Old Version Intro to Statistics with R Learn the key statistical concepts and techniques used by statisticians and data scientists every day. Learn More

Time Series with R Learn the core techniques necessary to extract meaningful insights from time series data. Learn More

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.

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

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

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

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

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

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

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

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

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

Introduction to Data in R Learn the language of data, study types, sampling strategies, and experimental design.

Introduction to Shell for Data Science The Unix command line helps users combine existing programs in new ways, automate repetitive tasks, and run programs ...

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

Correlation and Regression in R Learn how to describe relationships between two numerical quantities and characterize these relationships graphically.

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

Exploratory Data Analysis in R Learn how to use graphical and numerical techniques to begin uncovering the structure of your data.

Introduction to Natural Language Processing in Python Learn fundamental natural language processing techniques using Python and how to apply them to extract insights from ...

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.

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

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

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

Time Series Analysis in R Learn the core techniques necessary to extract meaningful insights from time series data.

Multiple and Logistic Regression in R In this course you'll learn to add multiple variables to linear models and to use logistic regression for classificat...

Extreme Gradient Boosting with XGBoost Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve...

Forecasting Using R Learn how to make predictions about the future using time series forecasting in R.

Foundations of Inference in R Learn how to draw conclusions about a population from a sample of data via a process known as statistical inference.

Manipulating Time Series Data in R with xts & zoo The xts and zoo packages make the task of managing and manipulating ordered observations fast and mistake free.

Manipulating Time Series Data in Python In this course you'll learn the basics of working with time series data.

Importing & Managing Financial Data in Python In this course, you'll learn how to import and manage financial data in Python using various tools and sources.

ARIMA Modeling with R Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R.

Visualizing Time Series Data in Python Visualize seasonality, trends and other patterns in your time series data.

Visualizing Time Series Data in R Learn how to visualize time series in R, then practice with a stock-picking case study.

Network Analysis in Python (Part 2) Analyze time series graphs, use bipartite graphs, and gain the skills to tackle advanced problems in network analytics.

Parallel Computing with Dask Learn how to take the Python workflows you currently have and easily scale them up to large datasets without the need...

Introduction to Financial Concepts in Python Using Python and NumPy, learn the most fundamental financial concepts.

Forecasting Product Demand in R Learn how to identify important drivers of demand, look at seasonal effects, and predict demand for a hierarchy of pr...

Manipulating Time Series Data in R: Case Studies Strengthen your knowledge of the topics covered in Manipulating Time Series in R using real case study data.

Tidy Data in Python Mini-Course Most of the world's data are not sorted in a clean and organized fashion; nor are they easy to process. As a data sci...