Join Harvard University instructor Pavlos Protopapas in this online course to learn how to use Python to harness and analyze data.
Duration
October 25, 2023 - October 23, 2024
Price
Free*
Modality
Online
Image
Time Commitment
3 - 4 hours per week
Pace
Self-paced
Subject
Course Language
English
Video transcript
English
Difficulty
Intermediate
Credit
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Platform
edX
Topics
Associated Schools
Harvard School of Engineering and Applied Sciences
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
Course description
Every single minute, computers across the world collect millions of gigabytes of data. What can you do to make sense of this mountain of data? How do data scientists use this data for the applications that power our modern world?
Data science is an ever-evolving field, using algorithms and scientific methods to parse complex data sets. Data scientists use a range of programming languages, such as Python and R, to harness and analyze data. This course focuses on using Python in data science. By the end of the course, you’ll have a fundamental understanding of machine learning models and basic concepts around Machine Learning (ML) and Artificial Intelligence (AI).
Using Python, learners will study regression models (Linear, Multilinear, and Polynomial) and classification models (kNN, Logistic), utilizing popular libraries such as sklearn, Pandas, matplotlib, and numPy. The course will cover key concepts of machine learning such as: picking the right complexity, preventing overfitting, regularization, assessing uncertainty, weighing trade-offs, and model evaluation. Participation in this course will build your confidence in using Python, preparing you for more advanced study in Machine Learning (ML) and Artificial Intelligence (AI), and advancement in your career.
Learners must have a minimum baseline of programming knowledge (preferably in Python) and statistics in order to be successful in this course. Python prerequisites can be met with an introductory Python course offered through CS50’s Introduction to Programming with Python, and statistics prerequisites can be met via Fat Chance or with Stat110 offered through HarvardX.
Instructors
Scientific Program Director, Institute for Applied Computational Science, John A. Paulson School of Engineering and Applied Sciences, Harvard University
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