How To Switch To Data Science From Your Current Career Path?

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Last updated on
07th Sep, 2022
20th Feb, 2021
How To Switch To Data Science From Your Current Career Path?


A data scientist needs to be well-versed with all aspects of a project and needs to have an in-depth knowledge of what’s happening. A data scientist’s job needs loads of exploratory data research and analysis on a daily basis with the help of various tools like Python, SQL, R, and Matlab. The life of a data scientist involves getting neck-deep into huge datasets, analysing them, processing them, learning new aspects and making novel discoveries from a business perspective.

This role is an amalgamation of art and science that requires a good amount of prototyping, programming and mocking up of data to obtain novel outcomes. Once they get desired outcomes, data scientists move forward for production deployment where the customers can actually experience them. Every day, a data scientist is required to come up with new ideas, iterate them on already built products and develop something better.


One of the most in-demand industries of the modern world is Data Science. Year on year, the increase in the total data generated by customers is huge, and has now almost touched 2.5 quintillion bytes per day. You can imagine how large that is! For any organization, customer data is of the utmost priority as with its help, they can sell their customer the products they want, by creating the advertisements they would be attracted to, providing the offers they won't reject, and in short delighting their customers every step of the way.

The money factor has already been mentioned by me earlier. A Data Scientist earns about 25% more than a computer programmer. A person with a die-hard passion to work on large datasets and to draw meaningful insights can definitely begin their journey in becoming a great data scientist. 


Data science skill sets are in a continuous state of fluctuation. Many people are confused with the thought that if they can gain expertise in 2 - 3 software technologies, they are well equipped to begin a career in data science and some also think that if they just learn machine learning, they can become a good data scientist. It is an undeniable fact that all these things together can make you a good data scientist but having only these skills will definitely not make you one. A good data scientist is a big data wrangler, who has the ability to apply quantitative analysis, statistics, programming and business acumen to help an enterprise grow. Solving just a data analysis problem or creating a machine learning algorithm will not make you a great enterprise data scientist.

An expert in programming and machine learning who is not able to glean valuable insights to help the growth of an organization cannot be called a real Data Scientist. Data scientists work very closely with different business stakeholders to analyse where and what kind of data can actually add value to the real-world business applications. Data scientists should be able to discern the impacts of solving a data analysis problem such as what is the criticality of the problem, identifying the logical flaws in the analysis outcomes and must always ponder on the question- Does the outcome of the analysis make any sense to the business?


The first and the foremost step is to understand the urgent need to change your path to Data Science, because if you have doubts in your mind then it would be hard to succeed. This does not mean that you need to quit your job, sit at home and wait for some company to hire you as a data scientist. It means that you need to understand your priority and have to work and develop the required skills to hone your knowledge in that field, so as to excel in the career path you tend to follow next.

A data scientist must be able to navigate through multifaceted data issues and various statistical models, keeping the business perspective in mind. Translation of the business requirements into datasets and machine learning algorithms to obtain value from the data, are the core responsibilities of a Data Scientist. Moreover, communication plays a pivotal role in data science as well because through the entire data science process, a data scientist must be able to closely communicate with the business partners. Data scientists should work in collaboration with top level executives in the organization like marketing managers, product development managers, etc. to figure out how to support each of the departments in the company to grow with their respective data driven analysis.

Data Science requires three main skills :-

  • Statistics: To enter the field of data science, a solid foundation in statistics is a must. Professionals must be well-equipped with statistical techniques, and should know when and how to apply them to a data-driven decision-making problem.    
  • Data Visualisation: Data visualization is the heart of the data science ecosystem as it assists to present the solution and outcome to a data driven decision making problem in a better format to the clients who do not belong to data analytics background.
  • Data visualization in data science is challenging as it requires finding answers to complex questions. Before stepping into this field, a lot of preparation in visualization needs to be done.
  • Programming: People often ask themselves “Do I need to be a BIG time coder or an expert programmer to pursue a lucrative career in Data Science?” The answer to this is probably no. Expertise in programming skills can be an added advantage in Data Science, but it is not compulsory. Programming skills are not needed in big data applications but are rather needed to solve a data equation that is time consuming when solved manually. If a data scientist can figure out what needs to be done with the dataset, that would be enough.


Data is the essence of Data Science. Data Science revolves around big datasets but many a times, data is not of the quality that is required to take decisions. Before being ready for processing, data goes through pre-processing which is a necessary group of operations that translate raw data into a more understandable format and thus, useful for further processing. Common processes are:

  • Collect raw data and store it on a server. 

This is untouched data that scientists cannot analyze straight away. This data may come from surveys, or through popular automatic data collection methods, like using cookies on a website.

  • Class-label the observations

This consists of arranging the data by categorizing or labelling data points to the appropriate data type such as numerical, or categorical data.

  • Data cleansing / Data scrubbing

Dealing with incongruous data, like misspelled categories or missing values.

  • Data balancing

If the data is unbalanced, for instance if the categories contain unequal numbers of observations and are not representative, applying certain data balancing methods, like extracting equal numbers of observations for the individual categories, and then processing it, fixes the issue.

  • Data shuffling

Re-arranging the data points to remove the unwanted patterns and improve predictive performance is the major task here. An example would be, if the first 1000 observations in the dataset are from the first 1000 people who have used a website; the data is not randomized due to different sampling methods used.

The gist of the requirements for a Data Scientist are:

  • Hands on with SQL is a must. It is a big challenge to understand the dicing and slicing of data without expert knowledge of various SQL concepts.
  • Revisit Algebra and Matrices
  • Develop expertise in statistical learning and implement them in R or Python based on the kind of dataset.
  • Ability to understand and implement Big Data, as the better the data, the more is the accuracy of a machine learning algorithm.
  • Data visualization is the key to master data science as it provides the summary of the solution.


There are many institutions which offer in-depth courses on data science. You can also undertake various online courses to equip yourself with Data Science skills. As the Data Science market is growing exponentially, more professionals are leaning toward a career in this rewarding space.  

To explore some course options in data science, you can visit.


Dipayan Ghatak

Project Manager

Leading Projects across geographies in Microsoft Consultant Services.