Python is a general-purpose programming language that is becoming ever more popular for data science. Companies worldwide are using Python to harvest insights from their data and gain a competitive edge. Unlike other Python tutorials, this course focuses on Python specifically for data science. In our Introduction to Python course, you’ll learn about powerful ways to store and manipulate data, and helpful data science tools to begin conducting your own analyses. Start DataCamp’s online Python curriculum now.

Master the basics of data analysis in Python. Expand your skillset by learning scientific computing with NumPy.

Discover how to use Python for data science, learning about the ways to store and manipulate data in the environment to begin conducting your own analyses.

Introduction to Python

After all of the hard work of acquiring data and getting them into a form you can work with, you ultimately want to make clear, succinct conclusions from them. This crucial last step of a data analysis pipeline hinges on the principles of statistical inference. In this course, you will start building the foundation you need to think statistically, speak the language of your data, and understand what your data is telling you. The foundations of statistical thinking took decades to build, but can be grasped much faster today with the help of computers. With the power of Python-based tools, you will rapidly get up-to-speed and begin thinking statistically by the end of this course.

Build the foundation you need to think statistically and to speak the language of your data.

Statistical Thinking in Python (Part 1)

Deep learning is the machine learning technique behind the most exciting capabilities in diverse areas like robotics, natural language processing, image recognition, and artificial intelligence, including the famous AlphaGo. In this course, you'll gain hands-on, practical knowledge of how to use deep learning with Keras 2.0, the latest version of a cutting-edge library for deep learning in Python.

Learn the fundamentals of neural networks and how to build deep learning models using Keras 2.0.

Discover how you can use deep learning to run natural language processing, image recognition, and artificial intelligence with Python package, Keras 2.0.

Introduction to Deep Learning in Python

Say you have a collection of customers with a variety of characteristics such as age, location, and financial history, and you wish to discover patterns and sort them into clusters. Or perhaps you have a set of texts, such as Wikipedia pages, and you wish to segment them into categories based on their content. This is the world of unsupervised learning, called as such because you are not guiding, or supervising, the pattern discovery by some prediction task, but instead uncovering hidden structure from unlabeled data. Unsupervised learning encompasses a variety of techniques in machine learning, from clustering to dimension reduction to matrix factorization. In this course, you'll learn the fundamentals of unsupervised learning and implement the essential algorithms using scikit-learn and SciPy. You will learn how to cluster, transform, visualize, and extract insights from unlabeled datasets, and end the course by building a recommender system to recommend popular musical artists.

Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.

Unsupervised Learning in Python

Functions are a fundamental building block of the R language. You've probably used dozens (or even hundreds) of functions written by others, but in order to take your R game to the next level, you'll need to learn to write your own functions. This course will teach you the fundamentals of writing functions in R so that, among other things, you can make your code more readable, avoid coding errors, and automate repetitive tasks.

Learn the fundamentals of writing functions in R so you can make your code more readable and automate repetitive tasks.

This course will teach you the fundamentals of writing functions in R so that you can make your code more readable, avoid coding errors, and automate tasks.

R Functions

Writing Functions in R

After completing Statistical Thinking in Python (Part 1), you have the probabilistic mindset and foundational hacker stats skills to dive into data sets and extract useful information from them. In this course, you will do just that, expanding and honing your hacker stats toolbox to perform the two key tasks in statistical inference, parameter estimation and hypothesis testing. You will work with real data sets as you learn, culminating with analysis of measurements of the beaks of the Darwin's famous finches. You will emerge from this course with new knowledge and lots of practice under your belt, ready to attack your own inference problems out in the world.

Learn to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing.

Statistical Thinking in Python (Part 2)

In this course, you'll learn how to leverage pandas' extremely powerful data manipulation engine to get the most out of your data. You’ll learn how to drill into the data that really matters by extracting, filtering, and transforming data from DataFrames. The pandas library has many techniques that make this process efficient and intuitive. You will learn how to tidy, rearrange, and restructure your data by pivoting or melting and stacking or unstacking DataFrames. These are all fundamental next steps on the road to becoming a well-rounded data scientist, and you will have the chance to apply all the concepts you learn to real-world datasets.

You will learn how to tidy, rearrange, and restructure your data using versatile pandas DataFrames.

Manipulating DataFrames with pandas

The R data.table package is rapidly making its name as the number one choice for handling large datasets in R. This online data.table tutorial will bring you from data.table novice to expert in no time. Once you are introduced to the general form of a data.table query, you will learn the techniques to subset your data.table, how to update by reference and how you can use data.table’s set()-family in your workflow. The course finishes with more complex concepts such as indexing, keys and fast ordered joins. Upon completion of the course, you will be able to use data.table in R for a more efficient manipulation and analysis process. Enjoy!

Master core concepts in data manipulation such as subsetting, updating, indexing and joining your data using data.table.

Master core concepts in data manipulation such as subsetting, updating, indexing and joining your data using data.table in this course. 

Merge, Join, Subset Data in R

Data Analysis in R, the data.table Way

Probability is the study of making predictions about random phenomena. In this course, you'll learn about the concepts of random variables, distributions, and conditioning, using the example of coin flips. You'll also gain intuition for how to solve probability problems through random simulation. These principles will help you understand statistical inference and can be applied to draw conclusions from data.

In this course, you'll learn about the concepts of random variables, distributions, and conditioning.

Learn about random variables, distributions and conditioning, while gaining intuition for how to solve probability problems through random simulation.

Foundations of Probability in R

The most successful companies today are the ones that know their customers so well that they can anticipate their needs.
Customer analytics and in particular A/B Testing are crucial parts of
leveraging quantitative know-how to help make business decisions that generate value. This course
 covers the ins and outs of how to use Python to
 analyze customer behavior and business trends as well as how to create, run,
 and analyze A/B tests to make proactive, data-driven business decisions.

Learn how to use Python to create, run, and analyze A/B tests to make proactive business decisions.

Customer Analytics and A/B Testing in Python

From online social networks such as Facebook and Twitter to transportation networks such as bike sharing systems, networks are everywhere—and knowing how to analyze them will open up a new world of possibilities for you as a data scientist. This course will equip you with the skills to analyze, visualize, and make sense of networks. You'll apply the concepts you learn to real-world network data using the powerful NetworkX library. With the knowledge gained in this course, you'll develop your network thinking skills and be able to look at your data with a fresh perspective.

This course will equip you with the skills to analyze, visualize, and make sense of networks using the NetworkX library.

Taught by Eric Ma from the Runstadler Lab at MIT, this course will equip you with the skills to analyze and visualize networks such as Facebook and Twitter.

Introduction to Network Analysis in Python

Machine learning is the study and application of algorithms that learn from and make predictions on data. From search results to self-driving cars, it has manifested itself in all areas of our lives and is one of the most exciting and fast growing fields of research in the world of data science. This course teaches the big ideas in machine learning: how to build and evaluate predictive models, how to tune them for optimal performance, how to preprocess data for better results, and much more. The popular <code>caret</code> R package, which provides a consistent interface to all of R's most powerful machine learning facilities, is used throughout the course.

This course teaches the big ideas in machine learning like how to build and evaluate predictive models.

This course teaches the big ideas in machine learning: how to build and evaluate predictive models, how to tune them for performance, and how to preprocess data

Machine Learning with caret in R

Python is now well established as a major platform for data analysis and data science. For many data scientists, the largest limitation of Python is that all data must fit into the resident memory of the available workstation. Further, traditionally, Python has only been able to utilize one CPU. Data scientists constantly ask, "How can I read and process large amounts of data?" and "How can I make use of more computational processing resources?" This course will introduce you to Dask, a flexible parallel computing library for analytic computing. With Dask, you will be able to take the Python workflows you currently have and easily scale them up to large datasets on your workstation without the need to migrate to a distributed computing environment.


Learn how to take the Python workflows you currently have and easily scale them up to large datasets without the need for distributed computing environments.

Parallel Computing with Dask

Simulations are a class of computational algorithms that use the relatively simple idea of random sampling to solve increasingly complex problems. Although they have been around for ages, they have gained in popularity recently due to the rise in computational power and have seen applications in multiple domains including Artificial Intelligence, Physics, Computational Biology and Finance just to name a few. Students will use simulations to generate and analyze data over different probability distributions using the important NumPy package. This course will give students hands-on experience with simulations using simple, real-world applications.

Learn to solve increasingly complex problems using simulations to generate and analyze data.

Statistical Simulation in Python

Mastery requires practice. Having completed Statistical Thinking I and II, you developed your probabilistic mindset and the hacker stats skills to extract actionable insights from your data. Your foundation is in place, and now it is time practice your craft.

In this course, you will apply your statistical thinking skills, exploratory data analysis, parameter estimation, and hypothesis testing, to two new real-world data sets. First, you will explore data from the 2013 and 2015 FINA World Aquatics Championships, where you will quantify the relative speeds and variability among swimmers. You will then perform a statistical analysis to assess the "current controversy" of the 2013 Worlds in which swimmers claimed that a slight current in the pool was affecting result. Second, you will study the frequency and magnitudes of earthquakes around the world. Finally, you will analyze the changes in seismicity in the US state of Oklahoma after the practice of high pressure waste water injection at oil extraction sites became commonplace in the last decade. As you work with these data sets, you will take vital steps toward mastery as you cement your existing knowledge and broaden your abilities to use statistics and Python to make sense of your data.

Take vital steps towards mastery as you apply your statistical thinking skills to real-world data sets and extract actionable insights from them.

Case Studies in Statistical Thinking

Have you taken DataCamp's Introduction to Network Analysis in Python course and are yearning to learn more sophisticated techniques to analyze your networks, whether they be social, transportation, or biological? Then this is the course for you! Herein, you'll build on your knowledge and skills to tackle more advanced problems in network analytics! You'll gain the conceptual and practical skills to analyze evolving time series of networks, learn about bipartite graphs, and how to use bipartite graphs in product recommendation systems. You'll also learn about graph projections, why they're so useful in Data Science, and figure out the best ways to store and load graph data from files. You'll consolidate all of this knowledge in a final chapter case study, in which you'll analyze a forum dataset and come out of this course a Pythonista Network Analyst ninja!

Analyze time series graphs, use bipartite graphs, and gain the skills to tackle advanced problems in network analytics.

Learn how you can analyze time series graphs, use bipartite graphs, and gain the skills to tackle advanced problems in network analytics using Python.

Intermediate Network Analysis in Python

This is your chance to dive into the worlds of marketing and business analytics using R. Day by day, there are a multitude of decisions that companies have to face. With the help of statistical models, you're going to be able to support the business decision-making process based on data, not your gut feeling. Let us show you what a great impact statistical modeling can have on the performance of businesses. You're going to learn about and apply strategies to communicate your results and help them make a difference.

In this course you'll learn how to use data science for several common marketing tasks.

Machine Learning for Marketing Analytics in R

In this introduction to R course, you'll master the basics of this widely used open source language&mdash;including vectors, factors, lists, and data frames. With the coding skills you'll gain in this course, you'll be ready to undertake your own data analysis in R. There are millions of R users worldwide, cementing it as a leading programming language in statistics and data science. More and more organizations are using R, so begin your coding journey in one of DataCamp's most popular courses today!

Master the basics of data analysis by manipulating common data structures such as vectors, matrices, and data frames.

DataCamp's Into to R training course teaches you how to use R programming for data science at your own pace with video tutorials & interactive challenges.

Introduction to R Online

Introduction to R

Learning Python is crucial for any aspiring data science practitioner. Learn to visualize real data with Matplotlib's functions and get acquainted with data structures such as the dictionary and the pandas DataFrame. After covering key concepts such as boolean logic, control flow, and loops in Python, you'll be ready to blend together everything you've learned to solve a case study using hacker statistics.

Level up your data science skills by creating visualizations using Matplotlib and manipulating DataFrames with pandas.

In this intermediate Python course, learn to visualize real data with Matplotlib's functions and get to know new data structures such as the pandas DataFrame.

Intermediate Python

In this course, you'll learn the basics of using SQL with Python. This will be useful because databases are ubiquitous and data scientists, analysts, and engineers must interact with them constantly. The Python SQL toolkit SQLAlchemy provides an accessible and intuitive way to query, build, and write to essential databases, including SQLite, MySQL, and PostgreSQL.

In this course, you'll learn the basics of relational databases and how to interact with them.

Discover the Python SQL toolkit, SQLAlchemy, which provides an accessible way to query, build, and write to databases, like SQLite, MySQL, and PostgreSQL.

Introduction to Databases in Python

It's time to push forward and develop your Python chops even further. There are tons of fantastic functions in Python and its library ecosystem. However, as a data scientist, you'll constantly need to write your own functions to solve problems that are dictated by your data. You will learn the art of function writing in this first Python Data Science Toolbox course. You'll come out of this course being able to write your very own custom functions, complete with multiple parameters and multiple return values, along with default arguments and variable-length arguments. You'll gain insight into scoping in Python and be able to write lambda functions and handle errors in your function writing practice. And you'll wrap up each chapter by using your new skills to write functions that analyze Twitter DataFrames.

Learn the art of writing your own functions in Python, as well as key concepts like scoping and error handling.

Python Data Science Toolbox (Part 1)

Introduction Statistical Modeling in R is a multi-part course designed to get you up to speed with the most important and powerful methodologies in statistical modeling in R.In In this introduction, we'll take a look at what statistical modeling is and what it's used for, R tools for model building, using models for prediction (and using prediction to test models), and how to account for the combined influences of multiple variables.This course has been written from scratch, specifically for DataCamp users. As you'll see, by using computing and concepts from machine learning, we'll be able to leapfrog many of the marginal and esoteric topics encountered in traditional 'regression' courses. <a href="https://www.datacamp.com/courses/statistical-modeling-in-r-part-2">The intermediate course</a> will get you up to speed with the most important and powerful methodologies in statistics.

This course is designed to get you up to speed with the most important and powerful methodologies in statistics.

Learn about the essentials of statistical modeling in R, by exploring data with models, assessing prediction performance and the notion of covariates.

Introduction to Statistical Modeling in R

Where should you buy a house to get the most value for your money? Your first step might be to make a map, but spatial analysis in R can be intimidating because of the complicated objects the data often live in.This course will introduce you to spatial data by starting with objects you already know about, data frames, before introducing you to the special objects from the sp and raster packages used to represent spatial data for analysis in R. You'll learn to read, explore, and manipulate these objects with the big payoff of being able to use the tmap package to make maps.By the end of the course you will have made maps of property sales in a small town, populations of the countries of the world, the distribution of people in the North East of the USA, and median income in the neighborhoods of New York City.

Learn to read, explore, and manipulate spatial data then use your skills to create informative maps using R.

Visualizing Geospatial Data in R

Machine learning is the field that teaches machines and computers to learn from existing data to make predictions on new data: Will a tumor be benign or malignant? Which of your customers will take their business elsewhere? Is a particular email spam? In this course, you'll learn how to use Python to perform supervised learning, an essential component of machine learning. You'll learn how to build predictive models, tune their parameters, and determine how well they will perform with unseen data—all while using real world datasets. You'll be using scikit-learn, one of the most popular and user-friendly machine learning libraries for Python.

Learn how to build and tune predictive models and evaluate how well they'll perform on unseen data.

Supervised Learning with scikit-learn

Data science isn't just for predicting ad-clicks-it's also useful for social impact! This course is a case study from a machine learning competition on DrivenData. You'll explore a problem related to school district budgeting. By building a model to automatically classify items in a school's budget, it makes it easier and faster for schools to compare their spending with other schools. In this course, you'll begin by building a baseline model that is a simple, first-pass approach. In particular, you'll do some natural language processing to prepare the budgets for modeling. Next, you'll have the opportunity to try your own techniques and see how they compare to participants from the competition. Finally, you'll see how the winner was able to combine a number of expert techniques to build the most accurate model.

Learn how to build a model to automatically classify items in a school budget.

This course is a case study from a machine learning competition on DrivenData. Learn how to build a model to automatically classify items in a school budget.

Case Study: School Budgeting with Machine Learning in Python

Previously, you learned the fundamentals of both statistical inference and linear models; now, the next step is to put them together.  This course gives you a chance to think about how different samples can produce different linear models, where your goal is to understand the underlying population model. From the estimated linear model, you will learn how to create interval estimates for the effect size as well as how to determine if the effect is significant. Prediction intervals for the response variable will be contrasted with estimates of the average response. Throughout the course, you'll gain more practice with the dplyr and ggplot2 packages, and you will learn about the broom package for tidying models; all three packages are invaluable in data science.

In this course you'll learn how to perform inference using linear models.

Inference for Linear Regression in R

In this course, you'll learn natural language processing (NLP) basics, such as how to identify and separate words, how to extract topics in a text, and how to build your own fake news classifier. You'll also learn how to use basic libraries such as NLTK, alongside libraries which utilize deep learning to solve common NLP problems. This course will give you the foundation to process and parse text as you move forward in your Python learning.

Learn fundamental natural language processing techniques using Python and how to apply them to extract insights from real-world text data.

Learn fundamental natural language processing (NLP) techniques using Python and how to apply them to extract insights from real-world text data in Python.

Introduction to Natural Language Processing in Python

From stock prices to climate data, time series data are found in a wide variety of domains, and being able to effectively work with such data is an increasingly important skill for data scientists. This course will introduce you to time series analysis in Python. After learning about what a time series is, you'll learn about several time series models ranging from autoregressive and moving average models to cointegration models. Along the way, you'll learn how to estimate, forecast, and simulate these models using statistical libraries in Python. You'll see numerous examples of how these models are used, with a particular emphasis on applications in finance.

In this course you'll learn the basics of analyzing time series data.

Learn the basics of analyzing time series data using several time series models including autoregressive, moving average, and cointegration in Python.

Time Series Analysis in Python

In this course, you'll learn how to use Spark from Python! Spark is a tool for doing parallel computation with large datasets and it integrates well with Python. PySpark is the Python package that makes the magic happen. You'll use this package to work with data about flights from Portland and Seattle. You'll learn to wrangle this data and build a whole machine learning pipeline to predict whether or not flights will be delayed. Get ready to put some Spark in your Python code and dive into the world of high-performance machine learning!

Learn to implement distributed data management and machine learning in Spark using the PySpark package.

Learn to wrangle data and build a machine learning pipeline to make predictions with  PySpark Python package. Practice your skills with real-world data.

Introduction to PySpark

They say that a picture is worth a thousand words. Indeed, successfully promoting your data analysis is not only a matter of accurate and effective graphics, but also of aesthetics and uniqueness. This course teaches you how to leverage the power of ggplot2 themes for producing publication-quality graphics that stick out from the mass of boilerplate plots out there. It shows you how to tweak and get the most out of ggplot2 in order to produce unconventional plots that draw attention on social media. In the end, you will combine that knowledge to produce a slick and custom-styled report with RMarkdown and CSS – all of that within the powerful tidyverse.

Leverage the power of tidyverse tools to create publication-quality graphics and custom-styled reports that communicate your results.

Communicating with Data in R (Tidyverse)

Communicating with Data in the Tidyverse

Accurately predicting demand for products allows a company to stay ahead of the market. By knowing what things shape demand, you can drive behaviors around your products better. This course unlocks the process of predicting product demand through the use of R. You will learn how to identify important drivers of demand, look at seasonal effects, and predict demand for a hierarchy of products from a real world example. By the end of the course you will be able to predict demand for multiple products across a region of a state in the US. Then you will roll up these predictions across many different regions of the same state to form a complete hierarchical forecasting system.

Learn how to identify important drivers of demand, look at seasonal effects, and predict demand for a hierarchy of products from a real world example. 

Forecasting Product Demand in R

Time series data is ubiquitous. Whether it be stock market fluctuations, sensor data recording climate change, or activity in the brain, any signal that changes over time can be described as a time series. Machine learning has emerged as a powerful method for leveraging complexity in data in order to generate predictions and insights into the problem one is trying to solve. This course is an intersection between these two worlds of machine learning and time series data, and covers feature engineering, spectograms, and other advanced techniques in order to classify heartbeat sounds and predict stock prices.

This course focuses on feature engineering and machine learning for time series data. 

Machine Learning for Time Series Data in Python

In this course, you will learn how to build a logistic regression model with meaningful variables. You will also learn how to use this model to make predictions and how to present it and its performance to business stakeholders.

In this course you'll learn to use and present logistic regression models for making predictions.

Learn how to build a logistic regression model in Python with meaningful variables and how to use this model to make predictions for business stakeholders.

Introduction to Predictive Analytics in Python

You've taken a survey (or 1000) before, right? Have you ever wondered what goes into designing a survey and how survey responses are turned into actionable insights? Of course you have! In Analyzing Survey Data in R, you will work with surveys from A to Z, starting with common survey design structures, such as clustering and stratification, and will continue through to visualizing and analyzing survey results. You will model survey data from the National Health and Nutrition Examination Survey using R's survey and tidyverse packages. Following the course, you will be able to successfully interpret survey results and finally find the answers to life's burning questions!

Learn survey design using common design structures followed by visualizing and analyzing survey results.

Analyzing Survey Data in R

In this course, you will learn the foundations of A/B testing, including hypothesis testing, experimental design, and confounding variables. You will also be exposed to a couple more advanced topics, sequential analysis and multivariate testing. The first dataset will be a generated example of a cat adoption website. You will investigate if changing the homepage image affects conversion rates (the percentage of people who click a specific button). For the remainder of the course you will use another generated dataset of a hypothetical data visualization website.

Learn A/B testing: including hypothesis testing, experimental design, and confounding variables.

A/B Testing in R

Now that you have learned the foundations of pandas, this course will give you the chance to apply that knowledge by answering interesting questions about a real dataset! You will explore the Stanford Open Policing Project dataset and analyze the impact of gender on police behavior. During the course, you will gain more practice cleaning messy data, creating visualizations, combining and reshaping datasets, and manipulating time series data. Analyzing Police Activity with pandas will give you valuable experience analyzing a dataset from start to finish, preparing you for your data science career!

Explore the Stanford Open Policing Project dataset and analyze the impact of gender on police behavior using pandas.

Analyzing Police Activity with pandas

Do you want to make beautiful, informative visualizations with ease? If so, then you must learn seaborn! Seaborn is a visualization library that is an essential part of the python data science toolkit. In this course, you will learn how to use seaborn's sophisticated visualization tools to analyze multiple real world datasets including the American Housing Survey, college tuition data, and guests from the popular television series, The Daily Show. Following this course, you will be able to use seaborn functions to visualize your data in several different formats and customize seaborn plots for your unique needs.

Use Seaborn's sophisticated visualization tools to make beautiful, informative visualizations with ease.

Learn how you can use Seaborn functions to visualize your data in several different formats and customize Seaborn plots for your unique needs in Python.

Intermediate Data Visualization with Seaborn

Deep learning methods use data to train neural network algorithms to do a variety of machine learning tasks, such as classification of different classes of objects. Convolutional neural networks are deep learning algorithms that are particularly powerful for analysis of images. This course will teach you how to construct, train and evaluate convolutional neural networks. You will also learn how to improve their ability to learn from data, and how to interpret the results of the training.

Learn powerful techniques for image analysis in Python using deep learning and convolutional neural networks in Keras.

Learn to construct, train and evaluate convolutional neural networks and improve their ability to learn from data to conduct image analysis using Keras.

Image Processing with Keras in Python

Linear algebra is one of the most important set of tools in applied mathematics and data science. In this course, you’ll learn how to work with vectors and matrices, solve matrix-vector equations, perform eigenvalue/eigenvector analyses and use principal component analysis to do dimension reduction on real-world datasets. All analyses will be performed in R, one of the world’s most-popular programming languages.

This course is an introduction to linear algebra, one of the most important mathematical topics underpinning data science.


This data science course is an introduction to linear algebra. Learn how to use matrix-vector equations, perform eigenvalue/eigenvector analyses and pca.



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