10 Essential Data Science Resource Collections to Use with Python - dummies

10 Essential Data Science Resource Collections to Use with Python

By John Paul Mueller, Luca Massaron

There really is a ton of information available out there for data scientists using Python. This information introduces you to a wealth of data science resource collections that you really need to know.

Gain insights with Data Science Weekly

The Data Science Weekly is a free newsletter that you can sign up for to obtain the latest information about changes in data science. The resources cover the following broad range of topics:

  • Data Science Books

  • Data Science Meetups

  • Data Science Massive Open Online Courses (MOOCs)

  • Data Science Datasets

  • Data Science Most Read Articles

  • Data Scientist Talks

  • Data Scientists on Twitter

  • Data Science Blogs

Obtain a resource list at U Climb Higher

Even with the right connections online and a good search engine, trying to find just the right resource can be hard. U Climb Higher has published a list of 24 data science resources that’s guaranteed to help keep your finger on the pulse of new strategies and technologies. This resource broaches the following topics: trends and happenings; places to learn more about data science; joining a community; data science news; people who really know data science well; all the latest research

Get a good start with KDnuggets

Learning about data mining and data science is a process. KDnuggets breaks the learning process down into a series of steps. Each step provides you with an overview of what you should be doing and why. You also find links to a variety of resources online to make the learning process considerably easier. Even though the site emphasizes the use of R, Python and SQL (in that order) to perform data science tasks, the steps will actually work for any of a number of approaches that you might take.

Access the huge list of resources on Data Science Central

Many of the resources you find online cover mainstream topics. Data Science Central provides access to a relatively large number of data science experts that will tell you about the most obscure facts of data science. Check out one of the more interesting blog posts.

This resource points you to a Trello list of some truly amazing resources. The categories include the following:

  • Data news

  • Data business people track

  • Data journalist track

  • Data padawan track

  • Data scientist track

  • Statistics

  • R

  • Python

  • Big data and other tools

  • Data

  • Others

Obtain the facts of Open Source Data Science from Masters

Many organizations now focus on open source for data science solutions. The focus has become so prevalent that you can now get an Open-Source Data Science Masters (OSDSM) education at. The emphasis is on providing you with the materials that are normally lacking from a purely academic education. In other words, the site provides pointers to courses that fill in gaps in your education so that you become more marketable in today’s computing environment.

Locate free learning resources with Quora

It’s really hard to resist the word free, especially when it comes to education, which normally costs many thousands of dollars. The Quora site provides a listing of the best nonpaid learning resource for data science.

Most of the links take on a question format, such as, “How do I become a data scientist?” The question-and-answer format is helpful because you might be asking the questions that the site answers. The resulting list of sites, courses, and resources are a good way to get started working in the data science field.

Receive help with advanced topics at Conductrics

The Conductrics site as a whole is devoted to selling products that help you perform various data science tasks. However, the site includes a blog that contains a couple of useful blog posts that answer the sorts of advanced questions that you might find it difficult to answer elsewhere.

The author of the blog posts, Matt Gershoff, makes it clear that the listings are the result of answering people’s questions in the past. The list is huge, which is why it appears in two posts rather than one, so Matt must answer many questions. The list focuses mostly on machine learning rather than hardware or specific coding issues.

Learn new tricks from the Aspirational Data Scientist

The Aspirational Data Scientist blog site provides you with an amazing array of essays on various data science topics. The author splits the posts into these areas: data science commentary; online course reviews; becoming a data scientist.

Data science attracts practitioners from all sorts of existing fields. The site seems mainly devoted to serving the needs of social scientists moving into the data science field. In fact, the most interesting post that appears provides a listing of resources to help the social scientist move into the data scientist field. The list of resources is organized by author, so you may find names that you already recognize as potential informational resources.

Find data intelligence and analytics resources at AnalyticBridge

The AnanlyticBridge site contains an amazing array of helpful resources for the data scientist. One of the more helpful resources is the list of data intelligence and analytics resources. This page contains a wealth of resources you won’t find anywhere else that are organized into the following categories: general resources; big data; visualization; best and worst of data science; new analytics startup ideas; rants about healthcare, education, and other topics; career stuff, training, and salary surveys; miscellaneous.

Zero in on developer resources with Jonathan Bower

More than a few interesting resources appear on GitHub, a site devoted to collaboration, code review, and code management. One of the sites you need to check out is Jonathan Bower’s listing of data science resources. The majority of these resources will appeal to the developer, but just about anyone can benefit from them. You find resources categorized into the following topics:

  • Data science, getting started

  • Data pipeline and tools

  • Product

  • Career resources

  • Open source data science resources