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

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)

In this second Python Data Science Toolbox course, you'll continue to build your Python data science skills. First, you'll learn about iterators, objects you have already encountered in the context of for loops. You'll then learn about list comprehensions, which are extremely handy tools for all data scientists working in Python. You'll end the course by working through a case study in which you'll apply all the techniques you learned in both parts of this course.

Continue to build your modern Data Science skills by learning about iterators and list comprehensions.

Python Data Science Toolbox (Part 2)

As a data scientist, you will need to clean data, wrangle and munge it, visualize it, build predictive models, and interpret these models. Before you can do so, however, you will need to know how to get data into Python. In this course, you'll learn the many ways to import data into Python: from flat files such as .txt and .csv; from files native to other software such as Excel spreadsheets, Stata, SAS, and MATLAB files; and from relational databases such as SQLite and PostgreSQL.

Learn to import data into Python from various sources, such as Excel, SQL, SAS and right from the web.

You'll learn the many ways to read data into Python: from flat files such as CSVs to Excel spreadsheets and relational databases in SQLite & PostgreSQL.

Introduction to Importing Data in Python

This is an introduction to the programming language R, focused on a powerful set of tools known as the Tidyverse. You'll learn the intertwined processes of data manipulation and visualization using the tools dplyr and ggplot2. You'll learn to manipulate data by filtering, sorting, and summarizing a real dataset of historical country data in order to answer exploratory questions. You'll then learn to turn this processed data into informative line plots, bar plots, histograms, and more with the ggplot2 package. You’ll get a taste of the value of exploratory data analysis and the power of Tidyverse tools. This is a suitable introduction for those who have no previous experience in R and are interested in performing data analysis.

Get started on the path to exploring and visualizing your own data with the tidyverse, a powerful and popular collection of data science tools within R.

Discover the fundamentals of the Tidyverse, and learn all about renaming and reordering variables, while becoming familiar with binomial distribution.

Introduction to the Tidyverse

As a data scientist, you will need to clean data, wrangle and munge it, visualize it, build predictive models and interpret these models. Before you can do so, however, you will need to know how to get data into Python. In the prequel to this course, you learned many ways to import data into Python: from flat files such as .txt and .csv; from files native to other software such as Excel spreadsheets, Stata, SAS, and MATLAB files; and from relational databases such as SQLite and PostgreSQL. In this course, you'll extend this knowledge base by learning to import data from the web and by pulling data from Application Programming Interfaces— APIs—such as the Twitter streaming API, which allows us to stream real-time tweets.

Improve your Python data importing skills and learn to work with web and API data.

Learn how to import data into Python from sources like the web and by pulling data from APIs, such as the Twitter streaming API to stream real-time tweets.

Intermediate Importing Data in Python

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

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

A vital component of data science involves acquiring raw data and getting it into a form ready for analysis. It is commonly said that data scientists spend 80% of their time cleaning and manipulating data, and only 20% of their time actually analyzing it. This course will equip you with all the skills you need to clean your data in Python, from learning how to diagnose problems in your data, to dealing with missing values and outliers. At the end of the course, you'll apply all of the techniques you've learned to a case study to clean a real-world Gapminder dataset.

This course will equip you with all the skills you need to clean your data in Python.

Data scientists spend 80% of their time cleaning and manipulating data. This course will equip you with all the skills you need to clean your data in Python.

Cleaning Data in Python

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

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

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)

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

As a Data Scientist, the majority of your time should be spent gleaning actionable insights from data -- not waiting for your code to finish running. Writing efficient Python code can help reduce runtime and save computational resources, ultimately freeing you up to do the things you love as a Data Scientist. In this course, you'll learn how to use Python's built-in data structures, functions, and modules to write cleaner, faster, and more efficient code. We'll explore how to time and profile code in order to find bottlenecks. Then, you'll practice eliminating these bottlenecks, and other bad design patterns, using Python's Standard Library, NumPy, and pandas. After completing this course, you'll have the necessary tools to start writing efficient Python code!

Learn to write efficient code that executes quickly and allocates resources skillfully to avoid unnecessary overhead.

Writing Efficient Python Code

Now that you’ve learned the basic tools of SQL you are ready to synthesize them into practical, real-world skills. In this course, you will work with a database of a fictional movie rental company. The size and complexity of this database will allow you to experience the challenges of working with databases firsthand. Throughout this course, you will use SQL to answer business-driven questions. You will learn new skills that will empower you to find the tables you need. You will then learn how to store and manage this data in tables and views that you create. Best of all you will also learn how to write code that not only clearly conveys your intent but is also legible.

Find tables, store and manage new tables and views, and write maintainable SQL code to answer business questions.

Applying SQL to Real-World Problems

A good database design is crucial for a high-performance application. Just like you wouldn't start building a house without the benefit of a blueprint, you need to think about how your data will be stored beforehand. Taking the time to design a database saves time and frustration later on, and a well-designed database ensures ease of access and retrieval of information. While choosing a design, a lot of considerations have to be accounted for. In this course, you'll learn how to process, store, and organize data in an efficient way. You'll see how to structure data through normalization and present your data with views. Finally, you'll learn how to manage your database and all of this will be done on a variety of datasets from book sales, car rentals, to music reviews.

Learn to design databases using star and snoflake schemas, and OLTP and OLAP. You will gain essential skills for creating and managing databases in SQL.

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