Learning the fundamentals of programming, such as variables, data types, control structures, functions, and loops in Python

Before diving into the data science tools, you need to understand the basics of statistics and probability, such as central tendency, variance, probability distributions, and hypothesis testing.

Learn how to clean and transform data from raw sources, including text files, spreadsheets, and databases. You will learn how to perform data manipulation using libraries such as Pandas, Numpy, and SQL.

Visualize Data. Data visualization tools, such as Matplotlib and Seaborn, will help you create visual representations of your data and communicate insights effectively.

Learn how to build models that can predict outcomes and patterns in your data. Learn about supervised and unsupervised learning algorithms and techniques, such as linear regression, logistic regression, decision trees, and clustering.

This project will demonstrate the student's ability to work with data, create visualizations, and build machine learning models to solve real-world problems.