How To Switch A Career From Non-Tech Background To Data Analyst

The popularity of data science has seen steady growth in recent years, and there are several job opportunities in this field. Today, there is an imbalance in the availability of jobs and that of qualified professionals.

Due to the excess vacancies, the industry is happily embracing professionals from other backgrounds (including the non-technical ones) to take up data analysis roles.

While this is indeed a golden career opportunity for people from a non-technical background, one needs to realize that this field demands a host of professional skills.

If you are looking to jump from a non-technical background to a data analyst role, you need to plan your career trajectory well and ensure that you upskill yourself for the job. Given below is a step-by-step guide that will help you plan the jump and land yourself a lucrative job in data analysis.

Have a Dream Job

Before you leap into the world of data and analysis, you need to realize that this is a diverse field, and there are different types of jobs that you can consider.

While some of these fields require a detailed understanding of mathematics, programming, or statistics, others demand a thorough business knowledge. Opting for a field that is in alignment with your current knowledge will make things easier for you.

For example, if you are someone with a bachelor’s degree in finance, you can consider taking up a data scientist’s role who will be the primary point of contact with experts from other fields. In this role, you will be explaining key insights of a project to the major stakeholders.

Similarly, there are several other roles you can consider. Having a clear picture of your dream job will allow you to upskill yourself for the position.

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Find a Mentor

It is a good idea to find a mentor who can guide you into finding an inroad to the industry in the initial days. Ideally, your mentor should be someone who has at least five years of industrial experience in the world of data science.


There are ample career opportunities in data sciences, but most of the coveted positions are referral-only posts. You can expect your mentor to refer you for vacancies they are aware of. They can also introduce you to other industry veterans and recruiters, thereby increasing your visibility.

Constructive Criticism

When you are venturing into something new, it is expected that you will have several questions. Your mentor can answer such questions or at least direct you to people who will help. If you are a budding entrepreneur, your mentor’s constructive criticism will lend a helping hand in your growth story.

Industry Insider Tips

Having an industry mentor will give you prompt insights into how the industry functions and what people expect of you. This will give you an edge when you apply for a job in the world of data science.

Long Term Support

The role of mentors in one’s career is not restricted to the initial days. Data science is a challenging field, and the chances are that there will be times when you will have insecurity about your position.

At such times, your mentor will be the voice of sanity and give you advice that will enable you to bounce back and drive your career’s success story.

Take up Certifications

Data science has facets from different industries, and it is a good idea for beginners to take up certifications. Such certifications help a novice get a basic understanding of the key concepts.

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Since most certifications have a rigid timeframe, it helps you set a mission and complete the tasks within a deadline. This is especially important in data science since every core concept is a diverse field, and you will find a lot of content on the internet.

If you are planning to take up a certification, ensure that it covers the basics of programming languages such as R, Java, and the core concepts of deep learning, data visualization, big data handling, statistics, and probability.

Alternatively, you can take up multiple data analyst certification courses to learn these concepts. In any case, ensure that you are confident in the basics of these topics before you apply for a job in this field.

Get Practical Experience

Data science is a field where practical skills have a much higher weightage over degrees. Start by taking up small personal projects to experiment with tools and bring an original ideal to life.

Having such chronological statements of attempts will give your potential employer a life demonstration of your skills and the willingness to learn. Most certification courses in data science have a practical element and are used to gauge one’s understanding of the core theoretical concepts.

Since an industry expert does the practical evaluation of such a course, these are highly respected in the field. Mentioning the same in your resume adds value to your profile and increases the chances of you bagging a job.

You can also consider taking up a project under your mentor. That way, you will not get stuck in a rut, and your mentor will give you the moral and intellectual boost whenever you hit a roadblock.

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Another option of learning things from a practical perspective would be by taking up personal coding projects wherein you explore your comfort level with a language without worrying about being graded on time-bound tests.

A look at the industry news will reveal that the world of data science is constantly changing. If you are planning to switch to this field, you need to brace yourself to read blogs, newspapers, and long-form articles written by industry veterans.

These will give you a picture of the important industry trends and give valuable insights into potential job opportunities. Such industry research will also help you streamline your personal learning goals with the industry expectations and increase your chances.

Thus, you see that the process of moving to data analysis roles is clear irrespective of the background you associate yourself with. All you need to do is plan the move well to pick up on the necessary skills and become an asset for the company that hires you.