Datacamp

SQL is an essential skill for data scientists, and Microsoft SQL Server is one of the world's most popular database systems. In this course, you'll start with simple SELECT statements, and refine these queries with...

Say you've found a great dataset and would like to learn more about it. How can you start to answer the questions you have about the data? You can use dplyr to answer those questions—it...

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...

There’s a lot more to Microsoft Excel than SUM and COUNT. Companies and institutions around the world use Excel’s powerful functions to efficiently transform mountains of raw data into clear insights, and now you can...

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...

The ability to produce meaningful and beautiful data visualizations is an essential part of your skill set as a data scientist. This course, the first R data visualization tutorial in the series, introduces you to...

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...

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...

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...

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...

You’ve already used SQL to query data from databases. But did you know that there's a lot more you can do with databases? You can model different phenomena in your data, as well as the...

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...

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...

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...

Say you have a collection of customers with a variety of characteristics such as age, location, and financial history, and you wish to discover patterns and sort them into clusters. Or perhaps you have a...

You have probably come across Google News, which automatically groups similar news articles under a topic. Have you ever wondered what process runs in the background to arrive at these groups? In this course, you...

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...

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...

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...

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...