Python for Data Science
Master fundamental data science and machine learning concepts using Python, from regression models to classification techniques.
Master fundamental data science and machine learning concepts using Python, from regression models to classification techniques.
This comprehensive course introduces students to essential data science concepts and techniques using Python programming. Learn to analyze complex datasets using popular libraries like sklearn, Pandas, matplotlib, and numPy. The curriculum covers regression models, classification techniques, and key machine learning concepts including model complexity, overfitting prevention, and evaluation methods. Through hands-on practice with real-world data challenges, students develop practical skills in machine learning and artificial intelligence applications.
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What you'll learn
Use Python to solve real-world data science challenges
Implement machine learning models using popular Python libraries
Evaluate and optimize model performance
Apply statistical methods for data analysis
Visualize and communicate data insights effectively
Develop foundation for advanced machine learning studies
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 8 modules in this course
This course provides a comprehensive introduction to data science using Python. Students learn to handle and analyze large datasets, implement various machine learning models, and evaluate their performance. The curriculum covers essential topics including linear regression, polynomial regression, model selection, cross-validation, classification techniques, and confidence intervals. Through practical exercises and a capstone project, students gain hands-on experience in applying these concepts to real-world data science challenges.
Linear Regression
Module 1
Multiple and Polynomial Regression
Module 2
Model Selection and Cross-Validation
Module 3
Bias, Variance, and Hyperparameters
Module 4
Multi-logistic Regression and Missingness
Module 6
Bootstrap, Confidence Intervals, and Hypothesis Testing
Module 7
Capstone Project
Module 8
Fee Structure
Instructor
9 Courses
Harvard Data Science and Computational Science Expert
Pavlos Protopapas is the Scientific Program Director of the Institute for Applied Computational Science (IACS) at Harvard's John A. Paulson School of Engineering and Applied Sciences. With a distinguished career spanning physics, astronomy, and data science, Protopapas has become a leading figure in computational science education and research. He holds a Ph.D. in theoretical physics from the University of Pennsylvania and has focused his recent work on applying machine learning and AI to astronomy and computer science.
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