Learn how to create one of the most efficient ways of storing data - relational databases!
Learn the most important PostgreSQL functions for manipulating, processing, and transforming data.
Learn how and when to use common hypothesis tests like t-tests, proportion tests, and chi-square tests in Python.
Learn to use best practices to write maintainable, reusable, complex functions with good documentation.
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.
Learn how to analyze a SQL table and report insights to management.
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 combine data across multiple tables to answer more complex questions with dplyr.
Learn the fundamentals of data visualization using spreadsheets.
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.
Expand your spreadsheets vocabulary by diving deeper into data types, including numeric data, logical data, and missing data.
Learn fundamental natural language processing techniques using Python and how to apply them to extract insights from real-world text data.
In this course, you will learn to read CSV, XLS, and text files in R using tools like readxl and data.table.
Learn the key concepts of data modeling on Power BI.
Take your Tableau skills up a notch with advanced analytics and visualizations.
Learn to design databases in SQL.
Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis with statsmodels in Python.
This course is an introduction to version control with Git for data scientists.
Learn to use facets, coordinate systems and statistics in ggplot2 to create meaningful explanatory plots.
Data is all around us, which makes data literacy an essential life skill.
Learn to retrieve and parse information from the internet using the Python library scrapy.
The Unix command line helps users combine existing programs in new ways, automate repetitive tasks, and run programs on clusters and clouds.
Use Seaborn's sophisticated visualization tools to make beautiful, informative visualizations with ease.
A non-coding introduction to cloud computing, covering key concepts, terminology, and tools.
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 how to use graphical and numerical techniques to begin uncovering the structure of your data.
Learn how to work with dates and times in Python.
In this course, you will be introduced to unsupervised learning through techniques such as hierarchical and k-means clustering using the SciPy library.
In this course you'll learn the basics of working with time series data.
Learn to use Python for financial analysis using basic skills, including lists, data visualization, and arrays.
Discover how to make better business decisions by applying practical data frameworks—no coding required.
Learn how and when to use hypothesis testing in R, including t-tests, proportion tests, and chi-square tests.
Dashboards are a must-have in a data-driven world. Increase your impact on business performance with Tableau dashboards.
Explore the Stanford Open Policing Project dataset and analyze the impact of gender on police behavior using pandas.
In this course you will learn the basics of machine learning for classification.
Learn to perform linear and logistic regression with multiple explanatory variables.
You will investigate a dataset from a fictitious company called Databel in Power BI, and need to figure out why customers are churning.
In this course you will learn the details of linear classifiers like logistic regression and SVM.
Learn to clean data as quickly and accurately as possible to help your business move from raw data to awesome insights.
Learn how to write unit tests for your Data Science projects in Python using pytest.
Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.
Learn how to build and tune predictive models and evaluate how well they'll perform on unseen data.
You’ll learn how to (un)pivot, transpose, append and join tables. Gain power with custom columns, M language, and the Advanced Editor.
Julia is a new programming language designed to be the ideal language for scientific computing, machine learning, and data mining.
Learn how to implement and schedule data engineering workflows.
Build the foundation you need to think statistically and to speak the language of your data.
Find tables, store and manage new tables and views, and write maintainable SQL code to answer business questions.
Learn the fundamentals of working with big data with PySpark.
Learn to acquire data from common file formats and systems such as CSV files, spreadsheets, JSON, SQL databases, and APIs.
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!