Python for Data Science Course Online (Self-Paced)
IDOXC

Our bootcamp takes you from the fundamentals of Python programming all the way to the basics of machine learning with Python. You’ll learn how and why Python is a leading tool for data science, how to write programs, work with and manipulate data, create data visualizations, and apply statistics to build machine learning models.

Python Fundamentals

We’ll begin with core Python concepts, including writing basic statements and expressions, creating variables, understanding data types, working with lists, and using indexing and slicing. You’ll also learn to apply functions and methods, and gain an introduction to object-oriented programming. Once your development environment is set up, you’ll work with strings, lists, dictionaries, and tuples—understanding the unique purpose of each and when to use them.

Structuring Programs

Next, you’ll explore conditional statements and control flow tools such as if/else statements, boolean operations, and various types of loops. These form the logical backbone of your programs. You’ll practice creating functions, iterating through data with for loops, working with dictionaries, and managing packages in Python.

Arrays & DataFrames

You’ll then move into the essential tools for data science, learning to import and clean data with NumPy and Pandas. You’ll gain experience working with Pandas DataFrames, wrangling datasets, and generating descriptive statistics.

Analyzing & Visualizing Data

Learn to analyze and visualize data with Pandas, NumPy, and Matplotlib. You’ll filter, clean, group, and pivot data to uncover insights through exploratory data analysis, then create visualizations such as bar charts, histograms, and more advanced graphics for clear and effective communication of your findings.

Next Steps

After mastering Python programming and data analysis fundamentals, you’ll be ready to move into machine learning. Building on your skills from the Python for Data Science Bootcamp, you’ll advance to topics like logistic regression, k-nearest neighbors, and decision trees, positioning you to become a full-fledged machine learning data scientist.