How To Start Your Career in Data Science

Original article was published by SHAIK SAMEERUDDIN on Artificial Intelligence on Medium


How To Start Your Career in Data Science

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Data Science is the industry’s new theme. Although many initially dismissed it as a simplistic trend, many companies have now recognized the ability of data science over the years to produce valuable knowledge from structured and unstructured data.

They all understood the significance of the data science profession, from banks to e-commerce businesses to manufacturing industries, and embraced it in their daily activities to boost their efficiency.

The position of a data scientist has already won the reputation of “the 21st century’s sexiest work.” According to a study by the Mckinsey Global Institute, in the United States alone, there will be a shortage of 140,000 to 190,000 career professionals in data science by 2018.

With regard to India, some studies indicate that India’s data collection and analysis industry is at a stage where it is around 10–15 years old and that we can expect a boom in India in the field of analytical outsourcing.

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I also believe that India may very well be the leader in this industry with its science/analytics talent data pool. Some success stories, such as Mu Sigma and Fractal Analytics, are already available. Furthermore, we now officially live in the Big Data Era.

So it is very clear that in the near future, the reason why data scientists are in demand and many new jobs would also be created in this area. Data science can, therefore, be considered a lucrative career choice.

What’s a data scientist doing?

The mixture of business knowledge, mathematics, statistics, programming, and communication skills in Data Science. As such, as a data scientist, it is assumed that all of the above skills will be addressed.

A data scientist is expected to understand the business problem, develop a theory, understand the type of data needed, conduct data clean-up and preliminary data analysis, construct mathematical models to solve, and eventually communicate ideas to the customer effectively. Therefore, a data scientist’s work involves numerous tasks and functions.

Moving into a fresher and more experienced career in Data Science

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Now, when applying to an analytical firm, you need some training to make sure your resume catches eyeballs. The training will be different for a fresher one than for someone who, while in a different field, already has some experience working under his belt.

The focus is focused more on solving analytical issues and exposure to such programming languages for a degree of engineering or mathematics/statistics. Then either by investments in university colleges or off-campus placement initiatives, they will go to the analyst offices and try to ace their interview process.

But for someone with extensive work experience in another field, a computer professional says, it’s a totally different story. In general, a computer professional is fantastic at programming skills, but when it comes to mathematical insight or depth in business comprehension, they fall short enough.

How to begin a career in Data Science

Recruiters in analytics or data science are searching for suitable skills, so the trick is to learn these skills over a period of time and leverage them during an interview. We will now address the different factors required to work in order to make a good transition to the analytical industry.

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1. Get a Masters (MS / MBA) degree specialising in business analytics

Obviously, this is the conventional way, that is, beginning with a clean slate. In analytics, one may enrol in a postgraduate programme.

For instance, a few years ago, IIM Calcutta launched a PGP in business analysis with ISI Kolkata and IIT Kharagpur, and this programme is doing well.

At different American universities, there are still very strong master’s programmes. North Carolina State University, MIT Sloan, UC Berkeley, Texas A&M, for instance, etc.

One may also go for a general MBA, but some elective relevant analyses, such as advance data analysis, automated learning, etc., can be taken.

But again, for different reasons, this is something that may not be possible for everyone. In this case, the focus should be put on self-learning and the efficient use of learning opportunities which are readily available. Below, some of these are mentioned.

2. Construct Statistics / Foundations for machine learning

A researcher in charge of data mining is required to have some knowledge of the different statistical methods or automated learning in the industry.

We can start from the basis, that is, the normal distribution, the theorem of the central limit, the hypothesis of the test and then move on to advanced techniques. Linear regression, logistic regression, cluster analysis, generalised additive models, decision trees, etc.

3. In Analytics, develop technical skills

With regard to instruments in the analytical industry, before the open-source revolution took the industry by storm, SAS and SPSS were common. Open Source resources such as R and Python are the next big thing and spending time on them will make sense.

To learn both R and Python, there are enough resources freely accessible on the internet. Python is intuitive for individuals with coding abilities in object-oriented languages like Java. But when it comes to statistical modelling, R is the best tool (personal opinion) and it is also the favoured tool in academia.

The initiation course at R at datacamp.com can be a beginning point for an absolute novice. But the best way to learn about this app is by doing so. So, I propose that the available codes should be replicated and checked on some dummy data sets to understand what’s going on.

Also, when one appears for their interview, a working understanding of SQL with advanced MS Excel / VBA skills will serve as a differentiator.

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4. Read up on Data Science market applications

Given that data science is not just a matter of methodology, it would be very useful to understand its commercial applications and to be aware of numerous effective use cases as well.

This will help to see the broader picture and also allow it well prepared to consider what kind of approach fits a specific business question.

For example, how market basket analysis is used by retailers to group goods, how cluster analysis can be used for consumer segmentation for a new product launch, how logistic regression can be used to detect banking fraud.

5. Participate in different competitions in data science

Training, practise and practise will be the last but not least. By competing in different events, one way to do it would be.

Also, it might be helpful to explore the forums with like-minded data science enthusiasts.

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Finally, one needs to protect against complacency even though one has had a break in the data science industry. There is something new to learn every day about the way technology is evolving and the analytics sector is expanding!

My advice to you is to be open-minded and think outside of the box while you are looking for a career in data science. It will give you a competitive edge in your career in data science.

Bio: Shaik Sameeruddin I help businesses drive growth using Analytics & Data Science | Public speaker | Uplifting students in the field of tech and personal growth | Pursuing b-tech 3rd year in Computer Science and Engineering(Specialisation in Data Analytics) from “VELLORE INSTITUTE OF TECHNOLOGY(V.I.T)”

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Career Guide and roadmap for Data Science and Artificial Intelligence &and National & International Internship’s, please refer :

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