20 Data Science Resources to keep you Ahead of the Curve
Even though data science is a relatively new field there is a plethora of resources that one can use to hone one’s skills in data science and keep oneself updated with trends and best practices. There are lots of websites, books, communities, and resources that one can use one’s knowledge of the data science field. However at the same time it’s important that one chooses the right resources and doesn’t get lost in the information overload. We bring you here a list of credible sources which one can rely upon to enhance one’s knowledge and skills in the field of data science.
News & Updates
Data Science Weekly: Data Science Weekly offer loads of content such as interviews, job opportunities, and resources on how to build a career in data science which is delivered in the form of a weekly newsletter.
KDNuggets: KDNuggest is a rich source of articles, news, webinars etc. on data science, data mining and building models.
R-Bloggers: R-Bloggers is an aggregator site for content focused on news and tutorials related to R. You can rely on R-bloggers for latest developments, trends and content on R-language by some of the leading commenters of the subject at one place.
Revolutions: Revolutions is a blog dedicated to news and information of interest to members of the R community. This blog is updated every US workday, with contributions from leading minds in the field.
Flowing Data: This website by Dr. Nathan Yau(a PhD in statistics from UCLA) explores how statisticians, designers, data scientists, and others use analysis, visualization, and exploration to understand data. Yau keeps you updated with latest trends, tutorials and recommendations to help you keep ahead of the curve in data science
Simply Statistics: This website offers lots of articles on use of data to solve complex problems. The site also has useful insights for a successful career in data science.
ALSO READ: How to Become a Data Scientist
Practice Your Skills
Kaggle: is a machine learning competition site. Kaggle gives users a structured exposure to a lot of data and has a great community. It allows you to hone your data science skills through doing projects with real data sets.
crowdanalytix: CrowdANALYTIX operates a crowd-sourcing platform in which a large community of independent analytical experts solve your problems using a competitive contest model.
Datasciencecentral: Data Science Central is the an online resource Centre for the big data practitioners. From Analytics to Data Integration to Visualization, Data Science Central provides a community experience that includes a robust editorial platform, social interaction, forum-based technical support, the latest in technology, tools and trends and industry job opportunities.
Reddit Machine Learning Subreddit: this is a 30,000 strong community of data science practioners interacting on a wide range of issues from sharing news, research papers, videos to helping each other with info, advice and resource on machine learning, data mining, information retrieval, learning theory, and related topics.
Datatau: Datatau is popularly refered to as the Hacker News for data scientists as its community share loads of articles relevant for the community. There are additional community inputs for people trying to build a creer in data science field.
ALSO READ: 7 Key Traits of a Good Data Scientist
One important source of keeping oneself updated is to simply follow what the masters of the field are saying and talking about. Some of the tall names in the field which one could follow are :
- Dj Patil (@dpatil)
- Hillary Mason (@hmason)
- Jeff Hammerbacher (@hackingdata)
- Drew Conway (@drewconway)
- Nathan Yau (@flowingdata)
Here we are listing a few online resources to find good data science training destinations across the world:
- Open Source Data Science Masters: This site offers a free list of online classes and resources. The resources are organized as a self-paced curriculum.
- Learn Data Science: This site also offers a self-paced curriculum that introduces students to four key topics in the machine learning field: linear regression, logistic regression, random forests, k-means clustering.
- Coursera: There are 9 courses in it. They comprehensively cover all the important concepts and topics under data science such as Data Science Toolbox, R Programming & Getting and Cleaning Data among others.
- Udacity: Udacity has some of the advanced level courses for students interested in data science career.
- learnUnbound : The site lists data science courses by some of the leading training institutes. The site boasts of the most comprehensive listing of courses and helps students choose the right course at attractive prices and then goes on to hand hold the students with updates on job opportunities, emerging career trends and an opportunity to network with like-minded participants.