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

Begin your journey into Data Science! Even if you've never written a line of code in your life, you'll be able to follow this course and witness the power of Python to perform Data Science. You'll use data to solve the mystery of Bayes, the kidnapped Golden Retriever, and along the way you'll become familiar with basic Python syntax and popular Data Science modules like Matplotlib (for charts and graphs) and pandas (for tabular data).

Dive into data science using Python and learn how to effectively analyze and visualize your data. No coding experience or skills needed.

Dive into data science using Python and learn how to effectively analyze and visualize your data using Pandas and Matplotlib. No coding experience needed.

Introduction to Data Science in Python

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

pandas is the world's most popular Python library, used for everything from data manipulation to data analysis. In this course, you'll learn how to manipulate DataFrames, as you extract, filter, and transform real-world datasets for analysis. Using pandas you’ll explore all the core data science concepts. Using real-world data, including Walmart sales figures and global temperature time series, you’ll learn how to import, clean, calculate statistics, and create visualizations—using pandas to add to the power of Python!

Use the world’s most popular Python data science package to manipulate data and calculate summary statistics.

Data Manipulation with pandas

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)

Being able to combine and work with multiple datasets is an essential skill for any aspiring Data Scientist. pandas is a crucial cornerstone of the Python data science ecosystem, with Stack Overflow recording 5 million views for pandas questions. Learn to handle multiple DataFrames by combining, organizing, joining, and reshaping them using pandas. You'll work with datasets from the World Bank and the City Of Chicago. You will finish the course with a solid skillset for data-joining in pandas.

Learn to combine data from multiple tables by joining data together using pandas.

Learn to handle multiple DataFrames by combining, organizing, joining, and reshaping them using Pandas. You'll gain a solid skillset for data-joining.

Joining Data with pandas

Visualizing data in plots and figures exposes the underlying patterns in the data and provides insights. Good visualizations also help you communicate your data to others, and are useful to data analysts and other consumers of the data. In this course, you will learn how to use Matplotlib, a powerful Python data visualization library. Matplotlib provides the building blocks to create rich visualizations of many different kinds of datasets. You will learn how to create visualizations for different kinds of data and how to customize, automate, and share these visualizations.

Learn how to create, customize, and share data visualizations using Matplotlib.

Introduction to Data Visualization with Matplotlib

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

Seaborn is a powerful Python library that makes it easy to create informative and attractive visualizations. This course provides an introduction to Seaborn and teaches you how to visualize your data using plots such as scatter plots, box plots, and bar plots. You’ll do this while exploring survey responses about student hobbies and the factors that are associated with academic success. You’ll also learn about some of Seaborn’s advantages as a statistical visualization tool, such as how it automatically calculates confidence intervals. By the end of the course, you will be able to use Seaborn in a variety of situations to explore your data and effectively communicate the results of your data analyses to others.

Learn how to create informative and attractive visualizations in Python using the Seaborn library.

Learn how to visualize in scatter plots, box plots, and bar plots, to create informative and attractive visualizations using the Python libary, Seaborn.

Introduction to Data Visualization with Seaborn

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

It's commonly said that data scientists spend 80% of their time cleaning and manipulating data and only 20% of their time analyzing it. The time spent cleaning is vital since analyzing dirty data can lead you to draw inaccurate conclusions.

Data cleaning is an essential task in data science. Without properly cleaned data, the results of any data analysis or machine learning model could be inaccurate. In this course, you will learn how to identify, diagnose, and treat a variety of data cleaning problems in Python, ranging from simple to advanced. You will deal with improper data types, check that your data is in the correct range, handle missing data, perform record linkage, and more!

Learn to diagnose and treat dirty data and develop the skills needed to transform your raw data into accurate insights! 

Cleaning Data in Python

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

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

You'll probably never have a time machine, but how about a machine for analyzing time? As soon as time enters any analysis, things can get weird. It's easy to get tripped up on day and month boundaries, time zones, daylight saving time, and all sorts of other things that can confuse the unprepared. If you're going to do any kind of analysis involving time, you’ll want to use Python to sort it out. Working with data sets on hurricanes and bike trips, we’ll cover counting events, figuring out how much time has elapsed between events and plotting data over time. You'll work in both standard Python and in Pandas, and we'll touch on the dateutil library, the only timezone library endorsed by the official Python documentation. After this course, you'll confidently handle date and time data in any format like a champion.

Learn how to work with dates and times in Python.

Learn how to work with dates and times in Python using Pandas and dateutil library. You'll also have chance to practice with real-world data as you learn.

Working With Dates and Times in Python

Working with Dates and Times in Python

Have you got your basic Python programming chops down for Data Science but are  yearning for more? Then this is the course for you. Herein, you'll consolidate and practice your knowledge of lists, dictionaries, tuples, sets, and date times. You'll see their relevance in working with lots of real data and how to leverage several of them in concert to solve multistep problems, including an extended case  study using Chicago metropolitan area transit data. You'll also learn how to use many of the objects in the Python Collections module, which will allow you to store and manipulate your data for a variety of Data Scientific purposes. After taking this course, you'll be ready to tackle many Data Science challenges Pythonically.

Consolidate and extend your knowledge of Python data types such as lists, dictionaries, and tuples, leveraging them to solve Data Science problems.

Data Types for Data Science in Python

Before you can analyze data, you first have to acquire it. This course teaches you how to build pipelines to import data kept in common storage formats. You’ll use pandas, a major Python library for analytics, to get data from a variety of sources, from spreadsheets of survey responses, to a database of public service requests, to an API for a popular review site. Along the way, you’ll learn how to fine-tune imports to get only what you need and to address issues like incorrect data types. Finally, you’ll assemble a custom dataset from a mix of sources.

Learn to acquire data from common file formats and systems such as CSV files, spreadsheets, JSON, SQL databases, and APIs.

Streamlined Data Ingestion with pandas

The ability to build tools capable of retrieving and parsing information stored across the internet has been and continues to be valuable in many veins of data science. In this course, you will learn to navigate and parse html code, and build tools to crawl websites automatically. Although our scraping will be conducted using the versatile Python library scrapy, many of the techniques you learn in this course can be applied to other popular Python libraries as well, including BeautifulSoup and Selenium. Upon the completion of this course, you will have a strong mental model of html structure, will be able to build tools to parse html code and access desired information, and create a simple scrapy spiders to crawl the web at scale.

Learn to retrieve and parse information from the internet using the Python library scrapy.

Web Scraping with Python

Web Scraping in Python

Introduction to R (edX 1.1)

Introduction to R (edX 1.2)

Lab exercises on Create and Name Vectors.

Introduction to R (edX 2.1)

Introduction to R (edX 2.2)

Introduction to R (edX 2.3)

Lab exercises on Create and Name Matrices.

Introduction to R (edX 3.1)

Introduction to R (edX 3.2)

Introduction to R (edX 3.3)

Introduction to R (edX 4.1)

Introduction to R (edX 5.1)

Lab exercises on Subset and Extend Lists.

Introduction to R (edX 5.2)

Introduction to R (edX 6.1)

Lab exercises on Subset, extend and sort your data frame.

Introduction to R (edX 6.2)

Introduction to R (edX 7.1)

Introduction to R (edX 7.2)

Introduction to R (edX 7.3)

Understanding how to prep your data is an essential skill for working in Python. It’s what you have to do before you can reveal the insights that matter. In this track, you’ll learn how to import your data from a variety of sources, including .csv, .xls, text files, and more. After learning how to import your data it’s time to prepare your data for analysis. You’ll work with real-world data such as restaurant reviews as you learn how to handle improper data types, deal with missing data, and perform record linkage. You’ll then learn how you can leverage the Tweepy package to access Twitter’s API to scrape the web for data. Start this track and gain the data prepping skills you need to clean your dirty data.

Gain the real-world data prepping skills you need to reveal the insights that matter! Discover how to import, clean, and work with APIs and web data. 

with Python

Importing & Cleaning Data 

Are you ready to gain the foundational skills you need to become a Python programmer? In this track, you'll learn the Python basics you need to start on your programming journey, including how to clean real-world data ready for analysis, use data visualization libraries, and even how to write your own Python functions.

Your instructor Hugo will introduce you to how companies worldwide use Python to gain a competitive edge. Through hands-on coding exercises you’ll then learn how to store, manipulate, and explore data using NumPy. Then it’s time to level-up as you learn how to visualize your data using Matplotlib, manipulate DataFrames and dictionaries using pandas, and write your own functions and list comprehension. Start this track to add these essential Python skills to your data science toolbox.

Grow your programmer skills. Discover how to manipulate dictionaries and DataFrames, visualize real-world data, and write your own Python functions.

Python Fundamentals

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 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

As a data scientist, you will encounter many situations where you will need to extract key information from huge corpora of text, clean messy data containing strings, or detect and match patterns to find useful words. All of these situations are part of text mining and are an important step before applying machine learning algorithms. This course will take you through understanding compelling concepts about string manipulation and regular expressions. You will learn how to split strings, join them back together, interpolate them, as well as detect, extract, replace, and match strings using regular expressions. On the journey to master these skills, you will work with datasets containing movie reviews or streamed tweets that can be used to determine opinion, as well as with raw text scraped from the web.

Learn about string manipulation and become a master at using regular expressions.

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