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Introduction to Python
Master the basics of data analysis in Python. Expand your skillset by learning scientific computing with NumPy.
Level up your data science skills by creating visualizations using Matplotlib and manipulating DataFrames with pandas.
Investigating Netflix Movies and Guest Stars in The Office
Apply the foundational Python skills you learned in Introduction to Python and Intermediate Python by manipulating and visualizing movie and TV data.
Data Manipulation with pandas
Use the world’s most popular Python data science package to manipulate data and calculate summary statistics.
Joining Data with pandas
Learn to combine data from multiple tables by joining data together using pandas.
The GitHub History of the Scala Language
Find the true Scala experts by exploring its development history in Git and GitHub.
Introduction to Data Visualization with Matplotlib
Learn how to create, customize, and share data visualizations using Matplotlib.
Introduction to Data Visualization with Seaborn
Learn how to create informative and attractive visualizations in Python using the Seaborn library.
Introduction to NumPy
Learn how to use NumPy arrays in Python to perform mathematical operations and wrangle data with the best of them!
Python Data Science Toolbox (Part 1)
Learn the art of writing your own functions in Python, as well as key concepts like scoping and error handling.
The Android App Market on Google Play
Load, clean, and visualize scraped Google Play Store data to gain insights into the Android app market.
Python Data Science Toolbox (Part 2)
Continue to build your modern Data Science skills by learning about iterators and list comprehensions.
Intermediate Data Visualization with Seaborn
Use Seaborn's sophisticated visualization tools to make beautiful, informative visualizations with ease.
A Visual History of Nobel Prize Winners
Explore a dataset from Kaggle containing a century's worth of Nobel Laureates. Who won? Who got snubbed?
Introduction to Importing Data in Python
Learn to import data into Python from various sources, such as Excel, SQL, SAS and right from the web.
Intermediate Importing Data in Python
Improve your Python data importing skills and learn to work with web and API data.
Cleaning Data in Python
Learn to diagnose and treat dirty data and develop the skills needed to transform your raw data into accurate insights!
Writing Functions in Python
Learn to use best practices to write maintainable, reusable, complex functions with good documentation.
Exploratory Data Analysis in Python
Learn how to explore, visualize, and extract insights from data.
Analyzing Police Activity with pandas
Explore the Stanford Open Policing Project dataset and analyze the impact of gender on police behavior using pandas.
Introduction to Statistics in Python
Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data using Python.
Introduction to Regression with statsmodels in Python
Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis in Python.
Sampling in Python
Learn to draw conclusions from limited data using Python and statistics. This course covers everything from random sampling to stratified and cluster sampling.
Hypothesis Testing in Python
Learn how and when to use common hypothesis tests like t-tests, proportion tests, and chi-square tests in Python.
Dr. Semmelweis and the Discovery of Handwashing
Reanalyse the data behind one of the most important discoveries of modern medicine: handwashing.
Supervised Learning with scikit-learn
Grow your machine learning skills with scikit-learn in Python. Use real-world datasets in this interactive course and learn how to make powerful predictions!
Predicting Credit Card Approvals
Build a machine learning model to predict if a credit card application will get approved.
Unsupervised Learning in Python
Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.
Machine Learning with Tree-Based Models in Python
In this course, you'll learn how to use tree-based models and ensembles for regression and classification using scikit-learn.