Learn all about how DataCamp builds the best platform to learn and teach data skills.
This course is an introduction to version control with Git for data scientists.
This course focuses on feature engineering and machine learning for time series data.
This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.
This course will show you how to combine and merge datasets with data.table.
This course will equip you with the skills to analyze, visualize, and make sense of networks using the NetworkX library.
This course teaches the big ideas in machine learning like how to build and evaluate predictive models.
This course will show you how to integrate spatial data into your Python Data Science workflow.
This course is for R users who want to get up to speed with Python!
This course is an introduction to linear algebra, one of the most important mathematical topics underpinning data science.
This course covers the basics of financial trading and how to use quantstrat to build signal-based trading strategies.
This course is designed to get you up to speed with the most important and powerful methodologies in statistics.
This course will introduce the support vector machine (SVM) using an intuitive, visual approach.
This course covers in detail the tools available in R for parallel computing.
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'll learn the basics of working with time series data.
In this course you will learn the details of linear classifiers like logistic regression and SVM.
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 how to get your cleaned data ready for modeling.
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, students will learn to write queries that are both efficient and easy to read and understand.
In this course, you'll learn how to import and manage financial data in Python using various tools and sources.
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 will learn to fit hierarchical models with random effects.
In this course you'll learn to use and present logistic regression models for making predictions.
In this course you'll learn about basic experimental design, a crucial part of any data analysis.
In this course you'll learn to build dashboards using the shinydashboard package.
In this course you'll learn how to perform inference using linear models.
In this course you'll learn how to create static and interactive dashboards using flexdashboard and shiny.
In this course, you’ll explore the modern MLOps framework, exploring the lifecycle and deployment of machine learning models.
In this course you'll learn techniques for performing statistical inference on numerical data.
In this course you'll learn how to leverage statistical techniques for working with categorical data.
In this course you'll learn how to apply machine learning in the HR domain.
In this course you will gain an overview clinical trial designs, determine the numbers of patients needed and conduct statistical analyses.
In this course, you'll learn how to implement more advanced Bayesian models using RJAGS.
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'll learn how to use data science for several common marketing tasks.
In this interactive course, you’ll learn how to use functions for your Tableau calculations and when you should use them!
In this conceptual course (no coding required), you will learn about the four major NoSQL databases and popular engines.
In this four-hour course, you’ll learn the basics of analyzing time series data in Python.
This introductory and conceptual course will help you understand the fundamentals of data warehousing.
The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.
In this follow-up course, you will expand your stat modeling skills from the introduction and dive into more advanced concepts.
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.
Get ready to categorize! In this course, you will work with non-numerical data, such as job titles or survey responses, using the Tidyverse landscape.
Analyze the network of characters in Game of Thrones and how it changes over the course of the books.
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!
If you've never done a DataCamp project, this is the place to start!
Learn to analyze Twitter data and do a deep dive into a hot trend.
Use coding best practices and functions to improve a script!
Use your logistic regression skills to protect people from becoming zombies!