Get Started in Data Science: 5 steps you can Take Online for Free

Data Science, Machine Learning, Visualization, Data Mining, Statistics, Fusion Analytics World

From gaining the right skills to acing your first Data Science interview, these resources can help put you on the right track.

Making a career change is never easy, but few things are more motivating than the prospect of a good salary and a dearth of competition. That’s a fair summary of the data science world today, as at least one well-publicized study has made clear, so why not investigate a little further?

There’s been a flurry of free resources popping up online to help those who are intrigued learn more. Here’s a small sampling for each step of the way.

1. Understand what it is

Microsoft’s website might not automatically spring to mind as a likely place to look, but sure enough, a few months ago the software giant published a really nice series of five short videos entitled “Data Science for Beginners.” Each video focuses on a specific aspect, such as “The 5 questions data science answers” and “Is your data ready for data science?”

2. Dig a little deeper

If you think you might be interested in a career in data science, you may want to start getting a feel for the lay of the land by tapping into some of the big blogs and community websites out there. The newly revamped OpenDataScience.com is one example; KDnuggets is another useful resource. A recent post on Data Science Central (another good site) lists key accounts to follow on Twitter. KDnuggets suggests some good e-books to read before plunging into a data science career.

3. Learn the ropes

Still interested? Then you’re probably going to need to acquire some skills, as Adam Flugel, a data-science recruiter with Burtch Works, noted in an interviewearlier this year. DataCamp delivers instruction for both groups and individuals; other online options include Open Source Data Science Masters and this introductory textbook, which is available as a free PDF through a Creative Commons license. KDnuggets has a nice list of its own with other good options.

4. Get a job

There are lots of places to look for data scientist jobs today, including the aforementioned OpenDataScience.com, which has a section dedicated to that purpose. Kaggle has a jobs board of its own, and there are more to be found on Glassdoor and Indeed.com. Correlation One, a site that just launched earlier this year, bills itself as a matchmaker for data scientists and employers.

5. Prepare to impress

Once you get an interview, there’s nothing more confidence-building than expert tips for acing it. Look no further than KDnuggets, which has published just what you need in a post entitled, “21 Must-Know Data Science Interview Questions and Answers.” Good luck!

By , Source:Computer World
Profile Status
ACTIVE
Profile Info
 

Kalyan Banga178 Posts

I am Kalyan Banga, a Post Graduate in Business Analytics from Indian Institute of Management (IIM) Calcutta, a premier management institute, ranked best B-School in Asia in FT Masters management global rankings. I have spent 6 years in field of Analytics.

3 Comments

  • Using Big Data To Boost Oil Industry - Fusion Analytics World Reply

    December 7, 2016 at 6:49 am

    […] on it is the oil and gas exploration sector. In fact, the sector has been leading innovations in data science. As natural resources continue to shrink markedly, there is break-neck competition among oil […]

     
  • How To Become A Data Scientist In 2017 - Fusion Analytics World Reply

    December 7, 2016 at 12:43 pm

    […] Data scientists utilize their knowledge of statistics and modeling to convert data into actionable insights about everything from product development to customer retention to new business opportunities. […]

     
  • Google Acquires Data Science Community Kaggle - Fusion Analytics World Reply

    March 21, 2017 at 1:11 am

    […] Buying Kaggle, and its mindshare within the community, will also probably help with recruiting. Google needs to ensure it keeps snapping up the best talent that specializes in deep learning, competing with other companies like Pinterest (which focuses on visual search). Even if this isn’t a more specialized tech acquisition, it means that Google is broadening its focus to explore more perpendicular approaches to ensure its dominance in AI. […]

     

Leave a Comment

1 × 3 =