Gain the career-building Python skills you need to succeed as a data scientist. No prior coding experience required.
In this track, you'll learn how this versatile language allows you to import, clean, manipulate, and visualize data—all integral skills for any aspiring data professional or researcher. Through interactive exercises, you'll get hands-on with some of the most popular Python libraries, including pandas, NumPy, Matplotlib, and many more. You'll then work with real-world datasets to learn the statistical and machine learning techniques you need to train decision trees and use natural language processing (NLP). Start this track, grow your Python skills, and begin your journey to becoming a confident data scientist.
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.
PROJECT 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.
Use the world’s most popular Python data science package to manipulate data and calculate summary statistics.
PROJECT The Android App Market on Google Play Load, clean, and visualize scraped Google Play Store data to gain insights into the Android app market.
Learn to combine data from multiple tables by joining data together using pandas.
PROJECT The GitHub History of the Scala Language Find the true Scala experts by exploring its development history in Git and GitHub.
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.
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.
Use Seaborn's sophisticated visualization tools to make beautiful, informative visualizations with ease.
PROJECT 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?
Skill Assessment Data Manipulation with Python Advanced Score: 140 | Percentile: 91%
Learn to import data into Python from various sources, such as Excel, SQL, SAS and right from the web.
Improve your Python data importing skills and learn to work with web and API data.
Learn to diagnose and treat dirty data and develop the skills needed to transform your raw data into accurate insights!
Learn how to work with dates and times in Python.
Skill Assessment Importing & Cleaning Data with Python Intermediate Score: 100 | Percentile: 50%
Learn to use best practices to write maintainable, reusable, complex functions with good documentation.
Skill Assessment Python Programming Advanced Score: 154 | Percentile: 96%
Learn how to explore, visualize, and extract insights from data.
23 Analyzing Police Activity with pandas Explore the Stanford Open Policing Project dataset and analyze the impact of gender on police behavior using pandas.
24 Statistical Thinking in Python (Part 1) Build the foundation you need to think statistically and to speak the language of your data.
25 Statistical Thinking in Python (Part 2) Learn to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing.
26 Solidify your knowledge from previous courses PROJECT Dr. Semmelweis and the Discovery of Handwashing Reanalyse the data behind one of the most important discoveries of modern medicine: handwashing.
27 Supervised Learning with scikit-learn Learn how to build and tune predictive models and evaluate how well they'll perform on unseen data.
28 Solidify your knowledge from previous courses PROJECT Predicting Credit Card Approvals Build a machine learning model to predict if a credit card application will get approved.
29 Unsupervised Learning in Python Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.
30 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 sciki...
31 Case Study: School Budgeting with Machine Learning in Python Learn how to build a model to automatically classify items in a school budget.
32 Cluster Analysis in Python In this course, you will be introduced to unsupervised learning through techniques such as hierarchical and k-means c...
Statement of Accomplishment