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How to Switch your Career From IT to Data Science?

Last updated on: 
January 5, 2024
Vartika Rai
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About The Author!
Vartika Rai
Vartika Rai
Product Manager at Interview Kickstart. With exceptional experience with tech giants like Microsoft, she is a curious lady for the vast scope of NLP, Big data analytics, ML and data science.

Data science has unquestionably become one of the most fascinating fields in recent years. The application of data science is spreading throughout almost all industries. It propels organizations of all sizes and encourages them to make organizational choices, which is the factor that makes it unique and therefore, the demand for switching to data science is increasing. To be honest, a lot of people who had never worked in data science before got introduced into the industry and switched jobs as a result of different workflows. 

A career change enables you to earn a "Handsome Pay" and broaden your skills across other industries. What you need to know now is, "How to switch career to Data Science?" if you are currently working in a different field, supposedly IT. 

Here is what we are going to learn in this article!

  • Why is data science in high demand?
  • Information technology vs. data science
  • Switching career to data science
  • Job roles in data science
  • Gear up for your switch to data science
  • FAQs on how to transition to data science

Why is Data Science in High Demand?

In the ever-evolving globalized world of today, we usually wonder why data science is so important. Well, data excites people in practically every business imaginable. 

International Data Corporation (IDC) forecasts that by 2025, there will be more than 175 zettabytes of data available worldwide. The data collected by any organization over a period of time is considered gold for their future operations. With the use of this data, data scientists may produce insights that serve as business intelligence, supporting management in making choices about recruitment, tasks, acquisition, supplies, consumer demand, marketing, and nearly every other facet of the organization.

Elements of data science

The increasing interest in the field over time has led to businesses putting data science principles into practice to increase and improve consumer satisfaction. Determining what and how much data is needed for operations is one of the main duties of data scientists and engineers. 

The word "data science" encompasses a wide range of abilities and work duties. Hence, a data scientist's work at one organization might not be the exact same as one at a different one. Few data scientists work exclusively on building machine learning models all day. Many data scientists also have positions as data analysts, data engineers, or both.

Information Technology vs. Data Science

Information technology (IT) is one of the fields that data science has a lot in common with. Information technology is more often linked to customer support, but data science is more often related to significant computing abilities.

It's necessary to talk about some of the most significant parallels and divergences between information technology and data science. Through a comparative analysis of each sector, the careers in demand that combine those skills needed in these domains can be determined.

Data science covers the different phases of handling information and data, starting from gathering and storing the data and ending with analysis and visualization. On the other hand, information technology focuses on specific phases of the data science workflow, with the fundamental point of handling networks, data management, and data storage structures.

Regardless of whether you're interested in a job in data science or information technology, you need to build a skill set that involves managing facts and figures. Many of such skills are obtained in degree and certificate courses, which can be integrated or segregated. 

Both information technology and data science demand the principles for dealing with information and data in an approach that provides data safety and security while also preserving data integrity as time goes on.

Switching Career to Data Science

The first step in switching to a data science career is deciding how prepared you are for this change. What fascinates you, what needs improvement, what are your current strengths, and what standards do you would like to meet?

  1. Consider your goals and interests in data analysis, problem-solving, and curiously driven research. Determine which of your current abilities and talents you can use to pursue a career in data science.
  2. Analyze the employment market and various data science roles. Consult with experts in the field to gain insight into the expertise, skills, and roles. This will enable you to match your objectives and desires appropriately.
  3. Utilize your transferable skills from your last job role in the IT sector such as scripting and automation, cloud computing skills, shell scripting for data pipelines and troubleshooting and debugging skills.
How to transition to data science
  1. You must be proficient in both R and Python. While training, continue to explore popular libraries and frameworks such as TensorFlow, Scikit-learn, and Pandas.
  2. Take part in Kaggle tournaments and work on data science initiatives. These platforms give you a window into accurate data and duties that broaden your perspectives in terms of problem areas and improve your problem-solving skills.
  3. Gain an understanding of the fundamentals of data storytelling and learn how to communicate complicated ideas simply and effectively. Your ability to communicate clearly and visualize data will increase the impact of your work as a data scientist.
  4. Make your resume and cover letter stand out by emphasizing pertinent skills, initiatives, and experiences. Boost your technical skills, analytical thinking, and data-driven achievements. To highlight your work, think about adding a link to your GitHub repository or portfolio. 
  5. Preparing for data science interviews by studying prevalent interview questions and dealing with coding and analytical problems. Enhance your knowledge of fundamental topics, algorithms, and techniques. Present your ability to explain your thought process and your aptitude to solve problems.

Job Roles in Data Science

There are several job roles to keep in mind when switching career to data science. Here are a few of the popular job roles in data science:

Data science job roles
  1. As a data scientist, you will be responsible for managing each element of a project, from determining what matters to the business to data collection and analysis, including predictive modeling, to data visualization and presentations.
  2. Data analysts are accountable for a variety of duties, including data visualization, transformation, and manipulation. Analysts create reports that efficiently explain the patterns and insights obtained from their study in a format that everyone can understand.
  3. Data engineers are in control of developing, creating, and managing data pipelines. Businesses require ecosystems to be tested, and they must be ready for data scientists to execute their algorithms.
  4. Data architects develop new database platforms that are tailored to a particular business model's specifications. In addition to enhancing the efficiency of the data pipelines, they must guarantee that the data is properly prepared and available for use by data scientists and analysts.
  5. Business analysts play a bit of a slightly different role than other types of job roles in data science. They are easily able to differentiate between high-value and low-vale data. Along with that, they know how data-oriented technologies function and how to handle vast volumes of data. 
  6. A data and analytics manager determines data science procedures and assigns assignments to their team based on their abilities and knowledge. Technologies such as SAS, R, SQL, and so on should be among their strong points, along with program management.

Gear Up For Your Switch to a Data Science Career!

It is evident by now that there is no one-size-fits-all way when it comes to data science. We've taken note of the various jobs readily available in this industry. Making the switch to data science might not be simple because it demands determination, regularity, and patience. But if you put in the necessary preparation and effort, you can accomplish your objective and get an initial position in the industry. If you're not sure where to begin, Interview Kickstart is right here to help you learn how to transition to data science. It provides data science training from well-known corporate executives. Beginning with the fundamentals and progressing to interview questions, you will gain knowledge through practicing a variety of practical exercises. 

FAQs on How to Transition to Data Science

Q 1. Is data science beginner-friendly?

Data science is a beginner-friendly field. It requires an individual to dedicate enough time and effort to gain expertise and create a strong basis in computer programming, statistics, and math. 

Q 2. What is the minimum experience for a data scientist?

For entry-level jobs, a bachelor's degree in a related field—such as computer science, statistics, mathematics, or engineering—is typically necessary. Concerning the more advanced requirements for data scientists, certain organizations could favor applicants who hold a Master's or PhD in a relevant discipline.

Q 3. Does data science require coding experience?

Coding abilities are necessary for data science positions. It works with big datasets and builds machine-learning models using languages like R and Python. It is not recommended to pursue a career as a data scientist without knowing how to code.

Posted on 
December 11, 2023

Vartika Rai

Product Manager at Interview Kickstart | Ex-Microsoft | IIIT Hyderabad | ML/Data Science Enthusiast. Working with industry experts to help working professionals successfully prepare and ace interviews at FAANG+ and top tech companies

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