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

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

Intermediate R is the next stop on your journey in mastering the R programming language. In this R training, you will learn about conditional statements, loops, and functions to power your own R scripts. Next, make your R code more efficient and readable using the apply functions. Finally, the utilities chapter gets you up to speed with regular expressions in R, data structure manipulations, and times and dates. This course will allow you to take the next step in advancing your overall knowledge and capabilities while programming in R.

Continue your journey to becoming an R ninja by learning about conditional statements, loops, and vector functions.

Discover conditional statements, loops, and functions to power your own R scripts, and learn to make your R code more efficient using the apply functions.

Intermediate R

In this interactive tutorial, you will learn how to perform sophisticated dplyr techniques to carry out your data manipulation with R. First you will master the five verbs of R data manipulation with dplyr: select, mutate, filter, arrange and summarise. Next, you will learn how you can chain your dplyr operations using the pipe operator of the magrittr package. In the final section, the focus is on practicing how to subset your data using the group_by function, and how you can access data stored outside of R in a database. All said and done, you will be familiar with data manipulation tools and techniques that will allow you to efficiently manipulate data.

Master techniques for data manipulation using the select, mutate, filter, arrange, and summarise functions in dplyr.

Learn how to manipulate data in R with the easy and  intuitive Dplyr syntax--from summarizing to subsetting data.--from our leading course taught by RStudio. 

Manipulate Data in R

Data Manipulation in R with dplyr

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

Importing data into R should be the easiest step in your analysis. Unfortunately, that is almost never the case. Data can come in many formats, ranging from .csv and text files, to statistical software files, to databases and HTML data. Knowing which approach to use is key to getting started with the actual analysis.
In this course, you’ll start by learning how to read .csv and text files in R. You will then cover the readr and data.table packages to easily and efficiently import flat file data. After that, you will learn how to read .xls files in R using readxl and gdata.

In this course, you will learn to read CSV, XLS, and text files in R using tools like readxl and data.table.

Learn to read .xls, .csv, and text files in R using readxl and gdata, before learning how to use readr and data.table packages to import flat file data.

Introduction to Importing Data in R

Learn how to write a data report quickly and effectively with the R Markdown package, and share your results with your friends, colleagues or the rest of the world.  Learn how you can author your own R Markdown reports, and how to automate the reporting process so that you have your own reproducible reports. By the end of the interactive data analysis reporting tutorial, you will be able to generate reports straight from your R code, documenting your work — and its results — as an HTML, pdf, slideshow or Microsoft Word document.

Learn to create interactive analyses and automated reports with R Markdown.

Learn how to generate & write reports from your R code with the R Markdown package through this tutorial taught by RStudio. Learn at your own pace today!

Reporting with R Markdown - Online Tutorial

Reporting with R Markdown

It's commonly said that data scientists spend 80% of their time cleaning and manipulating data and only 20% of their time actually analyzing it. For this reason, it is critical to become familiar with the data cleaning process and all of the tools available to you along the way. This course provides a very basic introduction to cleaning data in R using the tidyr, dplyr, and stringr packages. After taking the course you'll be able to go from raw data to awesome insights as quickly and painlessly as possible!

Learn to explore your data so you can properly clean and prepare it for analysis.

DataCamp's course teaches you to clean data in R so you can turn raw data into valuable insights quicker. See why over 800,000 data scientists use DataCamp!

Cleaning Data In R

Cleaning Data in R

When your dataset is represented as a table or a database, it's difficult to observe much about it beyond its size and the types of variables it contains. In this course, you'll learn how to use graphical and numerical techniques to begin uncovering the structure of your data. Which variables suggest interesting relationships? Which observations are unusual? By the end of the course, you'll be able to answer these questions and more, while generating graphics that are both insightful and beautiful.

Learn how to use graphical and numerical techniques to begin uncovering the structure of your data.

Learn how to use graphical and numerical techniques for exploratory data analysis while generating insightful and beautiful graphics in R.

Exploratory Data Analysis in R

This follow-up course on Intermediate R does not cover new programming concepts. Instead, you will strengthen your knowledge of the topics in Intermediate R with a bunch of new and fun exercises.

Strengthen your knowledge of the topics you learned in Intermediate R with a ton of new and fun exercises.

Practice the skills you learned in DataCamp's Intermediate R course See why over 700,000 people use DataCamp today!

Intermediate R: Practice

Ultimately, data analysis is about understanding relationships among variables. Exploring data with multiple variables requires new, more complex tools, but enables a richer set of comparisons. In this course, you will learn how to describe relationships between two numerical quantities. You will characterize these relationships graphically, in the form of summary statistics, and through simple linear regression models.

Learn how to describe relationships between two numerical quantities and characterize these relationships graphically.

Learn how to describe relationships between two numerical quantities and characterize these relationships graphically through simple linear regression models.

Correlation and Regression in R

This course provides a comprehensive introduction on how to plot data with R’s default graphics system, base graphics.After an introduction to base graphics, we look at a number of R plotting examples, from simple graphs such as scatterplots to plotting correlation matrices. The course finishes with exercises in plot customization. This includes using R plot colors effectively and creating and saving complex plots in R.Base Graphics Background R supports four different graphics systems: base graphics, grid graphics, lattice graphics, and ggplot2. Base graphics is the default graphics system in R, the easiest of the four systems to learn to use, and provides a wide variety of useful tools, especially for exploratory graphics where we wish to learn what is in an unfamiliar dataset.

This course provides a comprehensive introduction to working with base graphics in R.

Data Visualization in R

In this course, you will take a deeper dive into the wide range of data formats out there. More specifically, you'll learn how to import data from relational databases and how to import and work with data coming from the web. Finally, you'll get hands-on experience with importing data from statistical software packages such as SAS, STATA, and SPSS.

Parse data in any format. Whether it's flat files, statistical software, databases, or data right from the web.

Learn how you can use R to parse data in any format, whether that data is from flat files, statistical software, databases, or data right from the web.

Intermediate Importing Data in R

Running exciting analyses on interesting datasets is the dream of every data scientist. But first, some importing and cleaning must be done. In this series of four case studies, you'll revisit key concepts from our courses on importing and cleaning data in R.

In this series of four case studies, you'll revisit key concepts from our courses on importing and cleaning data in R.

Practice importing and cleaning data in R with this case study focused tutorial.  You will improve your R skills quickly and efficiently with this course!

Importing & Cleaning Data in R: Case Studies

Scientists seek to answer questions using rigorous methods and careful observations. These observations—collected from the likes of field notes, surveys, and experiments—form the backbone of a statistical investigation and are called data. Statistics is the study of how best to collect, analyze, and draw conclusions from data. It is helpful to put statistics in the context of a general process of investigation: 1) identify a question or problem; 2) collect relevant data on the topic; 3) analyze the data; and 4) form a conclusion. In this course, you'll focus on the first two steps of the process.

Learn the language of data, study types,  sampling strategies, and experimental design.

Learn how you can identify a question, collect relevant data on the topic, analyze the data and then form a solid conclusion when working with Data in R.

Introduction to Data in R

Learn how to parse data in any format. Whether it's flat files, statistical software, databases, or web data, you'll learn to handle it all.

Learn how to parse data from flat files, statistical software, databases, websites, and more.

with R

Importing & Cleaning Data 

Learn about fundamental data types, logic, and how to create your own functions using the R language.

R Programming

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

Time series are all around us, from server logs to high frequency financial data. Managing and manipulating ordered observations is central to all time series analysis. The xts and zoo packages provide a set of powerful tools to make this task fast and mistake free. In this course, you will learn everything from the basics of xts to advanced tips and tricks for working with time series data in R.

The xts and zoo packages make the task of managing and manipulating ordered observations fast and mistake free.

You will learn everything from the basics of working with the xts and zoo packages to advanced tips and tricks for working with time series data in R. 

Manipulating Time Series Data with xts and zoo in R

This course will cover the basics on financial trading and will give you an overview of how to use quantstrat to build signal-based trading strategies in R. It will teach you how to set up a quantstrat strategy, apply transformations of market data called indicators, create signals based on the interactions of those indicators, and even simulate orders. Lastly, it will explain how to analyze your results both from statistical and visual perspectives.

This course covers the basics of financial trading and how to use quantstrat to build signal-based trading strategies.

This course covers the basics of financial trading and how to use quantstrat to build signal-based trading strategies in R and even simulate orders.

Financial Trading in R

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)

One of the foundational aspects of statistical analysis is inference, or the process of drawing conclusions about a larger population from a sample of data. Although counter intuitive, the standard practice is to attempt to disprove a research claim that is not of interest. For example, to show that one medical treatment is better than another, we can assume that the two treatments lead to equal survival rates only to then be disproved by the data. Additionally, we introduce the idea of a p-value, or the degree of disagreement between the data and the hypothesis. We also dive into confidence intervals, which measure the magnitude of the effect of interest (e.g. how much better one treatment is than another).

Learn how to draw conclusions about a population from a sample of data via a process known as statistical inference.

Learn to use statistical inference techniques, including p-value and confidence intervals, to draw conclusions about a population from a sample of data.

Foundations of Inference in R

Foundations of Inference

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

The role of a data scientist is to turn raw data into actionable insights. Much of the world's raw data—from electronic medical records to customer transaction histories—lives in organized collections of tables called relational databases. To be an effective data scientist, you must know how to wrangle and extract data from these databases using a language called SQL . This course teaches syntax in SQL shared by many types of databases, such as PostgreSQL, MySQL, SQL Server, and Oracle. This course teaches you everything you need to know to begin working with databases today!

Master the basics of querying tables in relational databases such as MySQL, SQL Server, and PostgreSQL.

Learn the basics of syntax in SQL, how to extract and wrangle data, and use querying tables in relational databases like MySQL, SQL Server, and PostgreSQL.

Introduction to SQL

Character strings can turn up in all stages of a data science project. You might have to clean messy string input before analysis, extract data that is embedded in text or automatically turn numeric results into a sentence to include in a report. Perhaps the strings themselves are the data of interest, and you need to detect and match patterns within them. This course will help you master these tasks by teaching you how to pull strings apart, put them back together and use stringr to detect, extract, match and split strings using regular expressions, a powerful way to express patterns.

Learn how to pull character strings apart, put them back together and use the stringr package.

In this course you will learn how to pull character strings apart, put them back together and use the stringr package.

String Manipulation with stringr in R

In this course you'll take your skills with simple linear regression to the next level. By learning multiple and logistic regression techniques you will gain the skills to model and predict both numeric and categorical outcomes using multiple input variables. You'll also learn how to fit, visualize, and interpret these models. Then you'll apply your skills to learn about Italian restaurants in New York City!

In this course you'll learn to add multiple variables to linear models and to use logistic regression for classification.

Multiple and Logistic Regression in R

If you want to apply your new 'Python for Data Science' skills to real-world financial data, then this course will give you some very valuable tools.
First, you will learn how to get data out of Excel into pandas and back. Then, you will learn how to pull stock prices from various online APIs like
Google or Yahoo! Finance, macro data from the Federal Reserve, and exchange rates from OANDA. Finally, you will learn how to calculate returns for various time horizons,
analyze stock performance by sector for IPOs, and calculate and summarize correlations.


In this course, you'll learn how to import and manage financial data in Python using various tools and sources.

Learn how to pull stock prices from online APIs to calculate returns for time horizons, analyze stock performance by sector, and summarize correlations.

Importing and Managing Financial Data 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

Version control is one of the power tools of programming. It allows you to keep track of what you did when, undo any changes you decide you don't want, and collaborate at scale with other people. This course will introduce you to Git, a modern version control tool that is very popular with data scientists and software developers, and show you how to use it to get more done in less time and with less pain.

This course is an introduction to version control with Git for data scientists. 

Discover Git, a popular and modern version control tool for data scientists, and how you can use it work more efficiently, quickly, and with fewer errors.

Introduction to Git

Understanding the basic principles of finance is essential for making important financial decisions ranging from taking out a student loan to constructing an investment portfolio. Combining basic financial knowledge with Python will allow you to construct some very powerful tools. You'll come out of this course understanding the time value of money, how to compare potential projects and how to make rational, data-driven financial decisions.

Using Python and NumPy, learn the most fundamental financial concepts. 

Learn about the time value of money, how to compare potential projects, and how to make rational, data-driven, financial decisions with Python and Numpy.

Introduction to Financial Concepts in Python

The world is full of unobservable variables that can't be directly measured. You might be interested in a construct such as math ability, personality traits, or workplace climate. When investigating constructs like these, it's critically important to have a model that matches your theories and data. This course will help you understand dimensionality and show you how to conduct exploratory and confirmatory factor analyses. With these statistical techniques in your toolkit, you'll be able to develop, refine, and share your measures. These analyses are foundational for diverse fields including psychology, education, political science, economics, and linguistics.

Explore latent variables, such as personality using exploratory and confirmatory factor analyses.

Factor Analysis in R

Twitter produces hundreds of million messages per day, with people around the world discussing sports, politics, business, and entertainment. You can access thousands of messages flowing in this stream in a matter of minutes. In this course, you will learn how to collect Twitter data and analyze tweet text, Twitter networks, and the geographical origin of the tweet. We'll be doing this with datasets on tech companies, data science hashtags, and the 2018 State of the Union address. Using these methods, you will be able to inform business and political decision-making by discovering the prevalence of important topics, the diversity of discussion networks, and a topic's geographical reach.

In this course, you'll learn how to collect Twitter data and analyze Twitter text, networks, and geographical origin.

Analyzing Social Media Data in Python

Monitoring the evolution of traded assets is key in finance. In this course, you will learn how to build a graphical dashboard with spreadsheets to track the performance of financial securities. You will focus on historical prices and dividends of the hypothetical stock ABC. You will learn how to visualize its prices, how to measure essential reward and risk indicators, and see if your investment in ABC outperformed a benchmark index. At the end of the course, you should be able to use spreadsheets to build great monitoring tools used by traders and financial analysts in their day-to-day business life!

Learn how to build a graphical dashboard with spreadsheets to track the performance of financial securities.

Learn how you can build a graphical dashboard with spreadsheets to monitor the performance of financial securities used by traders and financial analysts.

Data Science Courses

Data Science Tracks

Our Learning Experience

@spalthoffdaniel

Workspace Publications (0)

Completed Tracks(2)

Completed courses (16)

Completed Projects (0)