How to Become a Data Scientist –  A Simple Step-by-Step Guide

Published: October 12, 2023

If you’re here on this blog, you are either interested in becoming a data scientist or want to know the requirements to be a data scientist. But before we get into the details of how to become a data scientist, let us first understand who precisely a data scientist is.

Data scientists are the latest generation of analytical data experts with the technical ability to tackle complicated issues and the desire to investigate what problems need to be addressed.

They’re a mix of mathematicians, computer scientists, and trend forecasters. They’re also in high demand and well-paid because they work in both the corporate and IT sectors. So who wouldn’t desire to be one of these?

Data scientists decide which questions their organization should ask and how to respond to them using data. In addition, they frequently create prediction models to aid in thinking and predicting.

Daily, a data scientist may complete the following tasks:

  • First, enhance the accuracy of data or product portfolio by utilizing machine learning techniques.
  • Discover patterns and themes in datasets to get insights.
  • Create forecasting algorithms and data models.
  • Distribute suggestions to other teams and top management.
  • In data analysis, use Python, R, SAS, or SQL.
  • Stay updated with the latest developments in the world of data science.

Intrigued and interested, right? Given that the job is so exciting and even looks lucrative, what steps should you take, and which data scientist courses should you pursue to become a professional data scientist?

Don’t worry because now we’ll provide you with a step-by-step guide to help you become a data scientist.

How to Become a Data Scientist

how to become a data scientist by enrolling in online data scientist classes

Before we explore the different steps you will take toward becoming a data scientist, let’s first try to understand why this field has become so attractive for young graduates and current professionals.

According to the US Bureau of Labor Statistics (BLS), data scientists and mathematical science jobs are anticipated to rise by 36% between 2020 and 2030. 

According to Glassdoor, a data scientist makes an average income of $125,00 in the United States

This is significantly faster than the overall annual job growth of 8%. The emergence of big data and its rising relevance to corporations and organizations have been connected to the increased demand.

Now let’s look at the four steps you can undertake to become a data scientist.

4 Steps to Become a Good Data Scientist

  1. Learn the Languages Required to Become a Data Scientist
  2. Skills Required to Become a Data Scientist
  3. Earn a Data Scientist Degree
  4. Data Science Entry-Level Jobs

Step # 1. Learn the Languages Required to Become a Data Scientist

You should get a command of the languages of crucial use when working as a data scientist. Consider taking an online course or enrolling in a related BootCamp, like the best data science bootcamps, if you want to improve your complex data abilities.

Data Scientists utilize various specialized tools and applications for data polishing, analysis, and modeling. Aside from general-purpose Excel, data scientists are proficient with quantitative programming languages such as Python, R, or Hive and query languages like SQL.

Therefore, you must learn these programming languages to become a data scientist and take a step toward gaining a data science education.

To gain a command specifically of programming languages, you can also choose to look for the best programming language courses.

Step # 2. Skills Required to Become a Data Scientist

If you do not have any data-related job experience, you could still become a data scientist; nevertheless, you will need to establish the necessary foundation to pursue a data science profession.

Data Scientist is a high-level profession; before you achieve that level of expertise, you need to broaden your knowledge in the related topic. That might be mathematics, engineering, statistics, data analysis, coding, or information technology – some data scientists have even worked in banking and as sports scouts.

Let’s look at some critical skills required to become a data scientist. Also, note that you should work on honing these skills and your profile to enhance them to a level acceptable to the data science industry.

  • Programming: As already discussed above, you need to have a command over programming languages.
  • Statistics: Statistics and numerical analysis form a significant part of the data scientist’s skill set.
  • Data analysis: You must know how to analyze data to derive actionable insights.
  • Mathematics: Mathematics and numbers are the base of any data science-related job.
  • Engineering: Yes, you read it right; engineering does come into play when you want to work as a data scientist. Hence your fundamentals relevant to engineering should be clear.
  • Information Technology: Information technology drives the vehicle of data science due to newer software coming up every day and, of course, the general IT infrastructure as well.
  • Data Visualization: Using different software and tools, you need to know how to visualize data, especially in graphical representation.
  • Communication: As a data scientist, you must be able to communicate your ideas and results in actionable forms to the decision-makers; hence your communication skills should be top-notch.

Step # 3. Earn a Data Scientist Degree

Employers want to see academic qualifications to guarantee you have the knowledge to handle a data science job, although it is only sometimes essential. To gain a head start in the industry, consider pursuing a relevant Bachelor’s degree in data science, analytics, or computer science.

Suppose you do not have a Bachelor’s degree, given that you have already graduated from the university with a 4-year qualification. In that case, you can search for a Master’s program in data science.

Given that you may already be a professional and not a fresh graduate, then pursuing a Master’s degree online might be an option to consider while continuing with your job. You can check out the most affordable colleges and universities offering online options if you want to take this route.

Earn your Harvard data science certificate and take a decisive step towards becoming a proficient data scientist, equipping yourself with cutting-edge skills and knowledge from one of the world’s most prestigious institutions.

Step # 4. Data Science Entry-Level Jobs

Another one of the steps that you can take toward your journey to becoming a data scientist is to consider entry-level jobs in this field. In addition, you can look for internships, even free to-do projects, that will help you gain the relevant skills and knowledge required to become a data scientist.

Once you have gathered a command over the programming languages, working on projects that utilize the logic of these programming languages is one of the best ways to enter the data science industry.

You can start using your skills, honing your newly gained abilities, and expanding them even further.

Take on tasks requiring diverse abilities, such as utilizing Excel and SQL to maintain and query datasets and Python and R to analyze data statistically, construct models that evaluate behavior and give new insights, and apply the statistical study to anticipate uncertainties.

You could also look at different entry-level positions that require zero experience in the field. These positions will be pivotal for entering the sector and pursuing job training and learning.

6 Career Paths and Salary for Data Scientists

Career Path and Salary for Data Scientists

As data scientists’ work crosses so many sectors and fields, the jobs that data scientists might serve are known by various titles.

There are several additional types that will keep changing as data science becomes more common. However, while the variety of job descriptions in data science may appear endless, we will discuss the various jobs that data scientists most typically occupy.

  1. Data Scientist
  2. Data Engineer
  3. Data Architect
  4. Data Analyst
  5. Business Analyst
  6. Data Administrator

1. Data Scientist

With an average salary of $125,000 per annum, individuals with the job title “Data Scientist” may be required to do the tasks of a Data Engineer and Data Analyst, as well as more integrated data work, depending on the firm.

The data scientist’s function varies from that of the data analyst and engineer. For example, the data scientist may drive a firm’s big data strategy by raising the right questions and producing fresh concepts, products, and solutions.

2. Data Engineer

$113,000 per year is the average salary for a data engineer in the U.S. Data engineers gather, organize, and arrange information. Job postings for data engineers will often highlight a variety of duties, such as the ability to collect external data, develop database systems, and build data models.

3. Data Architect

With an average salary of $162,000 per annum, data architects tend to earn more than individuals with the title of data engineer. Data architects are information technology experts that utilize their data science and design abilities to assess and analyze an organization’s data architecture, plan future datasets, and develop solutions to manage and store data for businesses and their customers.

4. Data Analyst

Data analysts earn $65,000 annually, a salary way less than a data architect or data scientist. Yet, data analysts can use data processed and made available by a data engineer’s efforts to provide findings that may be used to solve issues, optimize products, and make substantial proof-based choices.

5. Business Analyst

They earn an average salary of $81,000 per annum. Business analysts bridge the gap between information technology and the business by assessing processes, determining needs, and delivering information-based suggestions and reports to stakeholders and managers.

6. Data Administrator

An average per annum salary for a data administrator is $90,000. A data administrator oversees converting data into a usable data model. In addition, the individual determines which data should be saved in the database. It is more of a business position with some technological skills than a technical role.

5 Major Benefits Of Learning Data Science

Now let us look at some of the advantages and benefits you will enjoy if you decide to indulge in this field of data science and learn the relevant concepts and skills.

  1. It’s a growing sector that is providing new opportunities every day.
  2. It offers lucrative careers if we compare the average salaries earned by the individuals in it.
  3. This field has a lot of versatility. You can work for the government, education, healthcare, and any sector you can think of.
  4. The field and its positions come with great responsibility as they drive business decisions. Hence, they command prestige.
  5. Data scientists work on meaningful and actionable insights, in the end, which are the main drivers behind improving society and consumer life in general.

How to Become a Data Scientist - FAQs

What Qualifications or Educational Background Do I Need to Become a Data Scientist?

To become a data scientist, having a strong foundation in mathematics, statistics, and computer science is essential. While a degree in fields like data science, computer science, mathematics, engineering, or related disciplines can be advantageous, practical experience and specialized certifications also hold significant value.

What Programming Languages Should I Learn to Excel as a Data Scientist?

Proficiency in programming languages like Python or R is highly recommended as they are widely used in the data science community. Additionally, familiarity with SQL and other tools for data manipulation and analysis can greatly enhance your capabilities as a data scientist.

How Important Is Machine Learning Knowledge in Becoming a Data Scientist?

Machine learning is a fundamental aspect of data science. Understanding various machine learning algorithms, techniques, and frameworks is crucial for leveraging data to gain insights, make predictions, and create valuable models.

What Are Some Essential Tools and Technologies Used in Data Science?

Familiarity with tools like Jupyter Notebooks, Pandas, NumPy, and data visualization libraries (e.g., Matplotlib, Seaborn) is vital for data manipulation and analysis. Knowledge of popular machine learning libraries like sci-kit-learn and TensorFlow is also advantageous.

Is It Essential to Have Domain-Specific Knowledge as a Data Scientist?

Having domain knowledge in areas like finance, healthcare, marketing, or any other field of interest can be a significant advantage. It allows you to understand the data context better and generate more meaningful insights for specific industries.

How Crucial Is Hands-on Experience in Data Science?

Practical experience is paramount in becoming a successful data scientist. Engage in personal projects, participate in hackathons, and collaborate on real-world projects whenever possible to apply your skills and build a robust portfolio.

What Are Some Tips for Networking and Breaking into the Data Science Industry?

Participate in data science communities, attend conferences, and join online forums to connect with professionals in the field. Networking can provide valuable insights, job opportunities, and collaborations that can accelerate your career as a data scientist.

Should I Pursue a Master’s or a ph.d? In Data Science for Career Advancement?

While advanced degrees can open up additional opportunities and higher positions in the data science industry, they are not always necessary. Building a strong foundation through relevant coursework, practical experience, and continuous learning can be equally valuable in advancing your career.

What Are the Typical Job Roles for Data Scientists, and What Do They Entail?

Data scientists can work as data analysts, machine learning engineers, AI researchers, or data engineers, among others. The roles involve tasks such as data cleaning, exploratory data analysis, model development, and using data to solve complex business problems.

Is Data Science a Growing Field?

Yes. Data scientists’ employment is expected to expand by 36% between 2021 and 2031, substantially more quickly than the average for all professions. On average, 13,500 opportunities for data scientists are expected per year over the next decade.

Do You Need a College Degree to Be a Data Scientist?

In most situations, an entry-level position as a data scientist will require at least a Bachelor’s degree in a relevant discipline. But many non-entry level employments in data science will require a Master’s degree.

How Long Will It Take to Become a Data Scientist?

On average, it takes 7 to 12 months of rigorous study for a person with no prior coding skills and mathematics background to qualify as an entry-level data scientist.

What Are Some Common Challenges That Data Scientists Face?

Some common challenges that data scientists face are collecting data from multiple sources, identifying relevant core business issues, and communicating results to stakeholders with non-technical backgrounds.


Now that you have gone through our step-by-step guide on becoming a data scientist, we recommend that you also reflect on and analyze the benefits of this field.

After all, not only does it offer lucrative job options, but given the rate at which it is growing and individuals with the relevant skills being in high demand, it is also the future and provides job security.

So, what are you waiting for? Enroll in the best data science program or course today and take the next giant leap toward an illustrious and successful career!