Master the basics of data analysis with Python in just four hours. This online course will introduce the Python interface and explore popular packages.
Learn how to create and query relational databases using SQL in just two hours.
Learn how to use ChatGPT. Discover best practices for writing prompts and explore common business use cases for the powerful AI tool.
Master the basics of data analysis in R, including vectors, lists, and data frames, and practice R with real data sets.
An introduction to data science with no coding involved.
Level up your data science skills by creating visualizations using Matplotlib and manipulating DataFrames with pandas.
Master the Power BI basics and learn to use the data visualization software to build impactful reports.
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.
Discover how data engineers lay the groundwork that makes data science possible. No coding involved!
Grow your machine learning skills with scikit-learn in Python. Use real-world datasets in this interactive course and learn how to make powerful predictions!
Master the Excel basics and learn to use this spreadsheet tool to conduct impactful analysis.
Master the complex SQL queries necessary to answer a wide variety of data science questions and prepare robust data sets for analysis in PostgreSQL.
Start your Tableau journey with our Introduction to Tableau course. Discover Tableau basics such as its features and dashboards.
Learn the art of writing your own functions in Python, as well as key concepts like scoping and error handling.
An introduction to machine learning with no coding involved.
Uncover AI's challenges and societal implications in this theoretical course. Discover machine learning, deep learning, NLP, generative models & more.
Continue your journey to becoming an R ninja by learning about conditional statements, loops, and vector functions.
Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data using Python.
Learn how to analyze data with PivotTables and intermediate logical functions before moving on to tools such as what-if analysis and forecasting.
Learn to combine data from multiple tables by joining data together using pandas.
Learn how to create, customize, and share data visualizations using Matplotlib.
Get started on the path to exploring and visualizing your own data with the tidyverse, a powerful and popular collection of data science tools within R.
Discover the full potential of LLMs with our conceptual course covering LLM applications, training methodologies, ethical considerations, and latest research.
Learn how to explore what's available in a database: the tables, relationships between them, and data stored in them.
Learn the fundamentals of statistics, including measures of center and spread, probability distributions, and hypothesis testing with no coding involved!
Enhance your Power BI knowledge, by learning the fundamentals of Data Analysis Expressions (DAX) such as calculated columns, tables, and measures.
Continue to build your modern Data Science skills by learning about iterators and list comprehensions.
An introduction to data visualization with no coding involved.
Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data.
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.
Learn to produce meaningful and beautiful data visualizations with ggplot2 by understanding the grammar of graphics.
Power BI is a powerful data visualization tool that can be used in reports and dashboards.
Learn how to create informative and attractive visualizations in Python using the Seaborn library.
Learn to import data into Python from various sources, such as Excel, SQL, SAS and right from the web.
Learn how to explore, visualize, and extract insights from data using exploratory data analysis (EDA) in Python.
Learn to diagnose and treat dirty data and develop the skills needed to transform your raw data into accurate insights!
Learn to write efficient code that executes quickly and allocates resources skillfully to avoid unnecessary overhead.
Learn how to create queries for analytics and data engineering with window functions, the SQL secret weapon!
Learn how to create one of the most efficient ways of storing data - relational databases!
Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis in R.
Discover how to begin responsibly leveraging generative AI. Learn how generative AI models are developed and how they will impact society moving forward.
No one enjoys looking at spreadsheets! Bring your data to life. Improve your presentation and learn how to translate technical data into actionable insights.
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 implement distributed data management and machine learning in Spark using the PySpark package.
Learn about data science for managers and businesses and how to use data to strengthen your organization.
In this course, you'll learn how to use tree-based models and ensembles for regression and classification using scikit-learn.
Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.
A non-coding introduction to cloud computing, covering key concepts, terminology, and tools.
In this project, we will use data manipulation skills to zoom in on a time when Lego explored a new direction for their toy line!
Analyze the gender distribution of children's book writers and use sound to match names to gender.
Visualize the rise of COVID-19 cases globally with ggplot2.
You will explore the market capitalization of Bitcoin and other cryptocurrencies.
Use text mining to analyze Jeopardy! data.
Wrangle and visualize musical data to find common chords and compare the styles of different artists.
Learn to analyze Twitter data and do a deep dive into a hot trend.
Analyze the network of characters in Game of Thrones and how it changes over the course of the books.
Apply your importing and data cleaning skills to real-world soccer data.
Write SQL queries to answer interesting questions about international debt using data from The World Bank.
Explore Disney movie data, then build a linear regression model to predict box office success.
Discover the top tools Kaggle participants use for data science and machine learning.
Discover how the US bond yields behave using descriptive statistics and advanced modeling.
Import, clean, and analyze seven years worth of training data tracked on the Runkeeper app.
Use tree-based machine learning methods to identify the characteristics of legendary Pokémon.
Use logistic regression to determine which treatment procedure is more effective for kidney stone removal.
Process ingredient lists for cosmetics on Sephora then visualize similarity using t-SNE and Bokeh.
Load, clean, and explore Super Bowl data in the age of soaring ad costs and flashy halftime shows.
Use data manipulation and visualization to explore one of two different television broadcast datasets: The Super Bowl and hit sitcom The Office!
Check what passwords fail to conform to the National Institute of Standards and Technology password guidelines.
Analyze health survey data to determine how BMI is associated with physical activity and smoking.
Apply hierarchical and mixed-effect models to analyze Maryland crime rates.
Use your logistic regression skills to protect people from becoming zombies!
Predict the impact of climate change on bird distributions using spatial data and machine learning.
Use pandas to calculate and compare profitability and risk of different investments using the Sharpe Ratio.
Use NLP and clustering on movie plot summaries from IMDb and Wikipedia to quantify movie similarity.
Build a binary classifier to predict if a blood donor is likely to donate again.
Use cluster analysis to glean insights into cryptocurrency gambling behavior.
Apply unsupervised learning techniques to help plan an education program in Argentina.
Use R to make art and create imaginary flowers inspired by nature.
Load, clean, and visualize scraped Google Play Store data to gain insights into the Android app market.
Use data science to catch criminals, plus find new ways to volunteer personal time for social good.
Scrape news headlines for FB and TSLA then apply sentiment analysis to generate investment insight.
Build a book recommendation system using NLP and the text of books like "On the Origin of Species."
Explore the salary potential of college majors with a k-means cluster analysis.
If you've never done a DataCamp project, this is the place to start!
Analyze admissions data from UC Berkeley and find out if the university was biased against women.
Analyze the dialog and IMDB ratings of 287 South Park episodes. Warning: contains explicit language.
Build a machine learning model to predict if a credit card application will get approved.
Build a deep learning model that can automatically detect honey bees and bumble bees in images.
Experiment with clustering algorithms to help doctors inform treatment for heart disease patients.
Explore acoustic backscatter data to find fish in the U.S. Atlantic Ocean.
Manipulate and plot time series data from Google Trends to analyze changes in search interest over time.
Write functions to forecast time series of food prices in Rwanda.
Apply text mining to Donald Trump's tweets to confirm if he writes the (angrier) Android half.
Build a convolutional neural network to classify images of letters from American Sign Language.
Play bank data scientist and use regression discontinuity to see which debts are worth collecting.
Use regression trees and random forests to find places where New York taxi drivers earn the most.
Reanalyse the data behind one of the most important discoveries of modern medicine: handwashing.
Apply your skills from "Working with Dates and Times in R" to breathalyzer data from Ames, Iowa.