10 Free Certification Courses from Harvard University

10. Introduction to Data Science with Python

Course description: Computers around the world collect millions of gigabytes of data every minute. However, it can be challenging to make sense of this vast amount of data. That’s where data scientists come in.

They use algorithms and scientific methods to analyze and parse complex data sets. Programming languages like Python and R are commonly used in data science.

This course focuses on using Python to learn about machine learning models and basic concepts around Machine Learning (ML) and Artificial Intelligence (AI).

Throughout the course, learners will explore regression models (Linear, Multilinear, and Polynomial) and classification models (kNN, Logistic) using popular libraries such as sklearn, Pandas, matplotlib, and numPy.

They will also cover key concepts of machine learning such as selecting the right complexity, preventing overfitting, regularization, assessing uncertainty, weighing trade-offs, and model evaluation.

By the end of the course, participants will have a fundamental understanding of machine learning models and basic concepts around Machine Learning (ML) and Artificial Intelligence (AI).

To be successful in this course, learners should have a minimum baseline of programming knowledge, preferably in Python, and statistics. They can meet Python prerequisites by taking an introductory Python course offered through CS50’s Introduction to Programming with Python, and statistics prerequisites via Fat Chance or with Stat110 offered through HarvardX.

The course will help learners build confidence in using Python and prepare them for more advanced study in Machine Learning (ML) and Artificial Intelligence (AI), and career advancement. Course link

What you’ll learn

• Gain hands-on experience and practice using Python to solve real data science challenges
• Practice Python coding for modeling, statistics, and storytelling
• Utilize popular libraries such as Pandas, numPy, matplotlib, and SKLearn
• Run basic machine learning models using Python, evaluate how those models are performing, and apply those models to real-world problems
• Build a foundation for the use of Python in machine learning and artificial intelligence, preparing you for future Python study