Data Science is the latest trend in the industry. Although initially many rejected as a simple fashion, but now over the years several organizations have realized the potential of data science to generate useful information from structured and unstructured data.
From banks to e-commerce companies to manufacturing industries, they all understood the importance of data science career and adopted it in their daily activities to improve their performance.
The role of a data scientist has already earned the name of “the sexiest job of the 21st century”. According to a report by the Mckinsey Global Institute, there will be a shortage of 140,000 to 190,000 data science career professionals by 2018 in the United States alone.
With respect to India, some studies suggest that the data analysis and analysis industry in India is at a stage where it is about 10-15 years old and that we can expect a boom In the field of analytical outsourcing in India.
I also believe that India with its pool of science / analytics talent data can very well be the leader in this industry. Already there are some success stories such as Mu Sigma and Fractal analytics. In addition, we now officially live in the Age of Big Data.
So it is very clear that the reason why data researchers are in demand and also many new jobs would be created in this area in the near future. Therefore, the data science can be considered a lucrative career option.
What does a data scientist do?
Data Science is an amalgamation of business understanding, mathematics, statistics, programming and communication skills. As such, it is expected that all of the above skills will be presented as a data scientist.
It is expected that a data scientist will understand the business problem, develop a hypothesis, understand the type of data required, perform data clean-up and preliminary data analysis, build statistical models to solution and ultimately effectively communicate ideas to the client. Thus, the work of a data scientist encompasses various roles and functions.
Getting into Data Science career as a fresher and with experience
Now, to make sure your resume catches eyeballs when you apply to an analytical business needs some preparation. The preparation would be different for a fresher than for someone who already has some experience working under his belt even though in a different area.
For a graduate in engineering or mathematics / statistics, the emphasis is placed more on solving analytical problems and exposure to certain programming languages. Then they can go to the analyst offices either through investments in university colleges or off-campus placement campaigns and try to ace their interview process.
But for someone with substantial work experience in another area say a computer professional, it’s a different story altogether. A computer professional is generally good at programming skills, but they are falling short by enough when it comes to mathematical intuition or depth in business understanding.
So for an IT professional or in fact professional from any other sector, it is a bit difficult to make the transition in the science of data, but not impossible. I have made this transition and I can testify to that.
How to start a Data Science career
Analytics or Data Science Recruiters are looking for relevant skills and therefore the trick is to acquire these skills over a period of time and exploit them during an interview. Now we will discuss the various aspects that are needed to work to make a successful transition to the analytical industry.
1. Get a Masters (MS / MBA) degree with business analytics specialization
This is obviously the traditional way, that is, starting with a clean slate. One can enroll in a postgraduate program in analytics.
For example – IIM Calcutta started a PGP in business analysis with ISI Kolkata and IIT Kharagpur a few years back and this program is doing well.
There are also very good master’s programs in various American universities. For example, North Carolina State University, MIT Sloan, UC Berkeley, Texas A&M, etc.
One can even go for a general MBA, but take some elective related analyzes such as advance data analysis, automatic learning, etc.
But then this is something that might not be possible for everyone for various reasons. In this case, emphasis should be placed on self-learning and the effective use of freely available learning resources. Some of these are discussed below.
2. Build Statistics / Machine learning foundations
It is expected that a researcher in charge of data mining will have some knowledge of the various statistical methods or automatic learning in the industry.
We can start from the base, ie the normal distribution, the central limit theorem, the test hypothesis and then move on to advanced techniques. Linear regression, logistic regression, decision trees, cluster analyzes, generalized additive models, etc.
3. Gain technical skills in Analytics
With regard to tools in the analytical industry, SAS and SPSS were popular before the open source revolution took the industry by storm. Open Source tools like R and Python are the next big thing and it would make sense to invest time on them.
There are enough resources freely available on the web to learn both R and Python. For people with coding skills in object-oriented languages like Java will find Python intuitive. But R is the best tool (personal opinion) when it comes to statistical modeling and it is also the preferred tool in academia.
For an absolute beginner, the initiation course at R at datacamp.com can be a starting point. But the best way to learn these software is to do. So I suggest that one should reproduce the available codes and test it on some dummy data sets to understand what is going on.
Also, a working knowledge of SQL with advanced MS Excel / VBA skills can act as a differentiator when one appears for their interview.
4. Read up on business applications of Data Science
Given that the science of data is not only a matter of technique, it would be really useful if one understands the commercial applications of it and one is also aware of various cases of successful use.
This will help to see the larger image and also make a well equipped to understand what kind of methodology suits for a particular business problem.
For example, how market basket analysis is used for grouping products by retailers, how cluster analysis can be used for customer segmentation for a new product launch, how logistic regression can be used For the detection of fraud in the banking
5. Participate in various data science competitions
The last but not least would be – practice, practice and practice. One way to do it would be by participating in various competitions.
Also, the discussion on the forums with like-minded data science enthusiasts can be helpful.
Finally, even after one has got a break in the data science industry, one needs to guard against complacency. The way technology is progressing and the analytics field is developing, there is something new to learn everyday!