What Does a Data Scientist Do? And How To Become One

Indeed Editorial Team

Updated 17 August 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Data scientists analyse data using their mathematical, statistical and computer science skills. These professionals combine their multi-disciplinary knowledge to solve business challenges, power social media and even predict extreme weather events. Learning about the exact tasks of a data scientist can help you decide if you want to choose this career path. In this article, we answer the question, "What does a data scientist do?", explore what skills they need and discuss how you can become one.

What Does A Data Scientist Do?

A data scientist gathers, analyses and extracts meaning from structured and unstructured data. Using their skills in programming, statistics and computer science, data scientists derive easily understood insights. They usually project information from large data sets to find answers to specific questions. Here is a list of their specific responsibilities and duties:

  • Define relevant data sets and collecting that data from different sources

  • Increase the accuracy, uniformity and reliability of raw data

  • Study data to identify repetitive patterns and trends

  • Process, store, classify and manage data

  • Choose a suitable analysis model, algorithm and technique for problem-solving

  • Express data in visual or other forms for better understanding

  • Find data-backed strategies and recommendations to increase personalisation, decision-making, optimisation and efficiency

  • Prepare reports and learn solutions based on the insights gathered for business leaders

What Skills Are Needed To Be A Data scientist?

Data scientists require skills and knowledge of various subjects like computer science, mathematics and modelling, along with industry expertise and soft skills. A deep interest in applying analytical techniques to data to find answers is necessary. Here are a few skills you must develop to succeed as a data scientist:

  • Mathematics, specifically expertise in statistics, modelling and probability

  • Analytical skills

  • Computer science, particularly data programming and software engineering

  • Data reporting and visualisation

  • Programming, with expertise in Java, Python, R or SQL

  • Big Data analytical platforms with tools like Kafka, Apache Spark, Hive and Pig

  • Database management systems tools like MySQL, Redshift, Snowflake and Redis

  • Machine learning tools

  • Cloud computing and API integration

  • Data mining and cleaning techniques

  • Business and domain knowledge

  • Communication, both written and verbal

  • Curiosity

  • Critical thinking

Related: Analytical Skills: Definition, Tips and Examples

How Is A Data Scientist Different From A Data Analyst And Data Engineer?

The role of a data scientist is often mistaken for that of a data analyst or data engineer. This is probably because there is a significant overlap of the skills and competencies for these roles. However, they are different from each other in these distinct ways:

Data analyst

Data analysts decipher the numbers, trends and insights gathered from big data and find answers to specific business questions. The most significant difference between the role of an analyst and a scientist is that data analysts usually require lesser advanced tech skills. This job also does not require you to be an expert in all fields of mathematics and is more concerned with data collection, analysis, processing, interpretation and reporting.

Related: Frequently Asked Questions: What Is a Data Analyst?

Data engineers

Data engineers are less involved with using data to create models. They are responsible for data architecture, storage, infrastructure and data flow. They are experts in programming and data query writing, as they oversee the architecture, database design and data warehousing. Data engineers also address the technical aspect of data operations, like scalability, backups, access and availability. Unlike data scientists or analysts, data engineers do not require data visualisation or reporting skills.

Do data scientists get paid well?

Data scientists have quickly become some of the highest-paid professionals in the tech industry. The average salary of a data scientist is ₹8,49,540 per year. Depending on the industry and location, this figure can go up significantly. For example, the average salary of data scientists in Chennai, Tamil Nadu is ₹11,07,409. The estimated average salary for a data scientist in Mumbai, Maharashtra is ₹9,74,696.

Is data science a good career?

The increasing demand for data scientists has made data science an attractive career path for many professionals. As businesses rely more on data to drive their business processes and growth, the demand for data scientists may continue to grow. As compared to other IT and digital careers, digital science is still in the promising stage and is expected to witness significant growth opportunities over the next decade.

Data Scientist Work Environment

The exact responsibilities and routine of a data scientist can vary depending on the industry and company. However, some aspects of their routine and work environment remain constant:

  • Working on a desk for most of the day

  • Coordinating with other business teams to understand business requirements

  • Obtaining data through existing systems or setting up data-gathering processes

  • Performing data correction and cleaning techniques to improve accuracy and uniformity

  • Testing new algorithms, models and tools

  • Writing reports, proofs of concepts and building data visualisation

  • Participating in presentations and meetings with team leaders to discuss data trends, patterns and recommendations

Note that data science is a field that requires discipline, focus and patience. A major part of the day can go towards data optimisation, wrangling and cleanup.

Data Scientist Educational Requirements

Earning a bachelor's degree in computer science, computer engineering, information technology, mathematics or statistics is the first step you can take towards becoming a data scientist. However, most employers prefer candidates with a master's degree in a relevant subject. Many universities offer Bachelor of Science (B. Sc.) in Data Science, Bachelor of Computer Application (BCA) in Data Science and Bachelor of Technology (B. Tech.) in Data Science programmes. The master's degree programmes for all these courses are offered by many institutions as well.

Those who do not have the required degrees can sign up for short-term programmes offered by several universities and organisations of repute. However, if you only have certifications and boot camp training without a formal degree, it can be more difficult for you to get secure employment in this role. Even after completing your education, you may need additional training and practice before working in a full-time role as a data scientist. This training can come in the form of internships and business projects.

How To Become A Data Scientist

The following steps can help you build a career as a data scientist:

1. Get the right education

Since data science requires expertise in several domains and skills, most employers require candidates to have a master's degree. These requirements vary across locations, industries and organisations, so make sure that you have the minimum level of qualifying education. You can review job postings to see the most common education that employers look for from candidates.

2. Complete certifications

Short-term certifications and training programmes usually help in building focused data science skills and add value to your CV and job applications. Select the most relevant and result-oriented programme to suit your needs. You may also select programmes based on personal interest to show an employer your commitment to the industry.

3. Seek relevant experience

Data jobs require several years of practical experience. Consider looking for internships and professional opportunities early while you are in college. Consider working in the IT industry to develop computer and programming skills.

4. Prepare a winning application

It is important for your resume, cover letter and job application to highlight your skills, education and relevant work experience. Make sure to list all the tools and processes that you know in your job application and customise them for each job posting. Practise answering questions about the information in your resume so you can impress a hiring manager.

5. Keep learning and upskilling

Keep working on strengthening your skills and learning new tools by completing training and other programmes. The data science industry is evolving at a rapid stage and may continue to grow in the future. The more you learn, the more you can show an employer how passionate you are about making this your career choice.

Related: Top Data Structure Interview Questions With Example Answers

How Long Does It Take To Become A Data Scientist?

There is no fixed time that must be spent in fulfilling the educational or training requirements to become a data scientist. Most bachelor's degree programmes are three to four years long, and you can get a fresher's job right after that, provided you have the required skills and experience. Pursuing a master's degree can take you another two years before you can start working or look for better job opportunities.

Many data scientists also start their careers after completing shorter courses and training camps that are usually a few months long. Similarly, others continue to complete certifications and training even after completing their formal education or while working. Thus, the time it takes to become a data scientist depends entirely on your inclination to study and gain expertise.

Please note that none of the companies mentioned in this article are affiliated with Indeed.

Salary figures reflect data listed on Indeed Salaries at time of writing. Salaries may vary depending on the hiring organisation and a candidate's experience, academic background and location.


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