Introduction to Python
Master the basics of data analysis with Python in just four hours. This online course will introduce the Python interface and explore popular packages.
Follow short videos led by expert instructors and then practice what you’ve learned with interactive exercises in your browser.
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.
Learn the art of writing your own functions in Python, as well as key concepts like scoping and error handling.
Continue your journey to becoming an R ninja by learning about conditional statements, loops, and vector functions.
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 how to create one of the most efficient ways of storing data - relational databases!
Learn to write efficient code that executes quickly and allocates resources skillfully to avoid unnecessary overhead.
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 to implement distributed data management and machine learning in Spark using the PySpark package.
Dive in and learn how to create classes and leverage inheritance and polymorphism to reuse and optimize code.
Learn to use best practices to write maintainable, reusable, complex functions with good documentation.
Dive into data science using Python and learn how to effectively analyze and visualize your data. No coding experience or skills needed.
The Unix command line helps users combine existing programs in new ways, automate repetitive tasks, and run programs on clusters and clouds.
Master the basics of querying tables in relational databases such as MySQL, SQL Server, and PostgreSQL.
Gain an introduction to Docker and discover its importance in the data professional’s toolkit. Learn about Docker containers, images, and more.
Learn to use SQL Server to perform common data manipulation tasks and master common data manipulation tasks using this database system.
Bring your spreadsheets to life by mastering fundamental skills such as formulas, operations, and cell references.
Explore data structures such as linked lists, stacks, queues, hash tables, and graphs; and search and sort algorithms!
Learn how to work with dates and times in Python.
Learn about modularity, documentation, and automated testing to help you solve data science problems more quickly and reliably.
Learn the fundamentals of working with big data with PySpark.
Take your R skills up a notch by learning to write efficient, reusable functions.
Julia is a new programming language designed to be the ideal language for scientific computing, machine learning, and data mining.
Consolidate and extend your knowledge of Python data types such as lists, dictionaries, and tuples, leveraging them to solve Data Science problems.
Learn how to write unit tests for your Data Science projects in Python using pytest.
In this course, you will use T-SQL, the flavor of SQL used in Microsoft's SQL Server for data analysis.
Learn to write faster R code, discover benchmarking and profiling, and unlock the secrets of parallel programming.
This course is an introduction to version control with Git for data scientists.