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How to get a Better Job in Data Science?

How to get a Better Job in Data Science?

Job hunting in itself is a very daunting task, almost all of us have experienced the anxiety over acquiring enough knowledge, and getting through the challenging task of landing ‘The Dream Job’. Add to that the desire to get into the ever-evolving world of data science, let’s admit, it can be very intimidating, more so at the entry level. Preparation is the key to success, it is what takes your dependency away from luck and progresses towards definite success. This blog is a small effort in the direction of laying down the guide which could perhaps clear the vision of people, thinking of starting their career in data science or analytics. It is aimed at assisting you in decoding the framework which can help you to learn the relevant skills in data science.

  • Clarity in Goal

First thing first, be clear what do you need to advance in, the field of data science is very vast and varied. Data analyst, Data Engineer, Machine Learning Analyst, Data Architect, Data analyst, or the revered data Scientist, as you can see there are many options available in the field of data science. The choice also depends on factors like your academics, interest, work experience, etc…, for example, if you are a software developer, getting into data engineering would be the most obvious choice. Hence clarity of though is imperative to avoid confusion and fluctuation from one vertical to another. You can gain clarity by talking to people from the industry, career counseling, conduct your own research and choose the role that suits your interest and field of study. Do not make a hasty decision.

  • Upscale with the Help of a Course

Once you have decided on the role, assess what lacks and accordingly pick up a course that will help you reach the goal. Taking up a course not only adds to your skills but it will help you network with like-minded people, further making it that much more possible to land your dream job. Data science is considered the coolest field to work in, so clearly, besides the high demand, there is also a high number of applicants, hence ensure you are skilled and networked enough to grab the opportunity when you see one. 

  • Pick up a Tool/ Language and practice

Breaking into the data science industry is tough, hence it is important that you have an understanding of what lies underneath data science. Statistics, Machine Learning, Software Engineering, Math, Data Mining, Data Mugging, Probability, Predictive Analytics, Sentimental Analytics etc…, are some areas that you need to have knowledge in. Fluency in Programming Languages like R and Python, GUI tools, and coding is a must have.

  • Practical Application

Even when undergoing a course, ensure you learn with practical experience as opposed to theory, this way you will not only understand the concept but will also have a sense of applicability. A tip is to practice what you learn in theory as a continuous process. Continue to research and read in the area of interest, blogs and white papers by the most influential data scientist can be a starting point.

  • Soft Skills

In the effort of acquiring all the technical know-how do not overlook sharpening your soft-skills. A person working in the data science field needs to possess a combination of technical, analytical and presentation skills. It is not only required from you to be a creative problem solver but to be able to communicate the solution to a non-technical audience to get their buy-in.

Data science is an evolving field, and there are no signs of it slowing down or becoming redundant. Perks such as big pay packs, and job security aside, you also get to make a big impact in the organisation, by solving complex problems. Overall it is indeed a rewarding career choice. So to advance in this field take the right steps to refine your skills to inch towards securing the job of your dreams.


  • January, 11th, 2018
  • Posted in

Industry Report: Data Driven Innovation: Disruption Vs Optimization

Data-Driven Innovation

Any successful innovation is a result of a good measure of disruption. Over this, you must add a very generous helping of talent and a lot of creativity to go into the mixture and finally a splash of intuition and you’re good to go.

While this may be the probable recipe for any innovation, but does it also happen to be the recipe for a successful innovation? That seems to be a different story altogether, because usually the one thing that any successful innovation depends on, is whether it meets or exceeds the assigned business goals.

It also finally boils down to the inclusion of big data into the mix. It is this ingredient that would help you ensure that your innovation is very successful. This is how you will figure out the coveted je ne sais quoi for your success. So one must remember to always get out their big data analytics tools and crunch some data in order to get amazing results.

Innovativeness, responsiveness, and resilience happen to be the trifecta of deriving business agility. These happen to be the core business drivers, which when put together, you have a great picture of how businesses deal with change.

Analysing the information that you have gathered or that is at your disposal, will help any organization deal much better with change as a result of a thorough optimization process. All a professional is required to do is gather all the data that is available on anything that they are doing, crunch all the numbers and go on to make recommendations on what all changes need to be made in order to ensure the betterment of the process.

While data analytics may be supremely efficient in making human processes more efficient, it has also experienced one flaw. That flaw as surprising as it may sound is humans. This would be more clear as the processes become complex. For example, when the data grows, it inadvertently means that the need for analysing the same also grows. But the downside here is that people, as a rule, have a limited attention span. So when it comes to analysing information, this attention span can prove detrimental in the processing of the information. So in a way no matter how good and exemplary your data analytics tool is in giving off results, it is redundant if there happens to be no one to read them or even understand them.

This is the reason why in order to eliminate the weakest link, there are many organizations which are trying to establish totally automated feedback loops. For instance take a firm which is responsible for managing giant amounts of data, from airports to factories and data centres. While the traditional approach to handling any kind of glitches here would be, drawing up of a number of reports, based on mathematical formulas and adjust the maintenance schedule accordingly. On the other hand, if there are mechanisms that would know beforehand when the glitches would occur and correct them way before happening, this would be a better arrangement.

This is why Data Analytics is becoming the most sought after profession, with many data aspirants trying to get professionally trained from Imarticus Learning.

Enjoyed reading this report? Read more here:

The Future of India in the Field of Big Data Analytics

How Credit Managers can optimise the use of Big Data Analytics?


  • March, 25th, 2017
  • Posted in