How To Become A Data Scientist?

How To Become A Data Scientist?
How To Become A Data Scientist

It’s a common scenario for almost all the tech professionals to talk about switching to “Data Science career” and this newfound love is drastically changing the mindset of computing world professionals. However, we feel that it isn’t getting justified as there are discrepancies in existing skills of the professionals to the industry standard skillset.

So, why is this happening and what should you do before thinking of a Data Science career?

To address this concern, professionals and young aspirants should analyze some important qualities before diving into this field. Before you plug into a Data Science ocean, be honest and shoot some questions to yourself.

1) Do I like statistics and programming?
2) Will I continually update myself with the latest Data Science techniques and technologies, if I enter this field?
3) Am I just shifting to this career only to enjoy those sweet paychecks?
4) Am I fine, if I get any of the job roles such as Data Analyst, Business Analyst, etc.?

Today you join a course, and tomorrow you get a certificate …..Tada, you are a Data Scientist now. This is not how it works. Data Scientists should have some basic liking towards Statistics and should start thinking in “Data Science way”. Leverage your skills and interests to find out whether you have a good understanding of basic mathematics and statistics or even willing to revise them will help you to lay a strong foot in Data Science role. Remember, the path for becoming Data scientist will take its own time but your passion and interest is what drives the journey ahead.

Let’s jump into more exciting topics that can help you to become a Data Scientist

1) Develop your Math skill:

We would have probably skipped the toughest Algebra equations and calculus problems while we were in school, but it is time to revise your 10th-grade math books. The essential topics that you need to concentrate or familiarise in mathematics are Linear Algebra in which specifically, Matrix algebra and eigenvalues. In calculus, Derivatives and gradients are essential topics to be looked upon. Since Calculus is one among the important keys to unlock the ML applications and you should be able to calculate derivatives and gradients for optimization. In Linear Algebra, the study of vector spacing and linear mapping between these spaces is heavily used in machine learning. As you start exploring in Data Science career, you will understand that these core mathematics subjects comes handy. Depending on your Data Science role and the nature of your work, you may need to dive deeply into some of the complex details of linear algebra and calculus.

2) Do some statistics and Probability:

A data scientist is someone who is good at statistics and probability, “A die is rolled, find the probability that an even number is obtained” Does this question sounds familiar? Statistics is one of the branches of Mathematics that deals with analyzing and interpreting data. In specific, the probability is one of the topics of statistics that measure the likelihood that an event will occur. A lot of Data Science goes into analyzing the likelihood of occurrence of an event. Be it measuring the odds of an ad getting clicked or a movie being chosen to watch, and we need to analyze the probability of the success and the failure. Look for online books of pdf versions that you can download or start watching some YouTube videos to understand the basics of statistics and probability. DataMites™ ‘s “Business Statistics” course is the best way to learn and equip ourselves with the practical implications of statistical techniques and tools used. This course is developed in a case study based methodology which helps in detailed understanding and gives real-time experience. The link for “Business Statistics” course for your reference is

3) Start loving “Big Data”:

Data Scientists are always referred to as Data wranglers or Data jugglers, they should always work with humungous volumes of data which is structured or unstructured. Computations are often not simple with these data, and you should equip yourself in analyzing them using big data software such as Hadoop, MapReduce, or Spark. There are many free courses available online and youtube videos that help you in understanding the Big Data.

4) Knowledge on Database Management:

Every moment, huge volumes of data are pouring in, and the organization has employed different database management software such as MySQL or Cassandra to store and analyze them. Equipping oneself with the right amount of knowledge of how to work on DBMS comes handy in your Data Scientist role.

5) Programming Knowledge:

Learning the language in which data communicate with one another is really essential for a Data Scientist. The Data Science community mainly prefers R and Python languages. Other languages such as Julia and Matlab are used as well, but R and Python are renowned in this space. We have already discussed in detail in our previous post on “Which is the best choice for a Data Scientists to learn on, R or Python?” and the reference link is

6) Master yourself in Data Munging, Visualization, and Reporting:

When the raw form of Data is presented, no one could able to understand or analyze. Data munging is a crucial process of refining the raw form of data into an easy to study, analyze and visualize form. Since the managerial and administrative decisions heavily rely on analyzed data results, visualizing and presenting the data in an understandable format becomes a necessary skill for a Data Scientist.

7) Get a hang out of it through “Real Projects”:

Theory is important but getting your hands on real project will help you in practical guidance. A data Scientist will not only equip himself/herself with knowledge gained from the book but also look for data science projects to invest his/her quality time and gain a better working knowledge. DataMites™’s “Certified Data Scientist” course is an IABAC™ accredited course that is a combination of theoretical knowledge gained from the experts and real-time knowledge gained from the projects. Also, they get unlimited access to Data science cloud lab for practice and the reference link is

8) Join the Data Science communities and groups:

Building out your network and attending the conferences will expand your connects. Enrolling yourself in online groups and communities also proves to be helpful as you can participate as well as gain knowledge on the latest updates happening in Data science field. Some conferences host workshops which helps you to learn new skills. Furthermore, there are many online resources help you to plug yourself into the data science community through newsletters, Kaggle, and KDNuggets. In all of our DataMites™ courses, candidates get all these features for free.

9) Sharpen your communication Skills:

You are definitely strong in your communication skills, but more often you will be put across in a situation where you need to explain those heavy chunks of analyzed data to management people in order to make informed business decisions. Hence, you need to become better communicators and utilize the language of the business to engage business stakeholders by capturing their attention emotionally as well as logically.

10) The Job Search:

After successfully completing the learning and practicing part, you will start applying for jobs. If you have taken up a DataMites™ Data science course, then this part seems to be pretty simple as we already have career counseling as part of our course. In addition to this, you can take up Kaggle challenges and write Data Science blog articles. Freelancing also helps you, try enrolling in some sites like Upwork, Freelancer and pick some projects and make your presence known to the recruiter. Update your CV, LinkedIn, and Github to reflect your new skills. Search for interview questions and go through lots of practice problems so that you will know how to answer them and this process also eases out your initial job hunt phase. Familiarize yourself with a wide variety of topics then check out startup jobs that help you to get an entry into this most exciting data world.

You cannot become a Data Scientist in overnight, and it is all about learning through practice and keeping up your data curiosity throughout the journey. While we have numbered the things that will help you to make a become a successful Data Scientist, still you need to travel at your own pace and time. If you need more time to grasp the fundamentals, you better take that. You are not going to miss any bus, don’t hurry up, instead make a perfect aim to hit the job that steers you way ahead of everyone towards excellence.

Upskill your Data Science knowledge with DataMites™:

Every year, Glassdoor publishes its list of the best jobs in America, and according to its 2018 report, Data scientist grabs the best job title for the third consecutive year. The high earning potential, hiring demand, and job satisfaction are the essential aspects that have made Data Scientist job to the limelight situation and made it stay over there.

DataMites™ is one of the leading global professional training providers in Data Science, Artificial Intelligence, Machine Learning, Data Mining, and Deep Learning. We are training aspiring candidates with hands-on training that helps them to gain the required skills and a complete understanding Data Science and its related technologies. All our courses are perfectly aligned with the current industry requirements and give exposure to all the latest techniques and tools that help professionals to achieve in-depth knowledge and enhanced skills. It is the best time for you to get on board with DataMites™ and join the long list of candidates who have successfully excelled in their career. For more details, please visit