Data Scientist with Python A Data Scientist combines statistical and machine learning techniques with Python programming to analyze and interpret complex data. Learn More

Introduction to Python Master the basics of data analysis in Python. Expand your skill set by learning scientific computing with numpy.

Introduction to R Master the basics of data analysis by manipulating common data structures such as vectors, matrices and data frames.

Intro to SQL for Data Science Master the basics of querying tables in relational databases such as MySQL, Oracle, SQL Server, and PostgreSQL.

Intermediate Python for Data Science Level up your data science skills by creating visualizations using matplotlib and manipulating data frames with Pandas.

Joining Data in SQL Join two or three tables together into one, combine tables using set theory, and work with subqueries in PostgreSQL.

Deep Learning in Python Learn the fundamentals of neural networks and how to build deep learning models using Keras 2.0.

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.

Intermediate R Continue your journey to become an R ninja by learning about conditional statements, loops, and vector functions.

pandas Foundations Learn how to use the industry-standard pandas library to import, build, and manipulate DataFrames.

Supervised Learning with scikit-learn Learn how to build and tune predictive models and evaluate how well they will perform on unseen data.

Importing Data in Python (Part 1) Learn to import data into Python from various sources, such as Excel, SQL, SAS and right from the web.

Statistical Thinking in Python (Part 1) Build the foundation you need to think statistically and to speak the language of your data.

Introduction to the Tidyverse Get started on the path to exploring and visualizing your own data with the tidyverse, a powerful and popular collect...

Introduction to Data Visualization with Python Learn more complex data visualization techniques using Matplotlib and Seaborn.

Python Data Science Toolbox (Part 2) Continue to build your modern Data Science skills by learning about iterators and list comprehensions.

Cleaning Data in Python This course will equip you with all the skills you need to clean your data in Python.

Importing Data in R (Part 1) In this course, you will learn to read CSV, XLS, and text files in R using tools like readxl and data.table.

Importing Data in Python (Part 2) Improve your Python data importing skills and learn to work with web and API data.

Manipulating DataFrames with pandas You will learn how to tidy, rearrange, and restructure your data using versatile pandas DataFrames.

Introduction to Databases in Python In this course, you'll learn the basics of relational databases and how to interact with them.

Introduction to PySpark Learn to implement distributed data management and machine learning in Spark using the PySpark package.

Network Analysis in Python (Part 1) This course will equip you with the skills to analyze, visualize, and make sense of networks using the NetworkX library.

Merging DataFrames with pandas This course is all about the act of combining, or merging, DataFrames, an essential part your Data Scientist's toolbox.

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

Statistical Thinking in Python (Part 2) Learn to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing.

Web Scraping in Python Learn to retrieve and parse information from the internet using the Python library scrapy.

Interactive Data Visualization with Bokeh Learn how to create versatile and interactive data visualizations using Bokeh.

Importing Data in R (Part 2) Parse data in any format. Whether it's flat files, statistical software, databases, or data right from the web.

Machine Learning with the Experts: School Budgets Learn how to build a model to automatically classify items in a school budget.

Importing & Cleaning Data in R: Case Studies In this series of four case studies, you'll revisit key concepts from our courses on importing and cleaning data in R.

Convolutional Neural Networks for Image Processing Convolutional neural networks are deep learning algorithms that are particularly powerful for analysis of images.

Advanced Deep Learning with Keras in Python Build multiple-input and multiple-output deep learning models using Keras.

Software Engineering for Data Scientists in Python Learn all about modularity, documentation, & automated testing to help you solve Data Science problems quicker an...

Linear Classifiers in Python In this course you will learn the details of linear classifiers like logistic regression and SVM.

Writing Efficient Python Code Learn to write efficient code that executes quickly and allocates resources skillfully to avoid unnecessary overhead.