How to Start a Career in Analytics for Free?

What & Why Analytics?

Analytics is defined as the scientific process of transforming data into insight for making better decisions. It helps improve processes, saves cost and enhances revenue.

Although analytics has been around for a long while, it wasn’t until the last 5 to 10 years that its importance in the business field has been realized. It was in the last 10 years that technology has been revolutionized and we now produce about 2.5 Quintilian bytes of data every day. What has also changed in the last decade is that we now have the means to sift through these 2.5 Quintilian bytes of data in a reasonable amount of time, with little cost to store it.

• 90% of the world’s data has been generated in the last 2 years. The same data is predicted to double every 18 months from now on
• In 1990, cost of storing 1GB data = $9000 (cost of a car)
  In 2010, cost of storing 1GB data = 8 cents (cost of a lollipop)

In organizations, analytics enables professionals to convert extensive data and statistical and quantitative analysis into powerful insights that can drive efficient decisions. Therefore with analytics organizations can now base their decisions and strategies on data rather than on gut feelings. Thus with powerful insights, analytics promises reduced costs and increased profits.

• The analytics Industry is one of the fastest growing in modern times, poised to become a $50 billion market by 2017.
• With this sudden surge in the analytics industry there is a tremendous increase in the demand for analytics expertise across all domains, throughout all major organizations across the globe.
• It has been predicted that by 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.

Who Can Get Into Analytics?

There is no dedicated undergraduate degree for analytics as it is combination of various fields. Hence, no degree in itself can guarantee a career in analytics. However, Engineering or Statistics/Maths degrees are preferred but not necessary.

Pros:   Almost any undergraduate can apply
Cons: Tough Competition due to high number of applicants

*Note: Designations like data scientist, business analyst, data analyst and consultant are used interchangeably by different organizations.

How to Prepare for a Job in Analytics?

Scenario 1 : On-Campus Jobs

Selection of applicants in this case is less technical and more focused on the following skills:-

• Structured Thinking
• Attention to Details
• Numerical Skills
• Problem Solving Capabilities
• Communication Skills
• Knowledge of Undergrad Discipline

Interview Process is as follows:-

• Round 1 – Aptitude Test
• Round 2 – Group Discussion/Video Synthesis
• Round 3 – Technical (Puzzles, Guess-Estimates, Case Studies, Undergrad Questions)
• Round 4 – HR

If you are from a tier-1 institute then there are a good number of companies offering analytics related roles like data scientist, business analyst, data analyst, consultant and so on. Interview preparation (as mentioned above) is pretty standard and abundant information/resources can be found on the web.

However, for tier-2 and tier-3 institutes very few and sometimes no company offers such roles. The only reason is that the number of applicants is so high that they need to filter out candidates.

Scenario 2 : Off-Campus Jobs or Shifting Jobs Early in your Career

My story falls under this category so I would want to describe how I went about things. I only intend to share an approach that worked well for me:-

• I have done engineering from tier-2 institute and very few companies offered analytics related roles on campus. Not that I was too concerned with placements, not until the companies actually started pouring in.

• After initial carefree years, every final year student has to decide what he/she really wants from life. I was no different, confused as to what I really wanted to do. I did give GRE (getting a good score) but soon dropped the idea of pursuing MS as I had no inclination towards a particular subject. I dropped the idea of giving the MBA/MTech entrances as well.
• I sat for Accenture and got placed as an Associate Software Engineer. As the job situation in the market was pretty tight, once placed, we were not allowed to sit for other companies coming on campus. I applied for other companies off campus but could not even manage to get interview calls from most of them. With thousands of people applying for the same position, I was in no way better than them.
• So on 26^th August, 2014 I started working in Accenture. I liked my work but this is not what I wanted to do for the rest of my life, neither did I want to pursue Post Graduation without an aim in mind. I did not want to get into a trap just to run away from one.
• Two months after joining I started reading about analytics on the net. I started reading blogs, watching videos on YouTube and going through work profiles of people in it. The more I read about it, the more it fascinated me.

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Is this something that I like?  Yes

Is this what I really want?  Don’t know

Is this something that I would be good at?  Can’t say

Should I start applying to other companies?  Considering the above, don’t think so

Should I introspect and apply after a few months?  Yes, seems like a good option

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• Around this time I was first introduced to the concept of Massive Open Online Courses (MOOC). It is a course of study made available over the Internet without charge to a very large number of people. So I had the access to analytics courses offered by world’s best universities for free. It was as if I had struck gold. I could work on these courses after office hours and during weekends without compromising on my job work. For the next 8 months I not only completed these certification courses but also participated in online competitions, became part of professional communities and read various blogs/articles.

 MOOC that I had completed:-

1. https://www.edx.org/course/analytics-edge-mitx-15-071x-0
2. https://www.edx.org/course/data-analysis-take-it-max-delftx-ex101x
3. https://www.coursera.org/course/datascitoolbox
4. https://www.coursera.org/course/rprog
5. https://www.coursera.org/course/getdata
6. https://www.coursera.org/course/exdata
7. https://www.coursera.org/course/interactivepython1
8. http://academy.hubspot.com/inbound-certification
9. https://www.coursera.org/course/statistics
10. https://www.udacity.com/course/intro-to-descriptive-statistics–ud827
11. https://www.udacity.com/course/intro-to-inferential-statistics–ud201
12. Classroom/Virtual Training’s in Accenture

Competitions that I had participated in:-

1. https://www.kaggle.com/
2. https://www.crowdanalytix.com
3. http://discuss.analyticsvidhya.com/

Blogs that I followed:-

1. http://www.analyticsvidhya.com/blog/
2. http://www.datasciencecentral.com/
3. http://www.kdnuggets.com/

Communities that I am part of:-

1. http://www.meetup.com/Data-Science-Delhi/
2. http://www.meetup.com/New-Delhi-R-UseR-Group/
3. http://www.meetup.com/AC-DEL/
4. http://www.meetup.com/Predictive-Business-Analytics-with-R-New-Delhi/

What happened after I finished my preparation:-

I started applying to companies for roles like data scientist/analyst/statistical modeler and so on (It’s the work profile that matters and not the designation as designations can be misleading). I got calls from many companies and in almost every interview I did well.

I had developed the industry knowledge by reading blogs/articles and being part of professional communities, technical knowledge by doing MOOCs, hands on experience by completing MOOC assignments/projects and online competitions and most importantly developed the confidence that I was the right candidate.  It’s amazing what you can do in an interview when you’re confident about yourself.

Ultimately I had a few offers to choose from.  I chose to shift into Accenture Analytics after a round of interviews.

Summarizing on “How to Prepare for a Job in Analytics”:-

• Interview Process – Every company has its own interview process as I had described earlier. Some are rigorous and some can be cracked more easily. Prepare for them in advance.
• Internships – There are analytics companies willing to hire you as an intern. Not only will you learn a lot but it reflects well on your resume.
• MOOC – It provides you with wealth of knowledge and hands on experience. Do not complete it for the certifications. I was never asked to show even a single certificate for over a dozen of interviews. If you can convey what you learnt from such courses, it’s more than enough.
• Projects/Competitions – It’s best when you can show your work in form of a project or some competition. Its gives credibility to your knowledge and skills. Create a Github account and put all your work there.
• Professional Communities –When you’re part of professional communities you meet loads of people having similar interest and you get to learn a lot. I have mentioned a few of the communities that you can join.
• Reading Blogs and Articles – It widens your horizon. As it is a gradual process, its best to read simultaneously with your preparation.
• Resume – Having a cracker of a resume helps you get the crucial interview calls. Give your resume time and consideration before applying.

Conclusion:-

If you really want to start a career in analytics you can do so for free. All you need to give is time and dedication to it and utilize the resources that are available to you, I can speak so from my personal experience. I fly to Mumbai next week moving into Accenture Analytics. All I hope is this post will help many like me, who just need a little push to get them on their way.

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