Debbie Liske

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.

Intermediate Python for Data Science

Level up your data science skills by creating visualizations using matplotlib and manipulating data frames with Pandas.

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.

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.

Deep Learning in Python

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

Data Visualization with ggplot2 (Part 1)

Learn to produce meaningful and beautiful data visualizations with ggplot2 by understanding the grammar of graphics.

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.

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.

Data Manipulation in R with dplyr

Master techniques for data manipulation using the select, mutate, filter, arrange, and summarise functions in dplyr.

Cleaning Data in R

Learn to explore your data so you can properly clean and prepare it for analysis.

Manipulating DataFrames with pandas

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

Introduction to Data

Learn the language of data, study types, sampling strategies, and experimental design.

Correlation and Regression

Learn how to describe relationships between two numerical quantities and characterize these relationships graphically.

Introduction to Git for Data Science

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

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.

Merging DataFrames with pandas

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

Writing Functions in R

Learn the fundamentals of writing functions in R so you can make your code more readable and automate repetitive tasks.

Introduction to Shell for Data Science

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

Exploratory Data Analysis

Learn how to use graphical and numerical techniques to begin uncovering the structure of your data.

Reporting with R Markdown

Learn to create interactive analyses and automated reports with R Markdown.

Joining Data in R with dplyr

This course will show you how to combine data sets with dplyr's two table verbs.

Intermediate R - Practice

Strengthen your knowledge of the topics you learned in Intermediate R with a ton of new and fun exercises.

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.

Data Visualization in R

This course provides a comprehensive introduction to working with base graphics in R.

Text Mining: Bag of Words

Learn the bag of words technique for text mining with R.

Data Types for Data Science

Consolidate and extend your knowledge of Python data types such as lists, dictionaries, and tuples, leveraging them t...

Python for R Users

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