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How to Become a Data Scientist in 8 Steps

Find out everything you need to know about becoming a data scientist, and find out whether it’s the right career for you!
Updated Sep 2023  · 12 min read

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Data science is everywhere right now. One after the other, companies worldwide are turning to data science to solve the most diverse problems out there. This situation has put data scientists in an advantageous position regarding employment and payment.

Naturally, a lot of people are becoming interested in learning how to become a data scientist. If you are reading this article, you may be one of those people. Here, we will show everything you need to know about becoming a data scientist, including an effective and attractive way to learn what you need to get started in the data science world. We picked out data scientist in our list of top analytics careers, and you can find other roles in our full article.

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What Does a Data Scientist Do? 

We have a whole article dedicated to understanding data science. In there, we explore the role a data scientist may take. Essentially, data scientists work to deeply understand and analyze data to provide actionable insights. 

In a business environment, they work across multiple teams to lay the foundations for robust analytics. This means developing strategies to capture, gather, and clean data from a range of sources. After organizing and exploring this data, they can then build solutions and communicate their findings to the wider business. 

Data scientists often work with other professionals, including data engineers, analysts, IT architects, application developers, and business stakeholders.

Data Analyst vs Data Scientist: What's the Difference?

We've got a full guide comparing data analysts and data scientists. However, in summary, while both roles work with data, they differ in scope and responsibility:

  • Data analysts. Typically work on interpreting existing data to provide actionable insights. They may use simpler models and tools.
  • Data scientists. Take on a broader role, often formulating the questions that need answering. They use advanced tools and models, including machine learning algorithms, to derive insights.

Why Become a Data Scientist? 

There are many reasons why someone may choose to pursue a career in data science. It’s an industry that can be immensely rewarding, providing an intellectually challenging and stimulating environment. Data scientists must keep ahead of the latest technological trends and developments, working in an often dynamic setting. 

So, if you’re intellectually curious, have an analytical mind, and love working with data and technology, becoming a data scientist could be for you. 

Furthermore, there are many opportunities for talented data scientists in the job market. Data from statista shows that the big data market size is expected to grow significantly in the coming years, predicted to be worth $103 billion in 2027 compared to $70 billion in 2022. 

Similarly, The US Bureau of Labour Statistics predicts a 36% rise in data science industry jobs between 2021 and 2031, far higher than the 4% national average. 

This growth is reflected in the popularity of data science careers, with organizations such as the U.S. News & World Report ranking data scientist as the 3rd best job in technology, 6th best in STEM jobs, and 6th best overall job. Similarly, Glassdoor ranks it as the 3rd best job in America for 2022. 

If you’re still not convinced why you should think about this career, the average data scientist salary could be appealing. With a median salary of $102k per year, it’s clear to see that the demand for data scientists is met with a generous pay packet. 

What Qualifications do Data Scientists Need?

There is some debate as to whether a degree is necessary for someone to become a data scientist. Plenty of professionals have entered the industry through other routes, but a university qualification can certainly be beneficial. 

Most graduate schemes and graduate jobs look for individuals with qualifications in fields such as computer science, data science, mathematics, statistics, engineering, and even physics. However, some schemes will train anyone with a degree to become a data scientist. 

We’ll cover the exact skills in more detail further down, but a working knowledge of programming languages such as Python, R, SQL, and/or Julia can be helpful. 

Others will find that they can rely on their own self-directed learning, picking up the necessary skills and experience in their own time and impressing employers at the interview stage. For example, our Data Scientist with Python track can help you learn all the necessary skills to start a career in data science. 

You can also get certified as a data scientist with DataCamp and prove your data science knowledge to potential employers. 

What Skills Do Data Scientists Need? 

We’ve covered the top 15 data scientist skills in more detail in a separate article, so we’ll summarize that here. Essentially, to succeed in data science, you’ll need an assortment of hard and soft skills. 

Hard Skills

These are the technical skills you’ll need to develop to become a fully-fledged data scientist: 

  • Python
  • R
  • Statistics and math
  • SQL and NoSQL 
  • Data visualization
  • Machine learning
  • Deep learning 
  • Natural language processing 
  • Big data
  • Cloud computing

Soft Skills 

These are now usually referred to as ‘power skills’ or ‘human skills,’ and they are the cross-functional skills that aren’t job specific but are still necessary. For a data scientist, these include: 

  • Communication 
  • Storytelling 
  • Critical thinking 
  • Business acumen 
  • Problem-solving 
  • Teamwork 

What is an Average Data Scientist Salary? 

We’ve covered data science salaries in detail in a separate article, giving a breakdown of pay based on a variety of factors. However, to sum things up briefly: 

  • Average salary data differs between sources. For example, in the US, Glassdoor give an average salary of $117,212 per year based on a sample of 18,000 salaries, whereas PayScale estimates $97,318 per year, based on 7,280 salary profiles.
  • Data science managers are generally the top earners, with a salary range of $113,000 - $230,000 per year. 
  • Data scientists and data engineers tend to have a similar salary, ranging from $82,000 - $167,000 per year for scientists and $76,000 - $166,000 per year for engineers. 
  • Company size matters; with companies of more than 1,000 employees paying a median salary ranging from $90,000 to $110,000 per year, compared to around $80,000 for medium-sized companies (26 to 1,000 employees) and $60,000 for  small companies and startups (2-25 members).

How to Become a Data Scientist 

By now, you’re likely eager to get started on your journey to becoming a data scientist. But where to start? Below, we’ve highlighted eight steps you need to take to become a data scientist from scratch. 

As we’ve discussed already, the exact data scientist requirements will depend on a variety of factors, but these are some of the most commonly cited steps: 

1. Learn data wrangling, data visualization, and reporting 

As you work to become a data scientist, you’ll often encounter large and complex datasets. To make sense of this information (for yourself and others), you’ll need to learn how to deal with it. This is where skills in data wrangling come in handy. This is where you’ll clean, organize, and transform raw data into a format you can analyze and draw conclusions from. 

There are various tools you can use with data wrangling, but libraries like pandas in Python are often the go-to choice. You can check out our data wrangling in Python with pandas cheat sheet for more info. 

When it comes to presenting your data with impact, you’ll need to master reporting and data visualization. You can find out more about some of the best data visualization tools out there, giving you a range of options depending on your ultimate goal. 

2. Work on your statistics, math, and machine learning skills 

While you don’t necessarily need a degree that covers these fields, you do need a functional knowledge of these areas. Generally, you’ll want to cover areas such as calculus, linear algebra, and statistics. However, it’s worth assessing your intent behind learning these things. 

For example, learning calculus can help you learn to create optimization algorithms for machine learning, although there are functions in both Python and R that can take care of the calculus for you. As a specific example, understanding a topic like gradient descent can help you measure the change in a function’s output when you tweak the inputs, which can help to refine machine learning models. 

If you’re looking to brush up on your linear algebra skills, our Linear Algebra for Data Science in R course can help you work with vectors and matrices, solve matrix-vector equations, perform eigenvalue/eigenvector analyses and use principal component analysis to do dimension reduction on real-world datasets. These areas are all useful for applied mathematics and data science. 

Similarly, Intermediate Statistical Modeling in R covers some of the essential machine learning statistics topics, helping you make progress in key areas. 

3. Learn to code 

In our article on the top programming languages for data scientists, we explore in detail some of the best languages you should consider learning right now. Two of the most obvious choices for data scientists are Python and R, given their versatility and ubiquity. 

Of course, working with data also means working with databases, so SQL is another essential programming language. Thankfully, it’s a relatively straightforward language once you’ve learned Python and R. 

Julia is a good choice once you’ve learned Python, R and SQL. With Julia, you get a language that’s built from the ground up for data science, making it fast and intuitive.  If you start working with very big data sets, you may need other languages, but until then, these four will be sufficient.

With a language like Java, you get an open-source language known for it’s efficiency and performance. For data science, Java Virtual Machines provide a solid and efficient framework for popular big data tools, such as Hadoop, Spark, and Scala. 

Other coding languages worth learning for data science with very large data sets include Scala, C/C++, JavaScript, Swift, Go, MATLAB and SAS. 

4. Understand databases 

We mentioned SQL in the topic above, and it’s a point that bears repeating. Relational databases allow data scientists to store structured data in a fast and efficient way. When collecting and organizing data, you’ll often find that SQL is your preferred tool here. 

SQL allows you to handle structured data, query databases, wrangle, prepare, and experiment with data, and more. What’s more, SQL is often deployed alongside Python, with libraries such as SQLite, PostgreSQL, and MySQL helping you connect different data sources. 

5. Learn to work with big data  

We’ve already touched briefly on the fact that, as a data scientist, you’ll often be working with large sets of data. In an era where everything and everyone is producing more data than ever before, these data sets are increasingly huge, making them harder to collect, maintain and process. 

Yet from these huge data sets, a skilled data scientist can extract new and detailed insights. So, learning to use cloud platforms such as AWS, Microsoft Azure, and Google Cloud can all benefit your career as a data scientis. Similarly, tools like Apache Spark can help with big data processing, analysis and machine learning. 

You can learn the big data fundamentals with PySpark with our course, which covers some of the essentials when dealing with large amounts of data for analysis.  

6. Get experience, practice, and meet fellow data scientists 

As with any career, to become a data scientist, you’ll need as much experience and practice as possible. Luckily, there are many ways that you can get involved with communities, work on projects, and develop your data science skills. 

For example, DataCamp Workspace provides a collaborative cloud-based notebook that allows you to analyze data, collaborate with others, and share insights. Workspace is designed to take you from learning data science to doing data science. It also has built-in datasets, so you can start analyzing data within minutes.

You can also apply your knowledge to various data science projects, allowing you to solve real-world problems from your browser. 

7. Take an internship or apply for a job 

Once you’ve developed all of the skills mentioned above, you’ll want to start applying them in more professional settings. Once you’re confident you have the data scientist skills needed to meet the expectations of a role, you can start applying for internships or jobs. 

Check out our full guide on applying for data science jobs and learn how to set yourself apart from other candidates. You’ll likely need a fairly thorough portfolio demonstrating a range of skills, and you’ll also need to prepare for the data scientist interview ahead of time. 

8. Follow and engage with the community 

If you want to become a data scientist, you’re going to need to keep up-to-date with a fast-paced industry. There is no better way to stay informed about developments in data science than by engaging with what can often be a generous and dedicated community. 

As well as social media sites such as LinkedIn, Twitter, Discord, and Reddit, there are all kinds of niche sites, blogs, and data science leaders you can follow. Look for people who are interested in the same areas as you, reach out for advice and contribute to discussions, and get involved with what’s going on. And, of course, check out the DataFramed Podcast to get industry news from a host of data professionals.

Educational Paths to Become a Data Scientist

Becoming a data scientist is a journey that can start from various educational backgrounds. As we've discussed, a bachelor's degree in data science, computer science, or a related field is often the first step that people take, but in reality, there are multiple paths to gain the necessary skills:

  1. Online courses and certifications: Starting with online resources such as DataCamp's data science tracks is an excellent way to gain the skills you need. As well as building your knowledge in various areas, you can also work towards becoming a certified data scientist with our recognized certification.
  2. Undergraduate degree. A bachelor's degree in data science, computer science, statistics, or a related field provides a strong foundation.
  3. Master's degree. For those looking to specialize or gain advanced skills, a master's degree in data science can be beneficial.
  4. Data science bootcamps: Data science boot camps offer intensive training to help you quickly acquire practical skills. You can read more about the top data science bootcamps and some alternative options in our separate article. 

Final Thoughts

The path to becoming a data scientist is as exciting as it is rewarding. With data science permeating every sector and industry, the role of a data scientist has never been more crucial. Whether you're driven by intellectual curiosity, the promise of a lucrative salary, or the desire to make impactful decisions based on data, a career in data science offers endless possibilities.

As we've explored in this comprehensive guide, there are multiple avenues to enter this dynamic field. From formal education in computer science or statistics to self-directed learning through online courses, tracks, and certifications like those offered by DataCamp, the journey can be tailored to fit your needs and preferences. 

The job market for data scientists is booming, with significant growth predicted in the coming years. This growth is not just in terms of job opportunities but also in the variety of roles and specializations within the field. Whether you see yourself as a data wrangler, a machine learning engineer, or a business intelligence analyst, there's a niche for you.

So, if you're analytical, enjoy problem-solving, and are intrigued by the power of data, there's no better time to become a data scientist. Get started today with our Data Scientist with Python career track. 

Data Scientist FAQs

How do you become a data scientist from scratch?

It can be hard to get started from a standing start, but you can learn data science by yourself. Start by mastering the fundamentals of statistics and mathematics, before learning how to code in Python, R and SQL. Next, work on understanding relational databases and how you can utilize them to organize and analyze data. You’ll also want to learn to present your data with impact, mastering some data visualization techniques. After working with big data and machine learning, you’ll want to gain as much experience as you can, setting targets, undertaking projects, and joining data scientist communities.

Can you become a data scientist without a degree?

Although many of the top data scientist jobs require a university qualification, often a Master’s degree, it is possible to break into the field without a degree. You’ll need patience, dedication, some natural flair, and the ability to be flexible and agile, but you can forge a career in this niche if you’re determined.

How long does it take to become a data scientist?

As we outline in our data science FAQs, on average, to a person with no prior coding experience and/or mathematical background, it takes around 7 to 12 months of intensive studies to become an entry-level data scientist. However, there are so many skills in data science that you can keep learning for your entire career.

Who is suited to a career as a data scientist?

The nature of this career suits those who are curious, analytical, and have a passion for information. It often requires problem-solving skills, as well as tenacity and self-motivation. Those who can communicate complex problems in a succinct and understandable way often thrive in this role.

What are some common tasks that data scientists perform?

A lot of a data scientist’s role focuses on data exploration, modeling and deployment. Data scientists will often outline the problem, gather and cleanse data, and draw conclusions about the information they have collected. Read more in our data science uses cases guide.


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