Building a Successful Career in Data Science and Analytics – Analytics Insight

by Analytics Insight

December 6, 2021

How to build a career in data science and analytics with essential requirements and skills?

Over the past decade, the availability of data and the demand for skills to be applied to data-driven decision making have skyrocketed. Further, the drastic shift in business operations and consumer behaviour, catalysed by the COVID-19 pandemic, pushed the need for data-driven decision-making into the limelight. Analytics and data science have become essential directional tools for organisations across industries and functions.

A career in data science is rewarding, especially with the increasing demand for data science professionals. Here are some benefits and opportunities this career offers:

Solving complex, real-world problems: For someone who enjoys challenges and solving complex problems, data science is a fulfilling career option. The primary task is to analyse vast amounts of raw data to find answers and insights for real-world problems. They can vary from driving sales to identifying future challenges for the business, economy and society. With the new hybrid/remote work culture, there are many opportunities to work with global organisations seeking data-driven business intelligence.

High salaries: According to data from Robert Half, the median starting salary for data scientists is $95,000, almost double the US median salary. At about $70,000, even the average salary for data analysts, an entry-level role, is considerably higher than the median salary in the US.

Low risk of job automation: Data science roles are in high demand and growing at an average of 50%, according to the Dice Q2 Tech Job Report 2020. Data scientists, especially analysts, therefore, face a very low risk of job automation. Organisations continually need human-powered decision making and observation of all data-led artificial intelligence (AI) and machine learning (ML) systems.

For a career in data science, you don’t necessarily need a technical bachelor’s or master’s degree or even experience, just the right skills and the willingness to learn. Bigger companies tend to have well-defined roles, whereas smaller ones seek more generalist skill sets.


Here are a few core data science skills:

  • Mathematics and statistics: Excellent math skills and an understanding of applied statistics are a must.
  • Big data and data modelling: Data scientists must understand working with databases and have knowledge of Apache Hadoop or Apache Spark, along with traditional methods of data processing.
  • Data visualisation: The ability to graphically represent data and to show trends and insights derived from the analysis are also important.
  • Programming: Knowledge of programming languages such as Python, Java and R is critical.

To build your resume further, you will want to add some professional practical experience. You can start with some freelancing platforms like Upwork or Fiverr, as well as search for part-time work or internships through social media and job boards. You can also write up your project as a blog post or read blogs by data scientists. Learning through new mediums like AirPlus helps aspirants gain foundational data skills. It is important to understand interest areas, such as deep learning, natural language processing (NLP), ML, and computer vision. Depending on your interest, there are many other roles available.



AnirbanRoychowdhury, VP of Human Resources, iMerit

AnirbanRoychowdhuryleads the global human resources function, and has focused on people strategy and creating a robust HR system over …….


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