How to Start a Career in Data Science

How to Start a Career in Data Science

With the advent of Industry 4.0 technology continues to widen its horizons. To match pace with the fastly changing dynamics of the current technological scenario one needs to constantly upgrade their skills and upskill themselves. A new buzzword that has become synonymous with technological advancement in our times is Data Science. Millions of professionals today are dreaming of starting a career in this domain but struggling to find the right path. If you are also eyeing a career in Data Science but don’t know where to start, keep on reading as we will try to break down the path to a successful Data Science career for you.


What is Data Science?


Data science is an integrative field which mines and extracts knowledge from data by the help of scientific methods, processes, algorithms, and systems. It is a wide subject which works on the concepts taken from Mathematics, Statistics, Computer Science & Information Science. It is used to understand and analyze data. 

The image represents the five stages of the data science life cycle: Capture, (data acquisition, data entry, signal reception, data extraction); Maintain (data warehousing, data cleansing, data staging, data processing, data architecture); Process (data mining, clustering/classification, data modeling, data summarization); Analyze (exploratory/confirmatory, predictive analysis, regression, text mining, qualitative analysis); Communicate (data reporting, data visualization, business intelligence, decision making). Source:



What are the Educational Requirements to Start a Career in Data Science?


Data Science is a technically evolved field which requires in-depth knowledge of certain core subject-matters. Approximately, 88% have at least a Master’s degree and 46% have PhDs. But, the most basic requirement in most cases is having a four-year bachelor’s degree in either of the following streams: 


  1. Computer Science
  2. Statistics
  3. Physics
  4. Social Science
  5. Mathematics
  6. Applied math
  7. Economics

To progress towards a senior profile or in order to gain advanced leadership position in the industry a Master’s Degree in data or related field is mandatory. There is a huge demand for Data Scientists in almost every industry today, therefore gaining experience in an industry (example: business, healthcare..,) of your choice can also help you build expertise in the domain.



What are the Technical Requirements to become a Data Scientist?


1. Learn R Programming 

Learning R today has become imperative for any budding Data Scientist. You can explore multiple free or paid resources to learn R. To solve every problem of data science one should learn & master R. 43% of data scientist use R programming to solve statistical problems.


2. Learn Python Coding  

One of the major programming used in Data Science is Python coding. As surveyed by O’Reilly 40% of the people use python coding in Data Science processes. Due to the easy creation of datasets and smooth searching of any type of dataset on google python is used. It can effortlessly take different formats of data and can simply import SQL tables into the codes.


3. Explore Hadoop Platform 

With 49% rating in the LinkedIn job, Apache Hadoop is the 2nd most imperative skill for a data scientist. When the space of your data exceeds the system memory and you need to send the data to several servers, Hadoop is used to handle the time of crisis. It swiftly conveys data to various points on the system. It is used for data filtration, data exploration, data sampling & summarization. 


4. Read More about SQL Database/Coding 

Structured Query Language is a programming language which carries operations like add, delete and extract data from the database. One needs to be proficient in SQL as a data scientist because it helps to access, communicate & work on data.


5. Explore Apache Spark 

It is the most trending big data technology worldwide. It works the same as the Hadoop but Spark caches its computations in memory. It also helps in running complicated algorithms and also helps in handling unstructured data sets. 


6. Think about Data Visualization 

It is a tool by which any complex result from the result could be converted into a simple language which will make it easy to comprehend. It gives the industry to work directly with the data.


What are the Non-Technical Requirements to become a Data Scientist?


i. Work on your Communication Skills

To explain and communicate the idea and technicalities of data science one needs to have good command over their communication skills. Communication is a wide area and it does not involve only speaking or interacting with the other person but it also includes their writing skills, sharing ideas amongst colleagues and peers. To build your communication skills interact with peers and seniors about industry-trends and changes, you can also explore online courses and training as per your needs. 


ii. Teamwork is the Mantra

To find a place and fit yourself in a team is a giant task. Teamwork is an irreplaceable important skill that one should have. When you work in a team you come up with more new and innovative ideas. So to have good teamwork one should always be open to healthy discussions and interaction with the team members.


iii. Build Problem Solving Abilities  

One of the crucial skills that every company looks for in every candidate is their ability to make their way through problems and complexities they might face professionally. How you approach a problem and how well-adept you are to come up with a solution will determine your future success at any organization. Therefore spend some time in exploring blogs, forums where industry professionals discuss project challenges and the approach they applied to overcome them.


Steps to Initiate a Strong Foundation in Data Science


a) Talk to people of the same field


To get insights and a good understanding of data science there is nothing better than talking the person who has experienced in the field. Attend various conferences, workshops offline or industry webinars hosted online by companies. You can also explore Data Science mentorships offered by various companies. It will help you network within the industry and keep a tab on new trends and changes.


b) Browse the Job Market


It’s never too early and it is never too late. So to have a basic idea regarding the job market of data science will help you choose your path and role in data science as it has various jobs and specializations. Therefore, research more on the job market of data science to be in sync with the trends and needs of the industry.


c) Start with a Project


There are so many tools and resources available on the internet which will help you in making your data science project when you are still a beginner in the field. This will help you develop an interest in the field and will also help you in knowing whether you like data science as your career option or not.


Mistakes to Avoid During the Journey


a) Cramming up only Theory

There is no harm in having theoretical knowledge but not applying it into the practical world is certainly the biggest mistake. Don’t trap or occupy yourself with so much theory as it’s not easy to remember every concept until and unless you don’t try it yourself practically. Cramming up too much theoretical information without any practical knowledge is not useful in the long run and might leave you feeling demotivated which is not an ideal situation.


b) Trying to Code each & every Algorithm from Scratch 

The most important factor in learning data science is to how easily we implement the algorithms in the current problem to get a perfect solution. It is good to implement some algorithms yourself to clear your basics but not all of them, as it will just waste your time in implementing an already existing algorithm.


c) Jumping up the Ladder too Soon

When you have just started your journey from start so why to immediately stop? One should never jump onto advanced topics of data science when you have no basic knowledge of the field. To reach the destination one has to take one step at a time so that he doesn’t trip or fall. Therefore, it’s better to focus on the basics and then on the advanced versions. 


Essential Tips


a) Join a Course 

To get clarity of the field one should join any course which covers the basics of the topic and involves the practical application of the subject also. As the demand for data scientists is increasing day by day so, there are innumerable courses available which will help you learn everything related to the course. To get extraordinary results you have to put in your efforts.


b) Choose a Language/ Tool  

To avoid deflection and disturbance during your journey choose one language or tool and stick to it. Start with any language which you are familiar with and learn the concepts and how it is used in data science ideas are implemented through it. 


c) Join a Peer Group 

To keep yourself motivated and excited about your field join a peer group. Joining a peer group will help you explore and learn about various aspects of data science. This will help you exchange several ideas and will help you know various opinions on one topic and come up with best the conclusion. 


d) Choose the Right Resources 

Never be satisfied with the knowledge you have, crave for more. Look out for every source of knowledge you can find on data science. But there are many sources available, read their reviews and mostly focus on blogs written by educational sites and by data scientists themselves. These keep you updated with new researches and advancement in the field.



As the world continues to generate staggering amounts of data, there is a huge demand for data scientists. You just need to learn and apply your knowledge in the practical world. Nothing in this world comes easy!  For a successful career in data science, you need to put in your time and money. Follow all the steps mentioned above to have a bright career in data science and avoid all the mistakes stated in this blog.



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