Python for Data Science Online Course

Python for Data Science Online Course

About the Course

Python is a powerful, open-source, general-purpose programming language that has become a cornerstone of modern data science. Its flexibility, extensive library ecosystem, and widespread community support make it ideal for tasks such as data analysis, visualization, and machine learning. This course is designed to equip you with the essential tools and skills needed to perform data science using Python.

Throughout the course, you’ll dive into practical, hands-on projects using core Python libraries. You’ll learn:

• NumPy for numerical and scientific computing
• Matplotlib and Seaborn for data visualization
• How to clean, transform, and prepare data for analysis
• The end-to-end process of building machine learning models
• How to apply your knowledge on real-world datasets

With a strong focus on real-world applications, you'll gain experience by working through projects that mirror the challenges faced in the field of data science.

By the end of this course, you’ll have a solid foundation in Python for data science—from data cleaning and exploratory analysis to predictive modeling and visualization. Get ready to step confidently into the world of data science.

Who should take this Course?

Course Curriculum

Beginning the Data Science Journey

• The Course Overview
• What Is Data Science?
• Python Data Science Ecosystem

Introducing Jupyter

• Installing Anaconda
• Starting Jupyter
• Basics of Jupyter
• Markdown Syntax

Understanding Numerical Operations with NumPy

• 1D Arrays with NumPy
• 2D Arrays with NumPy
• Functions in NumPy
• Random Numbers and Distributions in NumPy

Data Preparation and Manipulation with Pandas

• Create DataFrames
• Read in Data Files
• Subsetting DataFrames
• Boolean Indexing in DataFrames
• Summarizing and Grouping Data

Visualizing Data with Matplotlib and Seaborn

• Matplotlib Introduction
• Graphs with Matplotlib
• Graphs with Seaborn
• Graphs with Pandas

Introduction to Machine Learning and Scikit-learn

• Machine Learning
• Types of Machine Learning
• Introduction to Scikit-learn

Building Machine Learning Models with Scikit-learn

• Linear Regression
• Logistic Regression
• K-Nearest Neighbors
• Decision Trees
• Random Forest
• K-Means Clustering

Model Evaluation and Selection

• Preparing Data for Machine Learning
• Performance Metrics
• Bias-Variance Tradeoff
• Cross-Validation
• Grid Search
• Wrap Up