How to become a data scientist in 4 steps

Updated 1 May 2023

As a member of the information technology team, data scientists can analyse information to help a company create strategic plans and make better decisions to increase revenue and profits. Data scientists must enjoy solving complex problems and generating solutions to present to executive staff members. You need to take certain steps if you want to make a career in this profession. In this article, we discuss what a data scientist does, their average salary, how to become a data scientist, and address frequently asked questions about this profession.

What is a data scientist?

Data scientists are highly skilled professionals who study and analyse big data to predict the direction of goals and projects within a company. While understanding a multitude of directional models, a data scientist incorporates their industry knowledge to help drive company projects and pivot when changes can affect a project before and after completion. They use large computer databases and manipulate codes while statistics focus on testing theories and hypotheses. Data engineers build databases while data scientists must have a general understanding of the databases and the ability to pull the data needed.

What does a data scientist do?

When working in this field, your job duties can fluctuate depending on your employer or industry. A data scientist is often responsible for the following tasks:

  • Work alongside business leaders to assess how data can reach their key objectives

  • Collect and clean data so that it's ready for analysis

  • Form questions that can help guide their research process

  • Look at large sets of data, trying to find patterns or trends

  • Use data analysis methods to investigate and understand data

  • Create easy-to-understand presentations for business leaders

  • Adjust research based on feedback from peers or clients

Related: What does a data scientist do for businesses?

Data scientist skills

Some skills you need to have to become a data scientist include:

  • Coding: Knowing several different coding languages can give you an edge in this career. Common ones include Python, Java, Perl, C/C++, and SQL.

  • Collaboration: Being able to work well with others is an essential skill to have as a data scientist, as you're going to work alongside company stakeholders, data engineers, and other data scientists. Having a commitment to teamwork shows that you can contribute to the group.

  • Technical: Data scientists need to have notable math and computer skills to thrive. This position warrants them to know programming languages and statistics and probability to interpret data and make calculated decisions on the organisation's behalf.

  • Communication: Data scientists must communicate their findings to their managers and executive staff members in cases where new findings affect the company's technological infrastructure. Being able to listen to other's ideas and feedback can help you adjust your information accordingly.

  • Public-speaking: While this may be a smaller portion of this role, data scientists need basic public-speaking skills to present their findings and ideas to the rest of the company. Being able to give an effective presentation shows your competency in explaining complex topics in simplified terms.

  • Analytical: Data scientists must consistently review and check the accuracy of their information, so analytical skills help you come up with comprehensive solutions and explain how you got there.

  • Time management: Being mindful of your time is essential for data scientists working on various projects with timely deadlines. Each action is significant in determining the outcome, so it's key for them to map out the workflow of each project cycle to identify which one to address first.

  • Organisation: When working with large sets of data and visual representations, having organisational skills can help you keep track of all these different components. Data scientists become familiar with spreadsheets, data software, and other tools to organise their work.

Related: Analytical Skills: Definitions and Examples

Average salary of a data scientist

The national average salary for a data scientist is £50,864 per year. The amount you make can also vary based on your geographical location, level of experience, education, skill set, and other factors. As you progress through this career, you can expect to make a higher salary than that of an entry-level data scientist.

How to become a data scientist

Here is a list of steps to becoming a data scientist:

1. Earn a bachelor's degree

Most data scientists spend four years earning an undergraduate degree in IT, math, computer science, physics, or a related field. Most undergraduates have at least one internship before working in an entry-level role, so network early on in your college career with data scientists to see what the best path to take is. Informational interviews can give you a better idea about the day-to-day schedule of a data scientist and companies hiring for this position.

2. Earn a master's degree

It is strongly encouraged for data scientists to earn a master's degree in data, engineering, computer science, business administration, or a related field to obtain a leadership position. Having a master's degree might also give you an advantage over other data scientist candidates when applying for higher-paying jobs. Check the requirements for each program to see if they align with the career path you want.

3. Gain work experience

While you can develop many skills for this role throughout your educational programs, much of what you learn happens on the job. Data scientists continue to be in high demand and finding entry-level jobs in data science often leads to positions such as a junior data scientist or junior data analyst. While working in an entry-level position, companies may provide on-the-job training and certifications that can lead to promotions.

Related: IT Skills: Definitions and Examples

4. Build your portfolio

A successful data scientist has a portfolio showcasing examples of projects that positively impacted their previous employers. A visual presentation of projects from start to competition demonstrates evidence of your experience and renders you with an additional advantage over a candidate with similar training. Consider putting your work on a professional-looking website or include it in a slide show you can send to employers.

FAQs about data scientists

Here are some frequently asked questions about this career path:

What are the benefits of becoming a data scientist?

If you're someone who enjoys math and scientist, then this may be a rewarding career path for you. Working as a data scientist gives you the opportunity to work through challenging problems to find solutions. This is an ideal job for those who like variety, as you can expect to have many different tasks to complete each workday. Data scientists get to work on all kinds of interesting projects, allowing them to make a difference in their company or industry.

What are some challenges of becoming a data scientist?

When pursuing this career, you need to go through rigorous training and education. This career requires a high level of analytical intelligence, making it important that you can interpret sets of data. Another challenge is that computer science is rapidly changing, meaning that you may need to learn how to use new software and data applications throughout your career. While this career can be challenging, it can also be quite rewarding for someone who enjoys lifelong learning.

Related: Data analyst vs data scientist

How can a data scientist advance their career?

Data scientists can advance their career by pursuing additional education. Earning a Ph.D. in astrophysics, mathematics, data science, or a related field can set you apart from other candidates. During your doctorate program, you can gain advanced knowledge and credentials for this career path.

Another way to progress in this career is to focus on a specialisation. By finding a niche, such as insurance, healthcare, or manufacturing, you can become a more marketable candidate. By investing time into a specific industry, you can gradually grow through the ranks in your own company or be a more qualified candidate when applying for high-level positions.


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Who is the ideal person for this career path?

Those who pursue this career path tend to be curious and intelligent individuals. If you're someone who enjoys figuring out problems and finding solutions, this may be a fulfilling career. Likewise, data science requires a lot of continued learning, making it important that you can commit to enhancing your knowledge throughout your career. Someone with a natural aptitude for STEM and computer science topics may be a good fit for this role.


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