This unit covers data manipulation and numerical computing using Python libraries. Students will learn how to create and manipulate arrays using NumPy and work with structured datasets using Pandas DataFrames. Key topics include data loading, indexing, slicing, filtering, aggregation, and handling missing values.

This unit focuses on preparing raw data for analysis. Learners will understand techniques for handling missing data, removing duplicates, data transformation, normalization, and encoding categorical variables. Emphasis is placed on improving data quality to ensure accurate and meaningful analysis.

In this unit, students will explore datasets to identify patterns, trends, and relationships. Topics include descriptive statistics, correlation analysis, and feature understanding. Learners will use Pandas and visualization tools to summarize and interpret data effectively.

This unit introduces visualization techniques using Matplotlib and Seaborn. Students will learn to create various plots such as bar charts, histograms, scatter plots, and heatmaps. The focus is on communicating insights clearly through visual storytelling and dashboards.

This unit provides a basic introduction to machine learning concepts, including supervised learning and simple models like linear regression. Students will implement models using Scikit-learn and evaluate performance. The unit culminates in a mini project where learners apply the complete data science workflow to a real-world dataset.

Introduction to Data Science, AI, ML, Analytics Lifecycle Python Installation, Jupyter Notebook, Google Colab Python Basics: Variables, Data Types, Operators Conditional Statements and Loops Functions, Modules, and Packages

Lists, Tuples, Sets Dictionaries and Nested Structures String Manipulation and Exception Handling File Handling in Python Introduction to NumPy

Introduction to Pandas Series and DataFrames Data Importing and Exporting Data Cleaning Techniques Data Transformation and Aggregation and EDA

Introduction to Matplotlib Advanced Visualization Techniques Data Visualization with Seaborn Dashboard Concepts and Storytelling Mini Visualization Project

Descriptive Statistics Probability Fundamentals Probability Distributions Hypothesis Testing Correlation and Regression Basics

Introduction to Machine Learning Data Preprocessing Linear Regression Logistic Regression and Model Evaluation Metrics

Decision Trees and Random Forest K-Nearest Neighbors (KNN) Support Vector Machines (SVM) Clustering Techniques Dimensionality Reduction

Business Problem Identification End-to-End Data Science Workflow Data Collection and Preparation Model Building and Optimization Project Progress Review

Business Problem Identification Business Intelligence Concepts Introduction to Model Deployment Ethics in AI and Data Science Cloud Platforms for Data Science

Capstone Project Development Model Testing and Validation Report Writing and Documentation Final Project Presentation Course Review and Final Assessment