We've got big news! We're very proud to say that we have partnered with our friends at Continuum Analytics, the makers of Anaconda, to make a new series of courses focusing on Data Visualization and Data Manipulation with Python. Today, we are launching the first course, Interactive Data Visualization with Bokeh. Bokeh is an interactive visualization library for Python (and other languages!) that targets modern web browsers for presentation. It can create versatile, data-driven graphics, and connects the full power of the entire Python data-science stack to create rich, interactive visualizations. Thanks to the folks at Anaconda, you can learn how to leverage the power of Bokeh for your data science projects!
In this post, we provide some more details on our new series of Anaconda courses:
- Interactive Data Visualization with Bokeh
- Introduction to Data Visualization with Python (coming soon!)
- Pandas Foundations (coming soon!)
- Manipulating DataFrames with Pandas (coming soon!)
So, what are you waiting for? Read through the post and get on your way to becoming a Python master!
Intro to Python for Data Science (free!)
Python is a general-purpose programming language that is becoming more and more popular for doing data science. Companies worldwide are using Python to harvest insights from their data and get a competitive edge. Unlike any other Python tutorial, this course focuses on Python specifically for data science. In our Intro to Python class, you will learn about powerful ways to store and manipulate data as well as cool data science tools to start your own analyses. Enter DataCamp’s online Python curriculum.
The intermediate python course is crucial to your data science curriculum. Learn to visualize real data with matplotlib's functions and get to know new 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're ready to blend together everything you've learned to solve a case study using hacker statistics.
Interactive Data Visualization with Bokeh
The future of Data Visualization is interactive. To be a Datavis ninja, you'll need to be adept at building interactive figures. Enter Bokeh: a Python interactive visualization library that provides elegant, concise construction of novel graphics. You'll come out of this with a strong foundational knowledge of how to use Bokeh to create interactive graphics.
In the first chapter, you'll begin with an introduction to basic plotting with Bokeh. You will create your first plots, learn about different data formats Bokeh understands, and make visual customizations for selections and mouse hovering. Next, you'll learn how to combine multiple Bokeh plots into different kinds of layouts on a page, how to easily link different plots together in various ways, and how to add annotations such as legends and hover tooltips. In addition to versatile data-driven glyphs, Bokeh comes with a variety of high-level statistical chart types built in, so that you can get quick exploratory charts with very little code.
Towards the end of the chapter, you'll learn how Bokeh server applications let you connect all of the powerful Python libraries for analytics and data science, such as NumPy and Pandas, to rich interactive Bokeh visualizations. Learn about Bokeh's built-in widgets, how to add them to Bokeh documents alongside plots, and how to connect everything to real python code using the Bokeh server. In the final chapter, you'll build a more sophisticated Bokeh data exploration application from the ground up, based on the famous Gapminder data set.
Introduction to Data Visualization with Python (coming soon!)
Data visualization is sexy for a reason: it is a fundamental method of conveying the information contained in complex data sets and is also an essential tool in exploratory data analysis. Here you will learn how to plot data in a million ways with Matplotlib, an industry-standard plotting library, via dives into a series of sample data sets. After the course, you will be able to produce beautiful figures and images. Also, through experimentation with an array of data sets, you'll learn how to use the library Seaborn to build informative statistical graphs that incorporate advanced modeling procedures.
Pandas Foundations (coming soon!)
Many real-world data sets contain strings, integers, time-stamps & unstructured data. How do you store data like this so that you can manipulate it and easily retrieve important information? The answer is in Pandas! In this course, you'll learn how to use the industry-standard Pandas library to import, build and manipulate DataFrames and Series. With Pandas, you'll always be able to convert your data into a form that permits you to analyze it easily. You'll also learn more about NumPy, how it stores data and its relation to the Pandas data structures.
Manipulating DataFrames with Pandas (coming soon!)
It's time to really get your hands dirty and figure out just how powerful Pandas can be. In this course, you'll develop techniques to index, organize, reshape, extract, transform, and aggregate your data so that it's ready to answer your specific questions. You'll round this out with a deeper analysis of the Olympic Medals data set. This course will introduce you to advanced data organization techniques which is a fundamental next step on the road to becoming a well-rounded Data Scientist.
From the basecamp to the pinnacle of your journey, DataCamp is the first and foremost leader in Data Science Education offering skill-based training, pioneering technical innovation, and partnering with the world's best educators. We have trained over 620,000 (aspiring) data scientists in over 190 countries who together have completed over 25 million coding challenges.
Continuum Analytics help people discover, analyze, and collaborate by connecting their curiosity and experience with any data. It all comes together here. Anaconda gives superpowers to people who change the world.
They build technologies that enable analysts to answer questions from the data all around us. By engaging open source communities, they are building useful tools that are sustainable and widely used. By focusing on collaborative technologies, they give analysts a better medium to communicate their decision process and focus their stakeholders on the details that matter most. In creating a platform for distributing that expertise, they enable teams to quickly share insights and evolve solutions most critical to their success.