Data Science

Course Description

This comprehensive course on Data Science covers the foundational aspects of Data Science, Machine Learning, and Python programming. It begins with an introduction to AI, Data Science, and essential Machine Learning concepts, followed by a deep dive into Python’s features, data structures, and object-oriented programming. The course also explores data analysis using Python libraries like Numpy, Pandas, and Matplotlib, and includes SQL for database management. Real-time tasks and projects, such as wine quality analysis and stock market prediction, provide practical experience, making this course ideal for aspiring data scientists.

What Will You Learn?

• Understand the fundamentals of Data Science and AI
• Gain proficiency in Python programming, including its key features and data structures
• Learn essential Machine Learning concepts and how to implement them in Python
• Master data analysis techniques using Numpy, Pandas, and Matplotlib
• Develop skills in SQL for effective database management
• Apply Machine Learning algorithms to real-world datasets
• Work on hands-on projects to reinforce learning and build a strong portfolio

Course Curriculum

Introduction to AI & Data Science

• Justification of AI & Data Science
• Introduction to Data Science
• Machine Learning Concepts
• Introduction to Python
• Python Important Features
• Interfaces or Software to Execute Python

Python Basics

• Types of Operators in Python
• Control Statements in Python
• Functions in Python
• Packages & Modules in Python
• Exception Handling in Python

Python Data Structures

• Python Data Structures
• List Data Structures
• String Data Structure
• Sets and Tuples
• Dictionaries
• Files Concept in Python
• Regular Expressions

Object-Oriented Programming (OOP) in Python

• Object-Oriented Programming in Python

Data Analysis with Python

• Numpy Module
• Pandas Module
• Numpy Arrays
• Pandas Series Operations
• Pandas Aggregate Functions
• Pandas Aggregate Operations (Continued)
• Matplotlib Module
• Statistics
• Exploratory Data Analysis (EDA)
• EDA Continuation

SQL and Database Management

• Introduction to DBMS
• SQL Introduction
• SQL DB Creation
• SQL Concepts – SELECT, JOINS, WHERE CLAUSE
• SQL – CASE Statements
• SQL Ranking

Machine Learning with Python

• Simple Linear Regression
• Multiple Linear Regression
• Logistic Regression with Python
• Confusion Matrix, ROC Curve
• Naive Bayes ML Algorithm
• Hypothesis Testing for t-test
• Two Sample t-test
• Polynomial and Exponential Regression

Real-Time Tasks

• Exploring Wine Quality: A Data-driven Analysis and Classification Approach
• A Data-Driven Approach for Predicting Boston House Prices
• Stock Market Prediction and Forecasting: An Ensemble Learning Approach
• Analyzing and Predicting Movie Ratings: A Comprehensive Study on the IMDb Movie Dataset
• Identification and Analysis of Chronic Disease Indicators: A Data Science Perspective
• Analysis of Titanic Passengers Survived using Logistic Regression
• Analysis of Different Car CO2 Emission Models using Multiple Linear Regression
• Analysis of Sales Prices for TV Marketing using Simple Linear Regression
• Analysis of Bank Dataset Using Exploratory Data Analysis