Master the basics of data analysis in Python. Expand your skillset by learning scientific computing with numpy.
Master the basics of data analysis by manipulating common data structures such as vectors, matrices, and data frames.
Master the basics of querying tables in relational databases such as MySQL, SQL Server, and PostgreSQL.
Level up your data science skills by creating visualizations using Matplotlib and manipulating DataFrames with pandas.
Dive into data science using Python and learn how to effectively analyze and visualize your data.
Learn how to build and tune predictive models and evaluate how well they'll perform on unseen data.
Discover how data engineers lay the groundwork that makes data science possible. No coding involved!
An introduction to data science with no coding involved.
Use the world’s most popular Python data science package to manipulate data and calculate summary statistics.
Join two or three tables together into one, combine tables using set theory, and work with subqueries in PostgreSQL.
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.
Continue your journey to becoming an R ninja by learning about conditional statements, loops, and vector functions.
Learn the art of writing your own functions in Python, as well as key concepts like scoping and error handling.
Learn how to analyze data in Excel.
Get started with Tableau, a widely used business intelligence (BI) and analytics software to explore, visualize, and securely share data.
Gain a 360° overview of how to explore and use Power BI to build impactful reports.
Learn to produce meaningful and beautiful data visualizations with ggplot2 by understanding the grammar of graphics.
Continue to build your modern Data Science skills by learning about iterators and list comprehensions.
An introduction to machine learning with no coding involved.
Master the complex SQL queries necessary to answer a wide variety of data science questions and prepare robust data sets for analysis in PostgreSQL.
Learn how to create, customize, and share data visualizations using Matplotlib.
Learn to import data into Python from various sources, such as Excel, SQL, SAS and right from the web.
Learn how to efficiently collect and download data from any website using R.
Learn to combine data from multiple tables by joining data together using pandas.
Become proficient at using SQL Server to perform common data manipulation tasks.
Learn how to use the industry-standard pandas library to import, build, and manipulate DataFrames.
Learn to transform and manipulate your data using dplyr.
Build the foundation you need to think statistically and to speak the language of your data.
Learn how to create one of the most efficient ways of storing data - relational databases!
Learn to write efficient code that executes quickly and allocates resources skillfully to avoid unnecessary overhead.
Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.
Learn to diagnose and treat dirty data and develop the skills needed to transform your raw data into accurate insights!
Learn how to describe relationships between two numerical quantities and characterize these relationships graphically.
Learn how to explore, visualize, and extract insights from data.
Develop the skills you need to go from raw data to awesome insights as quickly and accurately as possible.
In this course, you will learn to read CSV, XLS, and text files in R using tools like readxl and data.table.
Learn to implement distributed data management and machine learning in Spark using the PySpark package.
Learn how to create informative and attractive visualizations in Python using the Seaborn library.
Learn the fundamentals of neural networks and how to build deep learning models using Keras 2.0.
Learn the language of data, study types, sampling strategies, and experimental design.
Dive in and learn how to create classes and leverage inheritance and polymorphism to reuse and optimize code.
Improve your Python data importing skills and learn to work with web and API data.
Learn fundamental natural language processing techniques using Python and how to apply them to extract insights from real-world text data.
Learn how to analyze data with spreadsheets using functions such as SUM(), AVERAGE(), and VLOOKUP().
Learn to use best practices to write maintainable, reusable, complex functions with good documentation.
Learn how to use graphical and numerical techniques to begin uncovering the structure of your data.
In this course you'll learn to add multiple variables to linear models and to use logistic regression for classification.
In this course you will learn the basics of machine learning for classification.
In this course you will learn the details of linear classifiers like logistic regression and SVM.
Learn to retrieve and parse information from the internet using the Python library scrapy.
Analyze the gender distribution of children's book writers and use sound to match names to gender.
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.
Load, clean, and explore Super Bowl data in the age of soaring ad costs and flashy halftime shows.
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 understand 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.
Plot Google Trends data to find the most famous Kardashian/Jenner sister. Is it Kim? Kendall? Kylie?
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.
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.