Master the basics of data analysis in Python. Expand your data science skill set by learning scientific computing with numpy.
Level up your Python data science skills by creating visualizations using matplotlib and manipulating data frames with Pandas.
Learn how to build and tune predictive models and evaluate how well they will perform on unseen data.
This course will equip you with the skills to analyze, visualize, and make sense of networks using the NetworkX library.
Learn to import data into Python from various sources, such as Excel, SQL, SAS and right from the web.
Learn the art of writing your own functions in Python, as well as key concepts like scoping and error handling.
This course is all about the act of combining, or merging, DataFrames, an essential part of any working Data Scientist's toolbox.
In this course, you'll learn how to use the industry-standard pandas library to import, build, and manipulate DataFrames.
Build the foundation you need to think statistically and to speak the language of your data.
Continue to build your modern Data Science skills by learning about iterators and list comprehensions.
Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.
In this course, you'll learn the basics of relational databases and how to interact with them.
Learn more complex data visualization techniques using Matplotlib and Seaborn.
Further improve your Python data importing skills and learn to work with more web and API data.
You will learn how to tidy, rearrange, and restructure your data using versatile pandas DataFrames.
Learn how to create versatile and interactive data visualizations using Bokeh.
Learn to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing.
Learn how to access financial data from local files as well as from internet sources.
Learn about how dates work in R, and explore the world of if statements, loops, and functions. You'll practice this knowledge u...