Data science courses

Learn how to create and query relational databases using SQL in just two hours.

Accompanied at every step with hands-on practice queries, this course teaches you everything you need to know to analyze data using your own SQL code today!

Learn how to import and clean data, calculate statistics, and create visualizations with pandas.

Level up your SQL knowledge and learn to join tables together, apply relational set theory, and work with subqueries.

Build real-world Excel skills in just 4 hours. This course will show you time-saving shortcuts and essential functions.

Learn to combine data from multiple tables by joining data together using pandas.

Enhance your Power BI knowledge, by learning the fundamentals of Data Analysis Expressions (DAX) such as calculated columns, tables, and measures.

Learn how to explore, visualize, and extract insights from data using exploratory data analysis (EDA) in Python.

Learn how to explore what's available in a database: the tables, relationships between them, and data stored in them.

Delve further into the Tidyverse by learning to transform and manipulate data with dplyr.

Learn how to create queries for analytics and data engineering with window functions, the SQL secret weapon!

Master your skills in NumPy by learning how to create, sort, filter, and update arrays using NYC’s tree census.

Learn the most important PostgreSQL functions for manipulating, processing, and transforming data.

Explore the world of Pivot Tables within Google Sheets, and learn how to quickly organize thousands of data points with just a few clicks of the mouse.

Learn the key concepts of data modeling on Power BI.

In this interactive Power BI course, you’ll learn how to use Power Query Editor to transform and shape your data to be ready for analysis.

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

Learn to combine data across multiple tables to answer more complex questions with dplyr.

You’ll learn how to (un)pivot, transpose, append and join tables. Gain power with custom columns, M language, and the Advanced Editor.

In this course you'll learn the basics of working with time series data.

Learn to use spreadsheets to clean, analyze, and draw insights from data. Discover how to sort, filter, and use VLOOKUP to combine data.

Data Analysis Expressions (DAX) allow you to take your Power BI skills to the next level by writing custom functions.

Master data modeling in Power BI.

Learn how to manipulate and visualize categorical data using pandas and seaborn.

Learn about string manipulation and become a master at using regular expressions.

Explore the Stanford Open Policing Project dataset and analyze the impact of gender on police behavior using pandas.

Discover a wide range of DAX calculations and learn how to use them in Microsoft Power BI.

In this course, you'll learn the basics of relational databases and how to interact with them.

Learn how to use conditional formatting with your data through built-in options and by creating custom formulas.

Reshape DataFrames from a wide to long format, stack and unstack rows and columns, and wrangle multi-index DataFrames.

Discover the different ways you can enhance your Power BI data importing skills.

Learn how to identify, analyze, remove and impute missing data in Python.

Enhance your reports with trend analysis techniques such as time series, decomposition trees, and key influencers.

Learn to manipulate and analyze flexibly structured data with MongoDB.

Transform almost any dataset into a tidy format to make analysis easier.

The xts and zoo packages make the task of managing and manipulating ordered observations fast and mistake free.

Analyze text data in R using the tidy framework.

Learn powerful command-line skills to download, process, and transform data, including machine learning pipeline.

Master time series data manipulation in R, including importing, summarizing and subsetting, with zoo, lubridate and xts.

Sharpen your skills in Oracle SQL including SQL basics, aggregating, combining, and customizing data.

Learn the gritty details that data scientists are spending 70-80% of their time on; data wrangling and feature engineering.

In this course, you'll learn how to collect Twitter data and analyze Twitter text, networks, and geographical origin.

Explore ways to work with date and time data in SQL Server for time series analysis

Learn how to use PostgreSQL to handle time series analysis effectively and apply these techniques to real-world data.

Learn the most important functions for manipulating, processing, and transforming data in SQL Server.

Master core concepts about data manipulation such as filtering, selecting and calculating groupwise statistics using data.table.

Learn how to manipulate data and create machine learning feature sets in Spark using SQL in Python.

This course will show you how to integrate spatial data into your Python Data Science workflow.

This course will show you how to combine and merge datasets with data.table.

Learn the fundamentals of exploring, manipulating, and measuring biomedical image data.

Learn to write scripts that will catch and handle errors and control for multiple operations happening at once.

Get ready to categorize! In this course, you will work with non-numerical data, such as job titles or survey responses, using the Tidyverse landscape.

Explore association rules in market basket analysis with R by analyzing retail data and creating movie recommendations.

Learn how to load, transform, and transcribe speech from raw audio files in Python.

Learn how to effectively and efficiently join datasets in tabular format using the Python Pandas library.

Manipulate text data, analyze it and more by mastering regular expressions and string distances in R.

Extract and visualize Twitter data, perform sentiment and network analysis, and map the geolocation of your tweets.

Learn how to perform advanced dplyr transformations and incorporate dplyr and ggplot2 code in functions.

Diagnose, visualize and treat missing data with a range of imputation techniques with tips to improve your results.

Learn how to import, clean and manipulate IoT data in Python to make it ready for machine learning.

Ensure data consistency by learning how to use transactions and handle errors in concurrent environments.