Data Science – From Scratch to Advanced with Hands-On Projects

Description

About the Course

This beginner-friendly course is designed to help students build a strong foundation in Data Science. You’ll learn how to collect, clean, analyze, and visualize data using Python, statistics, and modern tools. The course blends theory with hands-on projects, enabling you to transform raw data into meaningful insights for real-world applications.

What You Will Learn

• Basics of Python for Data Science

• Data cleaning, preprocessing, and handling datasets

• Exploratory Data Analysis (EDA) techniques

• Applying statistics for decision-making

• Data visualization with Python libraries (Matplotlib, Seaborn)

• Introduction to Machine Learning concepts

• Working on mini-projects with real-world datasets

Course Modules

1. Introduction to Data Science & Tools

   • Understanding Data Science workflow

   • Overview of Python, Jupyter, and Libraries

2. Python for Data Science

   • Data types, control structures, and functions

   • Numpy and Pandas for data handling

3. Data Preprocessing & Cleaning

   • Handling missing values and duplicates

   • Data transformation and feature scaling

4. Exploratory Data Analysis (EDA)

   • Descriptive statistics

   • Identifying patterns and correlations

5. Data Visualization

   • Matplotlib & Seaborn basics

   • Building effective visual stories with data

6. Applied Statistics for Data Science

   • Probability & distributions

   • Hypothesis testing and significance

7. Introduction to Machine Learning

   • Supervised vs Unsupervised learning

   • Building your first ML model

8. Mini Projects & Case Studies

   • Real-world datasets (finance, healthcare, sales, etc.)

   • End-to-end data analysis project