1-Year Placement-Oriented Program: Data Analytics, Science & GenAI

Python is the most popular language for data analytics and automation. This module will help you build a strong foundation in Python programming and core concepts used in data workflows.

Python Programming & Core Concepts:

Python Fundamentals:

Object-Oriented Programming (OOP):

File Handling:

Error & Exception Handling:

By completing this module, you'll gain the core Python skills needed for data processing and automation workflows.

SQL is essential for extracting, analyzing, and managing data stored in relational databases. This module will teach you to write queries, join tables, and create reports to support data-driven decisions.

SQL for Data Analysts:

SQL Fundamentals:

Aggregations & Grouping:

Joins & Subqueries:

Advanced SQL:

By completing this module, you'll confidently use SQL to query data, generate reports, and support analytics projects.

Excel remains the most versatile tool for quick data analysis and reporting. This module will teach you how to clean data, build formulas, and create interactive dashboards that turn raw information into actionable insights.

Excel Essentials:

Functions & Lookups:

Data Analysis & Visualization:

Building Dashboards:

By completing this module, you'll be ready to create polished reports and dashboards that showcase your data analysis skills.

This module will teach you the most important libraries and techniques for analyzing, cleaning, and visualizing data in Python. You'll gain hands-on experience working with real datasets to discover insights and present your findings.

Data Analysis with NumPy:

Data Manipulation with Pandas:

Data Visualization with Matplotlib:

Advanced Visualization with Seaborn:

Interactive Dashboards with Plotly:

By completing this module, you'll be able to confidently prepare, analyze, and visualize data using Python's most powerful libraries.

Exploratory Data Analysis (EDA) is a critical step that helps you understand your dataset, identify patterns, and prepare features before modeling. In this module, you’ll learn how to explore data visually and statistically.

EDA Fundamentals:

Outlier Detection & Feature Engineering:

Visual EDA Techniques:

By completing this module, you'll have the skills to uncover hidden insights and prepare datasets for modeling confidently.

Power BI is a leading business intelligence tool used to create interactive dashboards and reports. This module will guide you step by step in transforming data into compelling visual stories for decision-making.

Power BI Essentials:

Data Visualization:

Data Analysis Expressions (DAX):

Publishing & Sharing:

Practice & Assessment:

By the end of this module, you'll be able to create and share professional-grade dashboards that drive business decisions.

Learn core statistical and probability concepts to analyze data and make data-driven decisions.

Core Topics:

Tools:

Mini Projects:

Learn the fundamentals of supervised machine learning, covering regression, classification, and practical model evaluation techniques.

Core ML Concepts:

Model Evaluation:

Tools Used:

Mini Projects:

By completing this module, you'll be able to build and evaluate supervised ML models for real-world datasets.

Master unsupervised learning techniques to group similar data points, reduce dimensions, and prepare features for better model performance.

Core Unsupervised Learning Concepts:

Tools Used:

Mini Projects:

By completing this module, you'll gain the skills to analyze, group, and simplify data without predefined labels.

Learn the foundations of deep learning, neural networks, and essential concepts for building AI models from scratch.

Core Deep Learning Concepts:

By completing this module, you'll understand how deep learning works and be able to implement basic neural networks.

Advanced Deep Learning Concepts:

Tools Used:

Mini Projects:

By completing this module, you'll be equipped to work on advanced AI applications and deploy deep learning models effectively.

Learn how to handle and analyze massive datasets using PySpark. Get hands-on with distributed computing, RDDs, DataFrames, and Spark SQL.

Core Big Data & PySpark Concepts:

By the end, you'll know how to ingest, process, and analyze large datasets efficiently with PySpark.

Tools Used:

Mini Project:

Load, clean, and analyze NYC Taxi data. Identify peak hours, popular locations, and fare trends using DataFrames, SQL, and MLlib.

Learn to process and analyze text data for building intelligent NLP applications. Master text preprocessing, feature extraction, and classification techniques.

Core NLP Concepts:

By the end, you'll be able to transform raw text into structured features and build NLP models for classification and analysis.

Tools Used:

Mini Projects:

Apply NLP techniques to real-world datasets, build sentiment prediction models, and automate text classification tasks.

Understand the core concepts of Generative AI and Large Language Models (LLMs). Learn prompt engineering, embeddings, and how to integrate AI APIs into applications.

Core GenAI Concepts:

By the end, you'll be able to design prompts, store embeddings, and integrate LLMs into your applications.

Tools Used:

Mini Projects:

Build an interactive chatbot powered by OpenAI and an automated text summarizer using LLM APIs.

Learn to build real-world Generative AI applications from scratch. Explore LangChain, LlamaIndex, RAG pipelines, and deploy apps with modern tools.

Core GenAI App Concepts:

By the end, you'll know how to connect LLMs with custom data and deploy interactive AI applications.

Tools Used:

Mini Projects:

Build a document Q&A bot using LangChain and a voice transcription + summarization app using Whisper and GPT.

A hands-on lab to explore, integrate, and deploy AI tools using Python. Build mini AI solutions and experiment with advanced APIs.

AI Tools with Python:

Practice working with popular AI libraries, frameworks, and APIs to create custom AI solutions.

Mini Project Lab:

Cloud Tools:

Capstone Activity:

Combine multiple AI APIs into a single working application and present your solution.

Final month focused on polishing your portfolio, acing interviews, and preparing for real-world job opportunities. Includes resume building, LinkedIn optimization, GitHub showcase, and capstone projects.

Key Topics:

Capstone Projects (Pick 1-2):

By the end of this month, you’ll have a strong portfolio, a polished resume, and real project experience to showcase in job applications and interviews.