In this course, you'll learn how to use tree-based models and ensembles for regression and classification using scikit-learn.
You will investigate a dataset from a fictitious company called Databel in Power BI, and need to figure out why customers are churning.
Use Seaborn's sophisticated visualization tools to make beautiful, informative visualizations with ease.
Join two or three tables together into one, combine tables using set theory, and work with subqueries in PostgreSQL.
Learn how to use graphical and numerical techniques to begin uncovering the structure of your data.
Learn the key concepts of data modeling on Power BI.
Learn to design databases in SQL.
Learn to use best practices to write maintainable, reusable, complex functions with good documentation.
Learn to use SQL Server to perform common data manipulation tasks and master common data manipulation tasks using this database system.
Dive in and learn how to create classes and leverage inheritance and polymorphism to reuse and optimize code.
Gain an introduction to data in this hands-on course. Learn the basics of data types and structures, the DIKW framework, data ethics and more.
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 retrieve and parse information from the internet using the Python library scrapy.
Take your Tableau skills up a notch with advanced analytics and visualizations.
Master the basics of querying tables in relational databases such as MySQL, SQL Server, and PostgreSQL.
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.
Learn to use Python for financial analysis using basic skills, including lists, data visualization, and arrays.
Improve your Python data importing skills and learn to work with web and API data.
Learn how to analyze data with spreadsheets using functions such as SUM(), AVERAGE(), and VLOOKUP().
Learn to combine data across multiple tables to answer more complex questions with dplyr.
The Unix command line helps users combine existing programs in new ways, automate repetitive tasks, and run programs on clusters and clouds.
Learn how and when to use common hypothesis tests like t-tests, proportion tests, and chi-square tests in Python.
Learn how to analyze a SQL table and report insights to management.
Learn the fundamentals of AI. No programming experience required!
Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis with statsmodels in Python.
Dashboards are a must-have in a data-driven world. Increase your impact on business performance with Tableau dashboards.
Learn about the world of data engineering in this short course, covering tools and topics like ETL and cloud computing.
In this course, you will learn to read CSV, XLS, and text files in R using tools like readxl and data.table.
Learn how to manipulate and visualize categorical data using pandas and seaborn.
R Markdown is an easy-to-use formatting language for authoring dynamic reports from R code.
Learn to draw conclusions from limited data using Python and statistics. This course covers everything from random sampling to stratified and cluster sampling.
Learn to use spreadsheets to clean, analyze, and draw insights from data. Discover how to sort, filter, and use VLOOKUP to combine data.
Learn how to build and tune predictive models and evaluate how well they'll perform on unseen data.
Data is all around us, which makes data literacy an essential life skill.
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 to use facets, coordinate systems and statistics in ggplot2 to create meaningful explanatory plots.
Learn to perform linear and logistic regression with multiple explanatory variables.
Learn how to explore, visualize, and extract insights from data.
Learn how to work with dates and times in Python.
Learn how to leverage statistical techniques using spreadsheets to more effectively work with and extract insights from your data.
Learn to clean data as quickly and accurately as possible to help your business move from raw data to awesome insights.
In this course you'll learn the basics of working with time series data.
Learn the fundamentals of working with big data with PySpark.
Data Analysis Expressions (DAX) allow you to take your Power BI skills to the next level by writing custom functions.
Learn the basics of spreadsheets by working with rows, columns, addresses, and ranges.
In this course you will learn the basics of machine learning for classification.
You’ll learn how to (un)pivot, transpose, append and join tables. Gain power with custom columns, M language, and the Advanced Editor.
Learn to start developing deep learning models with Keras.
Build the foundation you need to think statistically and to speak the language of your data.
Expand your spreadsheets vocabulary by diving deeper into data types, including numeric data, logical data, and missing data.
Use pandas and Bayesian statistics to see if left-handed people actually die earlier than righties.
Create and explore interactive maps using Leaflet to determine where to open the next Chipotle.
Flex your data manipulation muscles on breath alcohol test data from Ames, Iowa, USA.
Build a machine learning classifier that knows whether President Trump or Prime Minister Trudeau is tweeting!
How can we find a good strategy for reducing traffic-related deaths?
How can we find a good strategy for reducing traffic-related deaths?
Get ready for Halloween by digging into a FiveThirtyEight dataset with all your favorite candy!
Rock or rap? Apply machine learning methods in Python to classify songs into genres.
Examine the relationship between heart rate and heart disease using multiple logistic regression.
Explore a dataset from Kaggle containing a century's worth of Nobel Laureates. Who won? Who got snubbed?
Examine the network of connections among local health departments in the United States.
Analyze the relative popularity of programming languages over time based on Stack Overflow data.
Build a model that can automatically detect honey bees and bumble bees in images.
If you have never done a DataCamp project, this is the place to start!
Automatically generate keywords for a search engine marketing campaign using Python.
Use web scraping and NLP to find the most frequent words in one of two pieces of classic literature: Herman Melville's novel, Moby Dick, or Peter Pan by J. M. Barrie.
Load, transform, and understand images of honey bees and bumble bees in Python.
If you've never done a DataCamp project, this is the place to start!
Explore a dataset from Kaggle containing a century's worth of Nobel Laureates. Who won? Who got snubbed?
Use MLB's Statcast data to compare New York Yankees sluggers Aaron Judge and Giancarlo Stanton.
Flex your data manipulation muscles on breath alcohol test data from Ames, Iowa, USA.
Reanalyse the data behind one of the most important discoveries of modern medicine: handwashing.
Analyze an A/B test from the popular mobile puzzle game, Cookie Cats.
Analyze athletics data to find new ways to scout and assess jumpers and throwers.
Compare life expectancy across countries and genders with ggplot2.
Find the true Scala experts by exploring its development history in Git and GitHub.
Use Natural Language Processing on NIPS papers to uncover the trendiest topics in machine learning research.
Use data manipulation, cleaning, and feature engineering skills to prepare a payment dataset for fraud prediction modeling.
Analyze product data for an online sports retail company to optimize revenue.
Use categorization and ranking techniques to explore 101 years of American baby name tastes.
Use summary statistics and filters to analyze test scores across New York City's public schools!
Use joins and set theory to discover the best years for video games!
Check what passwords fail to conform to the National Institute of Standards and Technology password guidelines.
Analyze data from the hit mobile game, Candy Crush Saga.
Use joining techniques to discover the oldest businesses in the world.
Apply data importing and cleaning skills to extract insights about the New York City Airbnb market.
Recreate John Snow's famous map of the 1854 cholera outbreak in London.
Use data management and exploratory data analysis skills to help analyze the UK Dairy Industry.
Apply the foundational Python skills you learned in Introduction to Python and Intermediate Python by manipulating and visualizing movie and TV data.
Use coding best practices and functions to improve a script!
Use DataFrames to read and merge employee data from different sources.
Apply data importing and cleaning skills to extract insights about the New York City Airbnb market.
Load, clean, and visualize scraped Google Play Store data to gain insights into the Android app market.
Reanalyse the data behind one of the most important discoveries of modern medicine: handwashing.
Analyze the relative popularity of programming languages over time based on Stack Overflow data.
Manipulate and plot time series data from Google Trends to analyze changes in search interest over time.
Apply the foundational Python skills you learned in Introduction to Python and Intermediate Python by manipulating and visualizing movie and TV data.
Use joining techniques to discover the oldest businesses in the world.
Find out about the evolution of the Linux operating system by exploring its version control system.
In this project, you will use importing and text manipulation skills to find out the main protagonists in Peter Pan!