Learn all about how DataCamp builds the best platform to learn and teach data skills.
This course introduces Python for financial analysis.
This course is an introduction to version control with Git for data scientists.
This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.
This course focuses on feature engineering and machine learning for time series data.
This course teaches the big ideas in machine learning like how to build and evaluate predictive models.
This course will equip you with the skills to analyze, visualize, and make sense of networks using the NetworkX library.
This course provides a comprehensive introduction to working with base graphics in R.
This course will show you how to combine and merge datasets with data.table.
This course is all about the act of combining, or merging, DataFrames, an essential part your Data Scientist's toolbox.
This course covers in detail the tools available in R for parallel computing.
This course is an introduction to linear algebra, one of the most important mathematical topics underpinning data science.
This course is designed to get you up to speed with the most important and powerful methodologies in statistics.
This course covers the basics of financial trading and how to use quantstrat to build signal-based trading strategies.
This course will show you how to integrate spatial data into your Python Data Science workflow.
This course teaches you the skills and knowledge necessary to create and manage your own PostgreSQL databases.
This course is for R users who want to get up to speed with Python!
This course will introduce the support vector machine (SVM) using an intuitive, visual approach.
In this course, you will learn to read CSV, XLS, and text files in R using tools like readxl and data.table.
In this course, you'll learn how to use tree-based models and ensembles for regression and classification using scikit-learn.
In this course you will learn the basics of machine learning for classification.
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 will learn the details of linear classifiers like logistic regression and SVM.
In this course you'll learn the basics of working with time series data.
In this course, you will use T-SQL, the flavor of SQL used in Microsoft's SQL Server for data analysis.
In this course, you'll learn the basics of relational databases and how to interact with them.
In this course you will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.
In this course you'll learn to add multiple variables to linear models and to use logistic regression for classification.
In this course you'll learn how to get your cleaned data ready for modeling.
In this course you'll learn the basics of analyzing time series data.
In this course, you'll learn how to import and manage financial data in Python using various tools and sources.
In this course, students will learn to write queries that are both efficient and easy to read and understand.
In this course, you'll learn how to collect Twitter data and analyze Twitter text, networks, and geographical origin.
In this course, you'll learn about the concepts of random variables, distributions, and conditioning.
In this course you'll learn about basic experimental design, a crucial part of any data analysis.
In this course, you'll learn how to use tree-based models and ensembles for regression and classification.
In this course you will learn to fit hierarchical models with random effects.
In this course you'll learn to analyze and visualize network data with the igraph package.
In this course you'll learn to build dashboards using the shinydashboard package.
In this course you'll learn how to leverage statistical techniques for working with categorical data.
In this course you'll learn to use and present logistic regression models for making predictions.
In this course, you'll learn how to implement more advanced Bayesian models using RJAGS.
In this course you'll learn how to create static and interactive dashboards using flexdashboard and shiny.
In this course you'll learn techniques for performing statistical inference on numerical data.
In this course you'll learn how to perform inference using linear models.
In this course you'll learn how to use data science for several common marketing tasks.
In this course you'll learn how to apply machine learning in the HR domain.
In this course, you'll prepare for the most frequently covered statistical topics from distributions to hypothesis testing, regression models, and much more.
In this course you will gain an overview clinical trial designs, determine the numbers of patients needed and conduct statistical analyses.
The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.
Analyze the network of characters in Game of Thrones and how it changes over the course of the books.
Build a machine learning classifier that knows whether President Trump or Prime Minister Trudeau is tweeting!
Get ready for Halloween by digging into a FiveThirtyEight dataset with all your favorite candy!
If you've never done a DataCamp project, this is the place to start!
If you've never done a DataCamp project, this is the place to start!
If you have never done a DataCamp project, this is the place to start!
Flex your pandas muscles on breath alcohol test data from Ames, Iowa, USA.
Flex your tidyverse muscles on breath alcohol test data from Ames, Iowa, USA.
Write functions to forecast time series of food prices in Rwanda.
Predict the impact of climate change on bird distributions using spatial data and machine learning.
Apply your importing and data cleaning skills to real-world soccer data.
Use regression trees and random forests to find places where New York taxi drivers earn the most.
Apply your skills from "Working with Dates and Times in R" to breathalyzer data from Ames, Iowa.
Compare life expectancy across countries and genders with ggplot2.
Reanalyse the data behind one of the most important discoveries of modern medicine: handwashing.
Use NLP and clustering on movie plot summaries from IMDb and Wikipedia to quantify movie similarity.
Reanalyse the data behind one of the most important discoveries of modern medicine: handwashing.
Experiment with clustering algorithms to help doctors inform treatment for heart disease patients.
In this project we will explore a database of every LEGO set ever built.
Use logistic regression to determine which treatment procedure is more effective for kidney stone removal.
You will explore the market capitalization of Bitcoin and other cryptocurrencies.
Use data science to catch criminals, plus find new ways to volunteer personal time for social good.
Load, clean, and visualize scraped Google Play Store data to understand the Android app market.
Analyze the dialog and IMDB ratings of 287 South Park episodes. Warning: contains explicit language.
Analyze data from the hit mobile game, Candy Crush Saga.
Discover the top tools Kaggle participants use for data science and machine learning.
Examine the relationship between heart rate and heart disease using multiple logistic regression.
Apply hierarchical and mixed-effect models to analyze Maryland crime rates.
Wrangle and visualize musical data to find common chords and compare the styles of different artists.
Build a convolutional neural network to classify images of letters from American Sign Language.
Use Natural Language Processing on NIPS papers to uncover the trendiest topics in machine learning research.
Learn to analyze Twitter data and do a deep dive into a hot trend.
Use MLB's Statcast data to compare New York Yankees sluggers Aaron Judge and Giancarlo Stanton.
Use text mining to analyze Jeopardy! data.
Create and explore interactive maps using Leaflet to determine where to open the next Chipotle.
Use pandas to calculate and compare profitability and risk of different investments using the Sharpe Ratio.
Discover how the US bond yields behave using descriptive statistics and advanced modeling.
Use R to make art and create imaginary flowers inspired by nature.
Build a machine learning model to predict if a credit card application will get approved.
Automatically generate keywords for a search engine marketing campaign using Python.
Classify patients with suspected infections using data.table and electronic health records.
Analyze athletics data to find new ways to scout and assess jumpers and throwers.
Build a model that can automatically detect honey bees and bumble bees in images.
Recreate John Snow's famous map of the 1854 cholera outbreak in London.
Find out about the evolution of the Linux operating system by exploring its version control system.
Scrape news headlines for FB and TSLA then apply sentiment analysis to generate investment insight.
Plot Google Trends data to find the most famous Kardashian/Jenner sister. Is it Kim? Kendall? Kylie?
Use web scraping and NLP to find the most frequent words in Herman Melville's novel, Moby Dick.
Explore the salary potential of college majors with a k-means cluster analysis.
Build a book recommendation system using NLP and the text of books like "On the Origin of Species."