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.

Introduction to Python

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.

Introduction to SQL

In  Introduction to R, you will master the basics of this widely used open source language, including factors, lists, and data frames. With the knowledge gained in this course, you will be ready to undertake your first very own data analysis. Oracle estimated over 2 million R users worldwide in 2012, cementing R as a leading programming language in statistics and data science. Every year, the number of R users grows by about 40%, and an increasing number of organizations are using it in their day-to-day activities. Begin your journey to learn R with us 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 Course

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

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.

Introduction to Data Science in 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

What is data science, why is it so popular, and why did the Harvard Business Review hail it as the “sexiest job of the 21st century”? In this non-technical course, you’ll be introduced to everything you were ever too afraid to ask about this fast-growing and exciting field, without needing to write a single line of code. Through hands-on exercises, you’ll learn about the different data scientist roles, foundational topics like A/B testing, time series analysis, and machine learning, and how data scientists extract knowledge and insights from real-world data. So don’t be put off by the buzzwords. Start learning, gain skills in this hugely in-demand field, and discover why data science is for everyone!

An introduction to data science with no coding involved.

Data Science for Everyone

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.

Introduction to the Tidyverse

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.

DataCamp's Intermediate R course shows you how learn statistics with R through interactive video tutorials. See why over 300,000 people use DataCamp today!

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 2019, the average salary for data engineers overtook data scientists. How did this happen? Companies wanting to find the gold within their data realized it wasn’t possible if they hadn’t yet built the mine. Data engineers lay the foundations that make data science possible.
 
In this course, you’ll learn about a data engineer’s core responsibilities, how they differ from data scientists, and facilitate the flow of data through an organization. Through hands-on exercises you’ll follow Spotflix, a fictional music streaming company, to understand how their data engineers collect, clean, and catalog their data. By the end of the course, you’ll understand what your company's data engineers do, be ready to have a conversation with a data engineer, and have a solid foundation to start your own data engineer journey.

Discover how data engineers lay the groundwork that makes data science possible. No coding involved!

Data Engineering for Everyone

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)

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

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.

Joining Data with pandas

What's behind the machine learning hype? In this non-technical course, you’ll learn everything you’ve been too afraid to ask about machine learning. There’s no coding required. Hands-on exercises will help you get past the jargon and learn how this exciting technology powers everything from self-driving cars to your personal Amazon shopping suggestions.

How does machine learning work, when can you use it, and what is the difference between AI and machine learning? They’re all covered. Gain skills in this hugely in-demand and influential field, and discover why machine learning is for everyone!


An introduction to machine learning with no coding involved.

Machine Learning for Everyone

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

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)

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

How do we get from data to answers?  Exploratory data analysis is a process for exploring datasets, answering questions, and visualizing results.  This course presents the tools you need to clean and validate data, to visualize distributions and relationships between variables, and to use regression models to predict and explain.  You'll explore data related to demographics and health, including the National Survey of Family Growth and the General Social Survey.  But the methods you learn apply to all areas of science, engineering, and business. You'll use Pandas, a powerful library for working with data, and other core Python libraries including NumPy and SciPy, StatsModels for regression, and Matplotlib for visualization.  With these tools and skills, you will be prepared to work with real data, make discoveries, and present compelling results.

Learn how to explore, visualize, and extract insights from data. 

Exploratory Data Analysis 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.

Introduction to Importing Data in R

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.

Introduction to Data Visualization with Seaborn

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

Object-oriented programming (OOP) is a widely used programming paradigm that reduces development times—making it easier to read, reuse, and maintain your code. OOP shifts the focus from thinking about code as a sequence of actions to looking at your program as a collection of objects that interact with each other. In this course, you’ll learn how to create classes, which act as the blueprints for every object in Python. You’ll then leverage principles called inheritance and polymorphism to reuse and optimize code. Dive in and learn how to create beautiful code that’s clean and efficient!

Dive in and learn how to create classes and leverage inheritance and polymorphism to reuse and optimize code.

Object-Oriented Programming 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

pandas DataFrames are the most widely used in-memory representation of complex data collections within Python. Whether in finance, a scientific field, or data science, familiarity with pandas is essential. This course teaches you to work with real-world datasets containing both string and numeric data, often structured around time series. You will learn powerful analysis, selection, and visualization techniques in this course.

Learn how to use the industry-standard pandas library to import, build, and manipulate DataFrames.

Pandas Foundations

pandas Foundations

Visualizing data using charts, graphs, and maps is one of the most impactful ways to communicate complex data. In this course, you’ll learn how to choose the best visualization for your dataset, and how to interpret common plot types like histograms, scatter plots, line plots and bar plots. You'll also learn about best practices for using colors and shapes in your plots, and how to avoid common pitfalls. Through hands-on exercises, you'll visually explore over 20 datasets including global life expectancies, Los Angeles home prices, ESPN's 100 most famous athletes, and the greatest hip-hop songs of all time.

An introduction to data visualization with no coding involved.

Data Visualization for Everyone

You've done your analysis, built your report, and trained a model. What's next? Well, if you want to deploy your model into production, your code will need to be more reliable than exploratory scripts in a Jupyter notebook. Writing Functions in Python will give you a strong foundation in writing complex and beautiful functions so that you can contribute research and engineering skills to your team. You'll learn useful tricks, like how to write context managers and decorators. You'll also learn best practices around how to write maintainable reusable functions with good documentation. They say that people who can do good research and write high-quality code are unicorns. Take this course and discover the magic!

Learn to use best practices to write maintainable, reusable, complex functions with good documentation.

Writing Functions 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.

Further improve your Python data importing skills and learn to work with more web and API data.

Intermediate Importing Data 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

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

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.

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.

Working With Dates and Times in Python

Working with Dates and Times in Python

This course builds on what you learned in Data Manipulation in R with dplyr by showing you how to combine data sets with dplyr's two table verbs. In the real world, data comes split across many data sets, but dplyr's core functions are designed to work with single tables of data. In this course, you'll learn the best ways to combine data sets into single tables. You'll learn how to augment columns from one data set with columns from another with mutating joins, how to filter one data set against another with filtering joins, and how to sift through data sets with set operations. Along the way, you'll discover the best practices for building data sets and troubleshooting joins with dplyr. Afterwards, you’ll be well on your way to data manipulation mastery!

This course will show you how to combine data sets with dplyr's two table verbs.

Using dplyr, you'll learn how to augment columns with mutating joins, filter a data set against another with filtering joins, and how to sift through data sets.

Join Data in R

Joining Data in R with dplyr

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.

Learn to use SQLAlchemy with Python to build and write SQLite, MySQL, Postgresql databases, and more.

Introduction to Databases in Python

Data scientists can experience huge benefits by learning concepts from the field of software engineering, allowing them to more easily reutilize their code and share it with collaborators. In this course, you'll learn all about the important ideas of modularity, documentation, & automated testing, and you'll see how they can help you solve Data Science problems quicker and in a way that will make future you happy. You'll even get to use your acquired software engineering chops to write your very own Python package for performing text analytics.

Learn about modularity, documentation, and automated testing to help you solve data science problems more quickly and reliably.

Software Engineering for Data Scientists in Python

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

Intermediate Importing Data in R

If you already know SAS, SPSS or Stata, you don’t need to spend time learning how to analyze data; you need a course that focuses on translating your knowledge into R. This comprehensive course introduces R jargon using the language you're familiar with.

Learn to translate your knowledge of SAS, SPSS, or Stata into R using the same statistics techniques you're familiar with.

Master R quickly with the help of your prior knowledge of SAS, SPSS, and STATA

R for SAS, SPSS and STATA Users

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

Quandl offers millions of free and open financial, economic, and social datasets. In this tutorial you will learn how to effortlessly pull any of Quandl's data into R.

How to work with Quandl in R

This course complements course material from the University of Amsterdam's Basic Statistics at Coursera (www.coursera.org/learn/basic-statistics). The Basic Statistics course is roughly based on the syllabus used by the department of Child Development and Educational Sciences at the University of Amsterdam (produced by A. van Maanen, A.A.J. van Peet, N. Borgers, E. Gebhardt, N. Lagerweij, R. Stoel, G. van de Wittenboer, F.J. Oort, P.J. Hoffenaar, and B.J.H. Zijlstra.)

Basic Statistics

Intro to Python for Data Science (edX 1.1)

Intro to Python for Data Science (edX 1.2)

Intro to Python for Data Science (edX 2.1)

Intro to Python for Data Science (edX 2.2)

Intro to Python for Data Science (edX 2.3)

Intro to Python for Data Science (edX 3.1)

Explore a dataset from Kaggle containing a century's worth of Nobel Laureates. Who won? Who got snubbed?



The Nobel Prize is perhaps the world's most well known scientific award. Every
year it is given to scientists and scholars in chemistry,
literature, physics, medicine, economics, and peace. The first Nobel
Prize was handed out in 1901, and at that time the prize was Eurocentric and
male-focused, but nowadays it's not biased in any way. Surely, right?

Well, let's find out! What characteristics do the prize winners have? Which
country gets it most often? And has anybody gotten it twice? It's up to you to
figure this out.

The [dataset](https://www.kaggle.com/nobelfoundation/nobel-laureates) used in this project is from The Nobel Foundation on Kaggle.


A Visual History of Nobel Prize Winners

Find the true Scala experts by exploring its development history in Git and GitHub.


Open source projects contain entire development histories, such as who made changes, the changes themselves, and code reviews. 
In this project, you'll be challenged to read in, clean
up, and visualize the real-world project repository of 
Scala that spans data from a version control system (Git)
as well as a project hosting site (GitHub). With almost
30,000 commits and a history spanning over ten years, Scala
is a mature language. You will find out who has had the
most influence on its development and who are the experts.

The dataset includes the project history of
[Scala](http://www.scala-lang.org) retrieved from Git and
GitHub as a set of CSV files.


The GitHub History of the Scala Language

Load, clean, and visualize scraped Google Play Store data to gain insights into the Android app market.



Mobile apps are everywhere. They are easy to create and can be lucrative. Because of these two factors, more and more apps are being developed. In this project, you will do a comprehensive analysis of the Android app market by comparing over ten thousand apps in Google Play across different categories. You'll look for insights in the data to devise strategies to drive growth and retention.
The [data](https://www.kaggle.com/lava18/google-play-store-apps) for this project was scraped from the [Google Play](https://play.google.com/store/apps?hl=en) website. While there are many popular datasets for Apple App Store, there aren't many for Google Play apps, which is partially due to the increased difficulty in scraping the latter as compared to the former. The data files are as follows:
-  `apps.csv `: contains all the details of the apps on Google Play. These are the features that describe an app.
- `user_reviews.csv`: contains 100 reviews for each app, [most helpful first](https://www.androidpolice.com/2019/01/21/google-play-stores-redesigned-ratings-and-reviews-section-lets-you-easily-filter-by-star-rating/). The text in each review has been pre-processed, passed through a sentiment analyzer engine and tagged with its sentiment score.


The Android App Market on Google Play

Use data manipulation and visualization to explore one of two different television broadcast datasets: The Super Bowl and hit sitcom The Office!


With Intermediate Python under your belt, you can already analyze and extract meaningful insights from various sources. 
For this set of projects, you will use a combination of data manipulation and visualization to explore television data. 

In this project's guided variant, you will look at Super Bowl Data, generating insights into game outcomes, viewership, and even halftime shows. 
In the unguided variant of this project, you'll develop an informative plot that helps to visualize the viewership and quality of The Office
throughout its nine seasons.


Analyzing TV Data

Reanalyse the data behind one of the most important discoveries of modern medicine: handwashing.


In 1847, the Hungarian physician Ignaz Semmelweis made a breakthough discovery:
he discovers handwashing. Contaminated hands was a major cause of childbed fever
and by enforcing handwashing at his hospital he saved hundreds of lives. 


Dr. Semmelweis and the Discovery of Handwashing

Use R to make art and create imaginary flowers inspired by nature.


R is a tool for doing serious statistics and data analysis. 
But not everything in life can be serious, life is also beautiful, 
and R can make beautiful things too. R can make art. 

The arrangement of leaves on a plant stem is ruled by spirals. 
This fact is called [phyllotaxis](https://en.wikipedia.org/wiki/Phyllotaxis)
and it is a nice example of how mathematics can describe patterns in nature.
In this project, we will *invent* flowers using this fact. 

This R project assumes you have familiarity with the `ggplot2` package. If you want to see more examples of how you can use R to make art, you should check out 
[the Fronkonstin blog](https://fronkonstin.com/) created by Antonio Sánchez Chinchón.


Phyllotaxis: Draw Flowers Using Mathematics

If you have never done a DataCamp project, this is the place to start!  


This is an introduction to DataCamp projects. DataCamp projects allow you to apply 
the skills you have learned in DataCamp courses. In each project, you will carry out 
an end-to-end analysis on real-world tasks using real-world tools and workflows.

In doing so, you will learn how to work with Jupyter notebooks: an open-source web 
application that is great for interactive data analysis.


Introduction to DataCamp Projects

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

In this track, you’ll gain the fundamental skills you need to speak the language of data. There’s no prior knowledge or coding skills required. Through hands-on exercises, you’ll learn how to understand data, whether it’s a bar plot on the news or as a statistic shared during a work meeting. You’ll expand your knowledge of key data topics, including data science, machine learning, data visualization, and even data engineering. You'll also learn about the many roles, technologies, and frameworks in data science. By the end of this track, you’ll have the skills you need to confidently interpret data and incorporate it into your daily life.

Data literacy isn't just for data specialists; it's a fundamental life skill that everyone needs.

Data Literacy Fundamentals

Real-world data is messy. That’s why libraries like pandas are so valuable. Using pandas you can take the pain out of data manipulation by extracting, filtering, and transforming data in DataFrames, clearing a path for quick and reliable data analysis. 

If you want to improve your data wrangling skills this is the track for you. You’ll learn how to prepare real-world data for analysis and grow your expertise as you work with multiple DataFrames using pandas. You’ll also gain hands-on experience of how to combine, merge, and create visualizations. At the end of the track, you'll apply your new-found data manipulation skills to analyze the impact of weather and gender on police behavior. Start this track and discover how pandas can save you time manipulating data. 

Take the pain out of data manipulation using pandas. You’ll learn how to transform, sort, and filter data in DataFrames, ready for quick analysis.

with Python

Data Manipulation 

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)

Now that you've learned the basics of SQL in our <a href="https://learn.datacamp.com/courses/introduction-to-sql">Introduction to SQL</a> course, it's time to supercharge your queries using joins and relational set theory. In this course, you'll learn all about the power of joining tables while exploring interesting features of countries and their cities throughout the world. You will master inner and outer joins, as well as self joins, semi joins, anti joins and cross joins—fundamental tools in any PostgreSQL wizard's toolbox. Never fear set theory again after learning all about unions, intersections, and except clauses through easy-to-understand diagrams and examples. Lastly, you'll be introduced to the challenging topic of subqueries. You will be able to visually grasp these ideas by using Venn diagrams and other linking illustrations.

Join two or three tables together into one, combine tables using set theory, and work with subqueries in PostgreSQL.

Learn to join tables, combine tables using set theory, and work with subqueries in PostgreSQL.

Data Science Courses

Data Science Tracks

Our Learning Experience

Kenyon Maree

Workspace Publications (0)

Completed Tracks(3)

Completed courses (48)

Completed projects (7)