Harvard University Free Course – Introduction to Data Science with Python

Learn how to harness and analyze data using Python in this online course taught by Harvard University educator Pavlos Protopapas.

What you’re going to discover

  • Get practical experience and practice applying Python to real-world data science problems.
  • Learn how to code in Python for modeling, statistics, and narrative.
  • Make use of well-known libraries like SKLearn, matplotlib, numPy, and pandas.
  • Use Python to run simple machine learning models, assess their performance, and apply them to real-world issues.
  • Lay the groundwork for your future Python studies by learning how to use Python in machine learning and artificial intelligence.

Course outline

Computers all throughout the world gather millions of gigabytes of data every minute. How are you going to sort through this deluge of information? How are these data used by data scientists for the applications that run the contemporary world?

Data science is a constantly developing field that parses complex data sets using scientific methods and algorithms. To manage and evaluate data, data scientists employ a variety of programming languages, including Python and R. The main topic of this course is data science with Python. You will have a basic understanding of machine learning models and artificial intelligence (AI) and machine learning (ML) by the end of the course.

Learners will use popular libraries like sklearn, Pandas, matplotlib, and numPy to investigate regression models (Linear, Multilinear, and Polynomial) and classification models (kNN, Logistic) using Python. Important machine learning principles include choosing the appropriate complexity, avoiding overfitting, regularization, evaluating trade-offs, assessing uncertainty, and model evaluation will all be covered in the course. Your proficiency with Python will increase as a result of taking this course, setting you up for further study in machine learning (ML) and artificial intelligence (AI) as well as job progression.

To succeed in this course, students need have a basic understanding of statistics and programming, preferably in Python. Prerequisites for Python can be satisfied by taking the Introduction to Programming with Python course offered by CS50. Prerequisites for statistics can be satisfied by taking Stat110 offered by HarvardX or by taking Fat Chance.