Introduction to Data Science with Python

Name: Introduction to Data Science with Python
Rating: 4.3 (174 reviews)

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Overview

Learn the concepts and techniques that make up the foundation of data science and machine learning.

What you'll learn

Gain hands-on experience and practice using Python to solve real data science challenges
Practice Python programming and 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

Syllabus 8

Linear Regression
Multiple and Polynomial Regression
Model Selection and Cross-Validation
Bias, Variance, and Hyperparameters
Classification and Logistic Regression
Multi-logstic Regression and Missingness
Bootstrap, Confidence Intervals, and Hypothesis Testing
Capstone Project

Skills you'll practise

Parsing Data Science Statistics Matplotlib (Python Package)Scikit-Learn (Python Package)Machine Learning NumPy (Python Package)Scientific Methods Pandas (Python Package)Algorithms Python (Programming Language)Artificial Intelligence Programming Languages R (Programming Language)K-Nearest Neighbors Algorithm

Advantages and disadvantages

Advantages

One of the best-taught programming courses in the world, by David J. Malan and his team.
Problem sets are real projects that stay on your GitHub.
Free certificate from CS50 when you finish on cs50.harvard.edu (the edX certificate is paid).
Active community on Discord, Reddit and Ed for help at any hour.
University courses you can audit for free, with lectures, readings and practice quizzes.
A verified certificate from the university if you pay for it.
Taught by Harvard University, one of the strongest names in its field.
331,233 learners have taken it, so help and notes are easy to find.

Disadvantages

Problem sets are hard and take far longer than the videos; plan 10–20 hours a week.
Lectures are long (about 2 hours each).
Graded assignments and the certificate need the paid track.
Audit access can expire a few weeks after the course ends.
Learning is free, but the certificate costs money.
Some parts (graded work, certificate) are paid.

Some points apply to every course of this kind; see how we rank.

Free to audit

Free: Choose "Audit this course" when you enrol: lectures, readings and practice are free.
Paid: Graded assignments and the verified certificate (Certificate $299). Audit access may end after the course closes.

Before you start

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.

Taught by

Pavlos ProtopapasScientific Program Director

Harvard University is in Tier 1: world-leading universities and India's top institutes of our institution ranking (98/100). CS50 and HarvardX are among the best-taught courses anywhere.

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Opens edx.org

PlatformedX
UniversityHarvard University
PriceFree to audit Certificate $299
CertificatePaid certificate
LanguageEnglish
Time8 weeks, 3 - 5 hours per week
PaceSelf-paced
LevelIntermediate
Learners331,233

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