Associate Data Scientist in Python

Apply the foundational Python skills you learned in Introduction to Python and Intermediate Python by manipulating and visualizing movie data.

Use data manipulation and summary statistics to analyze test scores across New York City's public schools!

Learn to combine data from multiple tables by joining data together using pandas.

Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data using Python.

Learn how to create, customize, and share data visualizations using Matplotlib.

Learn how to create informative and attractive visualizations in Python using the Seaborn library.

Explore a dataset containing a century's worth of Nobel Laureates. Who won? Who got snubbed?

Learn the art of writing your own functions in Python, as well as key concepts like scoping and error handling.

Continue to build your modern Data Science skills by learning about iterators and list comprehensions.

Learn how to explore, visualize, and extract insights from data using exploratory data analysis (EDA) in Python.

Find out when and where crime is most likely to occur, along with the types of crimes commonly committed in LA.

Learn how to manipulate and visualize categorical data using pandas and seaborn.

Apply your knowledge of data types and categorical data to prepare a big dataset for modeling!

No one enjoys looking at spreadsheets! Bring your data to life. Improve your presentation and learn how to translate technical data into actionable insights.

Learn to import data into Python from various sources, such as Excel, SQL, SAS and right from the web.

Learn to diagnose and treat dirty data and develop the skills needed to transform your raw data into accurate insights!

Apply your data importing, cleaning and manipulation skills to explore New York City Airbnb data.

Learn how to work with dates and times in Python.

Learn to use best practices to write maintainable, reusable, complex functions with good documentation.

Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis with statsmodels in Python.

Clean customer data and use logistic regression to predict whether people will make a claim on their car insurance!

Learn to draw conclusions from limited data using Python and statistics. This course covers everything from random sampling to stratified and cluster sampling.

Learn how and when to use common hypothesis tests like t-tests, proportion tests, and chi-square tests in Python.

Implement experimental design setups and perform robust statistical analyses to make precise and valid conclusions!

Perform a hypothesis test to determine if more goals are scored in women's soccer matches than men's!

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!

Dive into agriculture using supervised machine learning and feature selection to aid farmers in crop cultivation and solve real-world problems.

Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.

Arctic Penguin Exploration: Unraveling Clusters in the Icy Domain with K-means Clustering

In this course, you'll learn how to use tree-based models and ensembles for regression and classification using scikit-learn.

Build a regression model for a DVD rental firm to predict rental duration. Evaluate models to recommend the best one.