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Data visualization has quickly become a standard for disseminating information on the web. It's used across a range of industries, from business intelligence to journalism, to help us understand and communicate the insights within data. Our brains are primed to process information that's presented visually, making it much easier for us to understand data visualized in charts and graphs than data listed in tables and spreadsheets. A great data visualization should leverage these strengths of the human visual system to display data so that it can be readily absorbed and understood. It should take into account what we know about visual processing to enhance and ease the viewers' experience of the data. With so many tools and frameworks now available for building these graphics, it's time to go back to basics. What makes data visualizations effective? What guiding principles should we follow when designing with data? The following best practices will help you design rich, insightful data

How does it feel to be a data geek in 2017?

Use this curated list of resources to learn data science!

Many people are seeking to learn data science these days. It’s become a trendy topic associated with high salaries and some of the most interesting problems in the world. This demand has created many different resources in the data science space.

Docker is the world’s leading software container platform. Developers use Docker to eliminate “works on my machine” problems when collaborating on code with co-workers. Operators use Docker to run and manage apps side-by-side in isolated containers to get better compute density. Enterprises use Docker to build agile software delivery pipelines to ship new features faster, more securely and with confidence for both Linux and Windows Server apps.

Docker Terminology:

1. Docker Containers: Small user-level virtualization (isolation) that helps you install, build and run your code/workflow. All the code would be continuosly running in these containers.
2. Docker Images: An image is an inert, immutable, file that’s essentially a snapshot of a container. These are your actual committed containers (ones that have the process running, data stored, ports exposed to be used). Docker images are essentially the stored instances that you can (actually move around).
3. Dockerfile: It is a YAML (almost) based file from which Docker creates an image. It can be thought of as an automated script that has all the steps you want to execute.

These techniques cover most of what data scientists and related practitioners are using in their daily activities, whether they use solutions offered by a vendor, or whether they design proprietary tools. When you click on any of the 40 links provided, you will find a selection of articles related to the entry in question. Most of these articles are hard to find with a Google search, so in some ways this gives you access to the hidden literature on data science, machine learning, and statistical science. Many of these articles are fundamental to understanding the technique in question, and come with further references and source code.

Cutting edge data science projects.

After creating the Free Wtr bot using Tweepy and Python and this code, the author wanted a way to see how Twitter users were perceiving the bot and what their sentiment was. So he created a simple data analysis program that takes a given number of tweets, analyzes them, and displays the data in a scatter plot.

In order to create this, you have to install a few packages, including  Tweepy , Tkinter , Textblob and  matplotlib . These packages can be installed using the pip package manager.

An open source Data Science repository to learn and apply towards solving real world problems.

• Motivation
• Infographic
• What is Data Science?
• Colleges
• MOOC's
• Data Sets
• Bloggers
• Podcasts
• Books
• Facebook Accounts
• Twitter Accounts
• YouTube Videos & Channels
• Toolboxes - Environment
• Journals, Publications and Magazines
• Presentations
• Data Science Competitions
• Comics
• Other Awesome Lists

Many people new to data science might believe that this field is just about R, Python, Hadoop, SQL, and traditional machine learning techniques or statistical modeling. Below you will find fundamental articles that show how modern, broad and deep the field is. Some data scientists are actually doing none of the above.

The article on deep data science (see below) shows that data science is also about automating the tasks that many people (calling themselves data scientists) do routinely. And it can be done using very little mathematical / traditional statistical science.

Many of these articles should help the beginner to have a better idea about what data science is. Some are technical, but most can be understood by the layman.

24 Articles About Core Data Science

• Data Science Compared to 16 Analytic Disciplines
• 10 types of data scientists
• 40 Techniques Used by Data Scientists 
• 50 Questions to Test True Data Science Knowledge -- also read this article
• 24 Uses of Statistical Modeling
• 21 data science systems used by Amazon to operate its business
• 10 Modern Statistical Concepts Discovered by Data Scientists
• 8 Deep Data Science Articles
• 22 tips for better data science
• How to detect spurious correlations, and how to find the real ones
• High versus low-level data science
• The of curse of big data 
• 4 Easy Steps to Structure Highly Unstructured Big Data 
• Fast Feature Selection with New Definition of Predictive Power
• Fast clustering algorithms for massive datasets
• Building blocks of data science
• Life Cycle of Data Science Projects
• Data Scientist Shares his Growth Hacking Secrets
• Hitchhiker's Guide to Data Science, Machine Learning, R, Python
• Data Scientist vs Statistician
• Data Scientist versus Data Engineer
• Data Scientist versus Business Analyst
• Data Scientist versus Data Architect
• Vertical vs. Horizontal Data Scientist

What does “causality” mean, and how can you represent it mathematically? How can you encode causal assumptions, and what bearing do they have on data analysis? These types of questions are at the core of the practice of data science, but deep knowledge about them is surprisingly uncommon.

If you analyze data without regard to causality, you open your results up for the possibility of enormous biases. This includes everything from recommendation system results, to post-hoc reports on observational data, to experiments run without proper holdout groups.

Recent posts have been aimed at a more general audience. This one will be aimed at practitioners, and will assume a basic working knowledge of math and data analysis. To get the most from this post you should have a reasonable understanding of linear regression and probability (although we’ll review a lot of probability). Prior knowledge of graphical models will make some concepts more familiar, but is not required.

There are plenty of cool technologies available to collect and examine data. Both web and desktop applications have provided some really great interfaces to fall in love with data mining, and with the rise in popularity we have noticed an increased number of infographics created over the past few years.