Learn how to use graphical and numerical techniques to begin uncovering the structure of your data.
In this course, you'll learn the basics of relational databases and how to interact with them.
Consolidate and extend your knowledge of Python data types such as lists, dictionaries, and tuples, leveraging them to solve Data Science problems.
Learn complex data visualization techniques using Matplotlib and seaborn.
Learn how to explore what's available in a database: the tables, relationships between them, and data stored in them.
In this course, you'll learn how to use tree-based models and ensembles for regression and classification using scikit-learn.
Learn about the world of data engineering with an overview of all its relevant topics and tools!
Learn fundamental natural language processing techniques using Python and how to apply them to extract insights from real-world text data.
Use Seaborn's sophisticated visualization tools to make beautiful, informative visualizations with ease.
In this course you will learn the basics of machine learning for classification.
Learn about data science and how can you use it to strengthen your organization.
Learn to combine data across multiple tables to answer more complex questions with dplyr.
In this course, you will be introduced to unsupervised learning through techniques such as hierarchical and k-means clustering using the SciPy library.
Learn how to work with dates and times in Python.
An introduction to data visualization with no coding involved.
Explore the Stanford Open Policing Project dataset and analyze the impact of gender on police behavior using pandas.
Learn to acquire data from common file formats and systems such as CSV files, spreadsheets, JSON, SQL databases, and APIs.
Learn to process, transform, and manipulate images at your will.
Learn to design databases in SQL.
Parse data in any format. Whether it's flat files, statistical software, databases, or data right from the web.
You will learn how to tidy, rearrange, and restructure your data using versatile pandas DataFrames.
Take your Tableau skills up a notch with advanced analytics and visualizations.
R Markdown is an easy-to-use formatting language for authoring dynamic reports from R code.
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 the basics of working with time series data.
A non-coding introduction to the world of cloud computing.
Learn how to create queries for analytics and data engineering with window functions, the SQL secret weapon!
This course introduces Python for financial analysis.
Learn the fundamentals of how to build conversational bots using rule-based systems as well as machine learning.
In this course you will learn the details of linear classifiers like logistic regression and SVM.
Learn to start developing deep learning models with Keras.
In this course, you will use T-SQL, the flavor of SQL used in Microsoft's SQL Server for data analysis.
This course provides a comprehensive introduction to working with base graphics in R.
Learn how to to implement and schedule data engineering workflows.
Learn the fundamentals of neural networks and how to build deep learning models using TensorFlow.
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 to write faster R code, discover benchmarking and profiling, and unlock the secrets of parallel programming.
Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis in R.
Learn the core techniques necessary to extract meaningful insights from time series data.
In this course you'll learn the basics of analyzing time series data.
Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.
Use data manipulation and visualization skills to explore the historical voting of the United Nations General Assembly.
Take your R skills up a notch by learning to write efficient, reusable functions.
This course will equip you with the skills to analyze, visualize, and make sense of networks using the NetworkX library.
Learn the essentials of parsing, manipulating and computing with dates and times in R.
In this course, you'll learn about the concepts of random variables, distributions, and conditioning.
Learn essential data structures such as lists and data frames and apply that knowledge directly to financial examples.
Learn how to build a model to automatically classify items in a school budget.
Learn the most important PostgreSQL functions for manipulating, processing, and transforming data.
Learn about string manipulation and become a master at using regular expressions.
Flex your pandas 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 Herman Melville's novel, Moby Dick.
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 tidyverse 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.
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.
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.
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.
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.
In this project we will explore a database of every LEGO set ever built.