How to Become a Data Scientist? – Hindustan Times

A decade ago, there was no such role as Data Scientist and yet, Data Scientist has become the most desired job in this century with huge demand, millions of opportunities and high salaries.  This is due to the generation of enormous business data, which has immense potential to provide valuable business insights.

A Data Scientist’s work is to gather this data from various sources, clean, transform and prepare the data then use Statistics and Machine Learning skills to extract business insights from the data. 

In this article, we shall discuss the prerequisites, required skills, proven learning approach, getting job-ready and the timeframe to become a Data Scientist.

What are the prerequisites to becoming a Data Scientist?

Data Scientist’s role is overgeneralized. In practice, a Data Scientist’s role can vary from core research work to applying data science techniques to the data for business insights.

While a data scientist, who works on core research areas, and develops algorithms and optimization techniques, needs a strong statistical and mathematical background, it is  NOT required for a typical Data Scientist, who transforms data into business insights, to have strong mathematical knowledge or programming skills. 

The majority of the data science roles require applied knowledge in the fields, Statistics, Mathematics, Data Wrangling and  Machine Learning along with the respective business/domain knowledge.

There are NO hard prerequisites as such, but it is recommended to acquire foundation level skills in core data science areas including, Python Programming, Basic Mathematics, Statistics, and Exploratory Data Analysis, before venturing into the field of data science.

What are the skills required to become a Data Scientist?

Data Science is multidisciplinary consisting of programming, mathematics, statistics, machine learning and business/domain knowledge.

Technical skills required for data scientists include:

  1. Programming – Python ( Core Python, Essential Data Science packages, etc.,)
  2. Mathematics  (Linear algebra, Probability, Derivatives, Matrices etc.,)
  3. Statistics  (Descriptive data analysis, Sampling, Probability distributions, Hypothesis testing etc.,)
  4. Data Collection and Cleaning
  5. Data Visualisation and Exploratory Data Analysis.
  6. Machine Learning Modelling and Model Deployment

Non-technical skills include:

  1. Analytical Curiosity – Question everything.
  2. Data Intuition – Making sense of Data
  3. Strong Business understanding
  4. Effective Communication

It’s the non-technical skills that differentiate a good data scientist from an average data scientist.

What is the best approach to mastering data science?

Well, there is no one best approach to learning data science as it varies based on the learner profile. For someone starting from scratch, it will take on average 6 months to gain enough data science skills

DataMites®, a leading Data Science institute, successfully trained more than 50,000 learners and helped thousands to transition to a data science career. The DataMites® adapts one of the best approaches to learning Data Science, which essentially has 3 phases.

Phase 1: Learn Data Science Key Skills

The first is to start with gaining data science key skills from basics to advanced levels. This is the most demanding phase as you need to put rigorous effects into learning new concepts, practice and learn to apply.

Data Science Fundamentals ⇒ Python ⇒ Statistics ⇒ Exploratory Data Analysis ⇒ Data Visualisation ⇒ Machine Learning ⇒ Model Optimization ⇒ Data Science Model Deployment

Phase 2: Practice Data Science Capstone Projects

Data Science Capstone/learning projects are very important in mastering the new concepts you learned. There are many sources to practice projects and is the most popular one.

It is advised to practice at least 10 projects in each machine learning type, classification, regressions, clusterings, and recommendation.

Phase 3: Real-time  Data Science Project

Unless you add value to the business with real-time projects using data science and machine learning, you can’t be called a data scientist.  The real-time projects could be from a small business, a Proof of concept project for a large client, a start-up project or a product idea. 

This is very important to appreciate data science as a field and understand the value it could add to the business. Also, this will be of significant importance in your profile when looking for job opportunities in Data Science to take up real challenges.

How to get a Data Science job-ready?

Getting job ready requires more than gaining data science skills. 

Job role:

The Data Science field has many roles from technical, functional, business and leadership roles.  The first step is to select a suitable Data Science job role based on your profile and interest. 


The resume does the first talking, so it is crucial to fine-tune the resume by  prioritising relevant and important, highlighting technical skills, mentioning project details with your specific contribution etc., 

Mostly, the first level of filtration happens in an automatic manner with software bots so it is important to keep the right keywords matching job descriptions.

Interview Preparation:

Job Interviews require whole different preparations from presentation, communication, brushing the concepts etc.

For example:

  1. Though it is not so important to remember the key concepts, definitions and terms in practical work, it helps in job interviews to more confidently communicate and demonstrate your skills.
  2. Participate in mock interviews and learn from the experience.
  3. Glance frequently asked interview questions.

Simply, It is important to take time to prepare for the interview. 

Job Application Strategy:

Approach the job market with a good job application strategy for better results.

The job application strategy includes job application channels, selecting the right filters to find the job posting of your interest, application frequency, tracking, etc.

Finally, learn from failures:

Remember, most people land their dream jobs after multiple failed attempts. So, keep up a positive attitude when you fail in an interview and learn from your mistakes. 

There are many institutes providing Data Science structured courses with various learning options. It is very important to pick a course providing live sessions, proven mentors with industry experience, and, if possible, internship and real-time projects.

DataMites®, a leading data science institute, offers a  7-month Certified Data Science  (CDS) Course accredited by the International Association of Business Analytics (IABAC®).

The Certified Data Science Course from DataMites® is bundled with internship options with AI companies along with real-time projects mentored by industry experts.

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