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Can Someone Become A Data Scientist In 6 Months

Can Someone Become A Data Scientist In 6 Months

Rohit Chatterjee
Data Scientist

Data science is a field which has a considerable expansion, and it is a pretty tough job to build a career as a data scientist. It takes years of preparation along with certain degrees and educational background to understand what data science really is and how it works. The aim becomes more onerous for those who are looking to switch to data science from an entirely different background. In the domain of data science, there are many individuals who have successfully added the designation of a data scientist to their resumes. But there are some who aspires to do so in a very short span of six months which may not be possible. Ambition must be highly inclined, but it also has to meet realism.

In this article, we will share with you a number of reasons why it is not possible to become a data scientist in just six months irrespective of being highly determined and putting massive efforts. 

Understanding The Depth

The first step before beginning the preparation is to self evaluate the current scenario where the candidate should be clear about where he or she stands. One should start with rudimentary elements that are essential to becoming a data scientist and these go back to school days. To understand data science, it is very crucial to have mathematics as a subject in high school with a sound knowledge of chapters like calculus and algebra since these are the very foundations on which data science is built and functions. Although it is not absolutely essential to have these subjects in school but to become a data science, one needs to know these subjects which usually takes a semester or a period of four to five months to learn completely. Not to mention, learning alone is not enough since a lot of practice is a must to have these subjects on your fingertips.

Learning The Core

Like mentioned before, the data science universe is vast, and there is so much to learn. The learning in data science never comes to an end, but it starts with statistics which are divided into two parts, such as Inferential Statistics and Descriptive Statistics. An aspirant who is aiming to become a data scientist should know the A to Z of statistics and to do so, it is imperative to have a degree which teaches statistics along with a lot of other mathematics over a course of time, not in six months. Moving on, the next elements that must be learned by an aspirant are machine learning, deep learning, time series forecasting along with programming languages such as Python, R and SQL. For example, machine learning consists of several disciplines such as neuroscience, vision, speech etc. which takes more than a year to learn. Similarly, deep learning takes up more than six months, which also requires certain programming skills along with the ability to learn Python at the same time. Altogether, the amount of learning that is required to become a data scientist cannot be done in a mere time period of six months. 



How To Learn The Core

Considering the number of online training courses and institutes coming up every day, finding the best course or institute is a challenge in itself. Since six months is a concise period, it is advisable to go for a full-time course. Although, someone with a job in hand can dare to go for the online courses. An aspirant must be able to dedicate more than 8 hours a day in order to learn data science and even after doing that, one might fall short. To have real experience in regards to projects, an aspirant can take a look at several projects that are offered by the online courses which one can implement by taking parts in different competitions to get the first-hand experience. A lot of free materials are available on the web in the form of YouTube videos, questions and answers on community sites like Quora and StackOverflow. Also, doing a wide range of projects on Kaggle will demonstrate your capabilities in data science techniques. Going back to the beginning of the paragraph, it is essential to figure out the differences between a part-time course and full-time course. Our article “How to Choose Between a Full-Time Data and Part-Time Data Science Courses” could help you in figuring out the right choice.

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From Where To Learn The Core

Now that we have discussed the elements and how crucial a full-time or part-time course can play, let’s have a look at where one should head to learn data science. These days, there’s a new institute or training institute in every nook and corner. Definitely, one should avoid joining these institutes and find the credible one. The credibility of an institute is known by the teaching faculty and experience in the field. One should always conduct a thorough research about how and by whom the institute is being run. They should connect with aspirants from previous batches to understand how the institute functions.  Direct communication with an earlier student is the best way to find out the pros and cons of an institute. Furthermore, they should ask previous students about the possibility of learning data science in six months. Just in case an aspirant wants to enrol and take the challenge, we would like to ease the woes by sharing our articles “Top 10 Data Science Training Institutes in India” and “Top 10 Data Science Training Courses in India” which might help an aspirant to zero in on a particular course or institute, to begin with. 

The Last Step

In a scenario where an aspirant might have been able to pull off a miracle by learning data science in six months, by this stage he or she is probably looking forward to their first job as a data scientist. Having knowledge about data science alone will never be enough to get a job, especially the first one when a relevant experience is missing. Hence, preparing well for the interview is very crucial. One should make a list of several questions that might be asked by the recruiter related to data science. The chances are high that an aspirant will have to give several interviews before getting an offer. It is wise to work on the questions that an aspirant might not have been able to answer in an interview, which led to the declination of the offer. In this way, the aspirant will be able to narrow down the chances of failing and understand the questions and skills which a recruiter wants from a data scientist.
The road to learning data science is a hard one and irrespective of putting enough hard work, the time period is too short for anyone to know the ins and outs of data science. For any aspirant who is looking forward to ways of cracking an interview, we would suggest having a look at our article “Interview Strategies to Land a Data Science Job in 2020” for a clear understanding of how the interview process works in the field of data science.

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