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Python for Data Science
This course is part of multiple programs. Learn more.
Course Cost
₹ 25,972
Intermediate
Skill Level
8 Weeks
Self-paced Video lessons
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.
4.3
English
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
Shareable certificate
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Get a Completion Certificate
Share your certificate with prospective employers and your professional network on LinkedIn.
CREATED BY
PROVIDED BY
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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
Classification and Logistic Regression
Module 5
Multi-logistic Regression and Missingness
Module 6
Bootstrap, Confidence Intervals, and Hypothesis Testing
Module 7
Capstone Project
Module 8
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Learning Python for Data Science, Python for Data Science and Machine Learning, Data Science and Machine Learning
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Faculties
These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.
9
Courses
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.
Frequently asked Questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.