Data science courses

Master the basics of data analysis with Python in just four hours. This online course will introduce the Python interface and explore popular packages.

Master the basics of data analysis in R, including vectors, lists, and data frames, and practice R with real data sets.

Level up your data science skills by creating visualizations using Matplotlib and manipulating DataFrames with pandas.

Dive into data science using Python and learn how to effectively analyze and visualize your data. No coding experience or skills needed.

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

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

Master the complex SQL queries necessary to answer a wide variety of data science questions and prepare robust data sets for analysis in PostgreSQL.

Get started on the path to exploring and visualizing your own data with the tidyverse, a powerful and popular collection of data science tools within R.

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

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

Dive in and learn how to create classes and leverage inheritance and polymorphism to reuse and optimize code.

Master the basics of querying tables in relational databases such as MySQL, SQL Server, and PostgreSQL.

Learn how to analyze data with spreadsheets using functions such as SUM(), AVERAGE(), and VLOOKUP().

Learn how to create one of the most efficient ways of storing data - relational databases!

Learn to use SQL Server to perform common data manipulation tasks and master common data manipulation tasks using this database system.

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

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

Expand your spreadsheets vocabulary by diving deeper into data types, including numeric data, logical data, and missing data.

This course is an introduction to version control with Git for data scientists.

The Unix command line helps users combine existing programs in new ways, automate repetitive tasks, and run programs on clusters and clouds.

Learn how to work with dates and times in Python.

Learn the fundamentals of working with big data with PySpark.

Julia is a new programming language designed to be the ideal language for scientific computing, machine learning, and data mining.

Learn how to write unit tests for your Data Science projects in Python using pytest.

Consolidate and extend your knowledge of Python data types such as lists, dictionaries, and tuples, leveraging them to solve Data Science problems.

Learn to write faster R code, discover benchmarking and profiling, and unlock the secrets of parallel programming.

Take your R skills up a notch by learning to write efficient, reusable functions.

Learn the essentials of parsing, manipulating and computing with dates and times in R.

Bash scripting allows you to build analytics pipelines in the cloud and work with data stored across multiple files.

Shiny is an R package that makes it easy to build interactive web apps directly in R, allowing your team to explore your data as dashboards or visualizations.

Learn the basics of spreadsheets by working with rows, columns, addresses, and ranges.

Learn about modularity, documentation, and automated testing to help you solve data science problems more quickly and reliably.

In this course, you will use T-SQL, the flavor of SQL used in Microsoft's SQL Server for data analysis.

Begin your journey with Scala, a popular language for scalable applications and data engineering infrastructure.

Familiarize yourself with Git for version control. Explore how to track, compare, modify, and revert files, as well as collaborate with colleagues using Git.

Learn efficient techniques in pandas to optimize your Python code.

Master SQL Server programming by learning to create, update, and execute functions and stored procedures.

Manage the complexity in your code using object-oriented programming with the S3 and R6 systems.

Prepare for your next coding interviews in Python.

Learn about AWS Boto and harnessing cloud technology to optimize your data workflow.

Learn how to pull character strings apart, put them back together and use the stringr package.

Learn to create your own Python packages to make your code easier to use and share with others.

In this course, students will learn to write queries that are both efficient and easy to read and understand.

Learn how to structure your PostgreSQL queries to run in a fraction of the time.

Use your knowledge of common spreadsheet functions and techniques to explore Python!

Learn how to design and implement triggers in SQL Server using real-world examples.

Learn to easily summarize and manipulate lists using the purrr package.

This course is for R users who want to get up to speed with Python!

Create and share your own R Packages!

Learn how to use Python parallel programming with Dask to upscale your workflows and efficiently handle big data.

Bring your spreadsheets to life by mastering fundamental skills such as formulas, operations, and cell references.

Learn how to run big data analysis using Spark and the sparklyr package in R, and explore Spark MLIb in just 4 hours.

Learn how to write recursive queries and query hierarchical data structures.

Learn defensive programming in R to make your code more robust.

Continue learning with purrr to create robust, clean, and easy to maintain iterative code.

Learn to automate many common file system tasks and be able to manage and communicate with processes.

This course covers in detail the tools available in R for parallel computing.

Learn how to translate your SAS knowledge into R and analyze data using this free and powerful software language.

Use C++ to dramatically boost the performance of your R code.

Transition from MATLAB by learning some fundamental Python concepts, and diving into the NumPy and Matplotlib packages.

Learn how to write scalable code for working with big data in R using the bigmemory and iotools packages.