy, ROC-AUC)
Outcome: Build end-to-end ML models and interpret results with confidence.
🔹 Module 5: Deep Learning (DL)
Objective: Understand and build neural networks for complex data.
Topics:
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Introduction to Neural Networks & Backpropagation
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Deep Learning with TensorFlow/Keras
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CNNs for Image Classification
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RNNs & LSTMs for Time Series & Text
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Transfer Learning
Outcome: Build and deploy AI models for image, text, or time-series applications.
🔹 Module 6: Prompt Engineering
Objective: Learn how to design high-quality prompts to interact effectively with LLMs like ChatGPT.
Topics:
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Prompt Design Patterns: Role Prompting, Chain of Thought
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Use Cases: Writing SQL, Python, Reports, Resumes, Emails, Ideas
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Prompt Debugging, Prompt Templates
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Building Prompt Libraries for Reuse
Outcome: Solve real-world tasks using language models with effective prompt design.
🔹 Module 7: Generative AI (GenAI) Tools & Applications
Objective: Apply GenAI tools to automate business and creative tasks.
Topics:
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Understanding LLMs (GPT, Claude, Gemini, LLaMA)
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Tools: OpenAI Playground, LangChain, Hugging Face, Gemini
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RAG (Retrieval-Augmented Generation)
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Use Cases: Document Q&A, AI Resume Writing, Chatbots, Code Generation
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Building ChatGPT Plugins / AI Agents
Outcome: Use GenAI tools to build smart applications and automate workflows.
👨🏫 What’s Included:
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Instructor-led Live Sessions + Recorded Videos
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Weekly Doubt Solving + 1:1 Mentorship
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Real-World Case Studies + Projects
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Resume Building + Mock Interviews
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🚀 Final Outcomes
By the end of this course, you will be able to:
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Query and clean data from any source
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Create advanced dashboards for business decisions
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Build ML/DL models and deploy them
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Use ChatGPT and other LLMs for real-world business solutions
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Apply for roles like:
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Data Analyst
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BI Developer
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Data Scientist
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GenAI Application Developer
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Prompt Engineer
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