Data Science Guaranteed Placement ll Placement Process ll DataTrained


Things Happen.

The Conventional  Approach to Learning & Placement Process

Pick a Course/Tool and start learning

Resume Preparation

Search a Job

This approach doesn't work anymore

DataTrained  Approach to Learning & Placement Process

Choose Your Industry

Select 5 Target Positions

Select Top 5 Skill Profiles/Tools

Write Your Future Resume

Now Start Your Study

Invest Your Skills in end-to-end Projects like the ones on Kaggle

Achieve Milestones and Apply for the Job

Interview Preparation

Land a Job!

Nine hacks to become a Data Scientist in 2019



You all want to become a Data Scientist?

Well, everyone does.

After all, it’s the highly paid job of our times. We are sharing here the recent Salary disclosure made by Glassdoor in India.

Pick a course/Tool and Start Learning

Resume Preperation

Start Searching



for a Job

But is it that simple to become a Data Scientist?


We at DataTrained has created nine hacks to become a data scientist. This concept has been created by one of our senior Faculty member who is a self-made Data Scientist.


So, let’s start with a very basic definition of Data Science:


“Data Science is an art to cleanse an unstructured data and draw meaningful conclusions so that future occurrence of a particular pattern or event is predictable".


What exactly does a Data Scientist do?


If you ask 10 different data scientists about their job profile, you will hear 10 different work profiles and different skills or tools they are working upon. The hard truth is that Data Scientist profile is at a very nascent stage. Being a young discipline, different companies have created their own job profiles as per their specific needs. Likewise, they use different tools to analyze their data.





There’s so much content available online that it’s obvious that people tend to be lost. To add this, various institutions are providing different courses both in the online as well as offline data science training space. To name a few:

“Python, Spark, SAS, R, Hadoop, Big Data, Hive, SQL, Azure etc.”



This confusion compels to ask: “What should I learn and what not?”


And in any case, if we start learning with any one tool, after a little while, one starts to wander, there’s so much to learn and if I am even following the correct path that will lead to a job?


In fact, most of the job positions need only a handful of main skills to work upon, but these skills vary from industry to industry. For example, Data Scientists at KPMG don’t use SAS, while in Royal Bank of Scotland, they work on SAS all the time.





The solution lies in how we are approaching for a Data Science role. Your own Personalized Road Map could be a perfect answer.

The way India’s educational framework work, “greater emphasis is following the traditional path of learning and then search for a job”


Let’s first discuss this traditional path which almost everyone is following right from the ages and are still continuing with the same!



So, first, we pick a course and then join an institute. Once we are through to that learning, we create our resume and then start searching for a job. But we at DataTrained has altogether a different approach. Let’s take this in detail and here we will be discussing nine hacks to become a Data Scientist in 2019.


1. Choose your industry

The first task we need to do is to select the industry into which we can fix our own domain expertise. Based on, some popular industries for Data Scientist role are:


  • Biotech and Pharmaceutical

  • Marketing and Advertising

  • Banking and Financial Services

  • Internet and Tech

  • Media and Publishing

And there are more of others as well.


Visit and explore the number of opportunities in each of the above Data Science industries by different cities of your choice. Once you are done with the number of opportunities by each city and each industry, select the top three industry which has got the maximum number of opportunities in your city of choice.


For example, I want a job in Delhi NCR and Bangalore. So, I would search all the Data Science jobs in Delhi NCR first for each of the industries above and once done I will then again search all the Data Science jobs in Bangalore for each of the industries.


Then I will need to select the top three industries in both the cities in terms of the number of opportunities available. Let’s say, for instance, I have got these three top industries:

Delhi NCR

  • Marketing and Advertising

  • Banking and Financial Services

  • Media and Publishing


  • Banking and Financial Services

  • Internet and Tech

  • Marketing and Advertising

Now, select the common industries from the above two cities. So, we have now shortlisted:


  • Banking and Financial Services

  • Marketing and Advertising

Now, you will need to select one industry from above which fits into your criteria.

  • If you’re a fresher with no domain expertise, select the industry which aligns more to your educational background.

  • If you’re an experienced person, select the data science industry from the above two which matches the most to your domain expertise.

  • For example, if you’ve got experience in banking, you will find that the Banking and Financial Services industry would be an ideal one for you.



2. Select your 5 Target Positions

Once you’re specific about the industry you’re going to follow, visit and search for the Data Scientist positions in that particular industry. Try different search keywords like a data scientist, data analyst, machine learning engineer, quantities analyst, and business analyst.

Now check the job descriptions and see the requirements those positions are asking for. You will end up with too many options and eligibility.

Don’t lose heart here. Rather try to eliminate the positions which are hard to crack:


  • Some of the positions would ask for Ph.D. Eliminate that one because it’s unrealistic to target that position unless you have plans to go back to school.

  • Likewise, if you’re not a B.Tech or BE, eliminate those positions which ask for that technical background.

  • Also, eliminate those positions which don’t excite or thrill you enough.

You need to select your 5 data science target positions here. I have selected the following top 5 :

Well, you must be thinking, “what bullshit I am talking about?


Obviously, yes these positions will be closed by the time you’re skilled enough to be hired.

But the motive is to identify your targets first and then hit that target with a bang.

So, once you all have found your 5 target job positions, download and save their complete job description.



3. Make your Skills Profile


So, we have now selected the industry to target and also the 5 target positions. The current part is all about your Skills Profile. The rule is to write down the skills that appear in at least 2 of the 5 target job descriptions. Let’s do it. I have listed the job descriptions of all the 5 target positions I have selected above.

So, we can clearly see, Python, SQL, Regression have been asked for in at least two of the job descriptions.


If you now go through what we all have done until now, we have out of a dozen of industries, positions, skills have selected our own industry and have come up with 2 skills which are high in demand and thus mastering these skills will ultimately lead to a job.



Haha….not exactly.  Still, there’s a long way to go.



4. Write your Future Resume

So far, we have discussed how to select your industry, then selected top 5 positions and then came up with top 2 skills that have featured in at least three of the five jobs descriptions we have shortlisted earlier.

It’s now our turn to write our future resume. Our resume needs to be impressive yet realistic. Simply pretend that you’re applying for those 5 job positions tomorrow and you have acquired those skills.

Make the best version of yourself as a Data Scientist.

You can add some of the ideas:


  • List down your skills in details

  • If you’re currently just afresh out from your college, list down your projects or internship that have some curve of data science into it. Add those classes as well wherein you learned about analytics or data science or some sort of automation in your college.

  • If you’ve got some sort of experience, including the projects that had analytics/data science/automation/robotics into it.

  • Aim for some kaggle projects that can add further some weight to your profile

“Your future resume will act as a concrete goal and will also inspire you to achieve your milestones


Resources : Learn how to write a Perfect Data Science Resume


5. Start learning

The time has come now to focus on your future resume and the skills you need to accomplish. It’s like you have got a Google map along with the start and the end point rather than going directionless to achieve your goals.

The best part is that you have got a definite path to follow. The only thing you need is a firm resolve to achieve your goals.

Try to follow these 3 simple ways :


  • Start from learning a programming language rather than starting with stats. The reason here is that when you will start learning stats, later on, you will be able to apply your programming skills to it which will make your job much easier. That will further lead in solving real-life projects.

  • Make a plan and set a time limit within which you need to learn a particular skill. Setting a deadline will help you in not losing concentration. But please don’t make unrealistic deadlines.

  • Get your hands dirty in practicing that skill to solve a problem as much as you can. For example, if you’re learning SQL, then try to get a database, import it into a database server and practice writing queries. Try to get on to this and other multiple datasets as much as you can.

  • Once you’re done with the above three steps, go back and try to revise the concepts and the skills you have learned so far.

The Last piece of advice in during the learning phase of “Nine hacks to become a Data Scientist in 2019 :

“Don’t get lost or lose your heart if it’s taking time. Make yourself comfortable within your own pace and learning capability “.


Keep reading this article. We will soon come up with the next 4 hacks in this series of becoming a data scientist.