How to become a data scientist

Data science is the application of statistical and computational methods to large sets of structured or unstructured data. Through these processes, data scientists can extract valuable insights from noisy data to make informed decisions.

Data science is a multidisciplinary field that draws concepts and techniques from statistics, data analysis, machine learning, and computer science. It occupies an interesting middle ground in the world of engineering and science. It's not quite software development, nor is it completely mathematics or statistics.

Given that data science requires proficiency in a very specific set of skills, there is a high demand for skilled data professionals. Consequently, those who demonstrate exceptional proficiency in this field are often rewarded with substantial salaries.

We'll cover:

Essential skills for becoming a data scientist
Steps to becoming a data scientist
How long will it take to become a data scientist?
Typical salaries for data scientists in the US
The next steps to becoming a data scientist

Essential skills for becoming a data scientist

In this section, we'll demystify the expected qualifications of a data scientist– starting with
the hard skills, the technologies, and statistical and computer science principles you'll need to use regularly.

Next, we'll talk about the soft skills that make a data scientist particularly suited for the work that they do.

(Don't worry, later we'll give you plenty of options for how you can develop these skills for yourself.)

Technical skills

When it comes to technical skills, there are several key areas that data scientists need to be proficient in.

These include:

Programming languages
- Python: A widely used language in data science due to its versatility, ease of use, and extensive library of data science tools and frameworks.
- R: A language designed specifically for statistical computing that comes with a large number of libraries and packages used for data analysis and visualization.
- MATLAB: A multi-paradigm programming language and a popular numeric computing environment.
Statistics and mathematics
- Linear algebra is the study of linear equations and their properties, and it is used extensively in data science for tasks such as matrix operations, data transformation, and linear regression. Linear algebra also forms the foundation of many machine learning algorithms, such as principal component analysis (PCA) and singular value decomposition (SVD).
- Probability theory is the study of random events and their likelihood. In data science, probability is used to model uncertainty, make predictions, and estimate probabilities. It is the foundation for many statistical methods, such as hypothesis testing and Bayesian inference.
- Statistics is the science of collecting, analyzing, and interpreting data. It's used in data science for tasks such as data exploration, logistic regression, and decision trees.
- Calculus is the study of continuous change, and is used in tasks like optimization, curve fitting, and modeling dynamic systems.
Machine learning: Data scientists need to have expertise in various machine learning algorithms such as decision trees, random forests, neural networks, and support vector machines, as well as the ability to apply these techniques to real-world problems.
Data visualization: Data scientists need to be skilled in visualizing and presenting data effectively using tools such as Tableau, Power BI, Matplotlib, and Excel.
Data engineering: Data scientists need to have knowledge of database management systems, distributed computing, and data warehousing, as well as experience with big data technologies such as Hadoop, Spark, and NoSQL databases.
Data collection: When producing large data sets, data scientists must be skilled in data cleaning and preprocessing techniques, including handling missing values, outlier detection, and feature scaling, in order to prepare data for analysis.

Soft skills

What are the soft skills that make a good data scientist? It is easy to become mired in the technical aspects of data science, but the soft skills are of equal importance.

Critical thinking: Critical thinking is a non-negotiable skill, and is incredibly important in conjunction with statistical and mathematical hard skills. Data science involves as much observation and research as it does actual statistical analysis. As a result, you need to be discerning when examining data.
Communication: Like many other roles in tech, communication skills remain critical to data science. You'll rarely be working alone, so it's highly important that you're able to communicate complex information and technical ideas accurately and efficiently.
Problem solving: Problem solving is a mix between a hard and soft skill. Being able to chase down the root of a problem and clarify possible solutions is essential in data science. The best data scientists are able to identify, clarify, and optimally solve problems that others may not ever be aware of.
Curiosity: As mentioned above, your work will invariably involve a great deal of research. Being innately curious will go a long way when it comes to excelling in your career and learning the best approaches to solving problems.
Business acumen: Understanding the inner workings of your organization, as well as the macro and micro-economic factors at play will help you gain more insight into your research. Being aware of business factors will help contextualize your data science work and give you a better understanding of how your work contributes to business goals.

Steps to becoming a data scientist

Before you get too fixated on one aspect of your data scientist career journey, let's lay out the necessary steps to your dream job.

Get an education

For most entry-level data science positions, employers look for a bachelor's degree in a related field. Typically, related fields are engineering degrees: data science, computer science, statistics, or mathematics.

Some more senior data scientist jobs will value applicants with a master's degree in data science or another relevant field. For the most part, you won't be required to have an advanced degree, but they will help set you ahead from other applicants, especially if you also have more than three to five years of experience.

Consider your options

Regardless of your current level of education, or the goals you may have, you'll need to plan your education and consider multiple data science programs. Let's cover some of the options at your disposal, along with their pros and cons.

Four-year university

Pros: Is favored by employers, allows for wider job scope.
Cons: Time consuming and potentially very costly.
+ Master's degree: Expands the possibility of more senior roles, at the expense of time.

Associate's + Bachelor's

Pros: Same end result as a four-year university.
Cons: More affordable, but still expensive and time consuming.

Data science or coding bootcamp

Pros: High conversion rates to full-time employment, strong focus on practical, in-demand skills, typically brief (several months).
Cons: Less material can be covered, still fairly costly.

Certifications

Pros: University professor training, generally less expensive than other tuitions, less time consuming (about one year).
Cons: Better for junior roles, less comprehensive topic coverage.

Self-study

Pros: Large library of online resources, highly flexible.
Cons: Requires discipline and time, primarily for junior roles.

Hard + soft skills

Over the course of your preferred mode of education, pay attention to the hard and soft skills you acquire. Purposefully seeking opportunities to expand these skills and build new ones will go a long way when you sit down to refresh your résumé, portfolio, and LinkedIn presence.

Certain specific hard skills can be supplemented through self-study. If your education neglects to teach you certain niche skills that you feel will be valuable during your job search or in your future role, it is highly likely that you will be able to find courses online that meet your needs.

Pursue a certification (optional)

In some cases, you'll be able to translate a non-data science background into a data science role, permitting you to have a degree and work experience in a related field. This type of career change can be more easily facilitated with a certification program.

There are many different certificates to pursue, but all are grouped into one of two categories.

University certificates: These programs are typically one or two years in length, and focus on broader material related to professional development in a field of your choice. Often they teach technical skills as well, but not with the specificity of certain industry certifications. Many different higher education institutions offer data science certificate programs.

Industry certificates: These certifications are created by tech industry leaders like Google, IBM, and Microsoft. They aim to teach some of the most practical, in-demand data science skills like data visualization, data analysis, machine learning, and many more. Typically, you are expected to pace yourself.

Some of the best industry-provided data science certificate programs are as follows:

Build real-world experience

Gaining experience before securing a data science job can be challenging, especially since internships are often competitive and limited to those currently enrolled in an undergraduate data science program.

Internships are an excellent way to get comprehensive, real-world data science experience, but they aren't accessible to everyone. Luckily, there are other opportunities to build out a data science portfolio without an internship.

One effective way to showcase your coding skills and contribute to the data science community is by getting involved in open-source projects. While it can be overwhelming to know where to begin, there are resources available to help you get started. Check out this guide to contributing to open source projects on GitHub for useful tips.

Create a portfolio

Creating a portfolio is another important way of showcasing your skills and accomplishments to potential employers. Here are some steps you can follow to create an effective portfolio:

Select your best work: Review your past projects, and select the ones you're most proud of or ones that best showcase your skills. Try to include a brief summary of each project, explain its purpose, tools and techniques applied to complete it, and include visual aids to demonstrate your data visualization skills.
Create an online portfolio: There are several great platforms available to build your online portfolio, such as WordPress, Wix, Squarespace, and GitHub Pages. Choose a platform that suits your needs and skill level, and make sure it's user-friendly and visually appealing.
Be ready to discuss your work: When presenting your portfolio to potential employers, be prepared to discuss your process in-depth. If you have an interview coming up, it's a good idea to come ready with talking points related to any problems you faced during your work, how you overcame them, and the ultimate impact from your project/influence.

Keep up-to-date with the latest technologies and tools.

Working in tech often means that you are signing up to be a lifelong learner. It can sometimes feel like a full-time job of its own keeping up with all the most recent developments in your field of development; however, it's essential to stay current with the latest trends, technologies, and tools in your field.

Here are some ways you can stay up-to-date on the latest developments:

Read documentation: Reading documentation for tools and libraries that you frequently use can help you stay updated on the latest features. Documentation typically includes best practices for using said tools, so you'll likely end up learning better, faster ways to complete your tasks.
Sign up for newsletters: Data science publications, blogs, and following industry experts on social media is a straightforward way to stay informed on the latest trends and developments.
Attend conferences: Conferences are great for learning about the latest developments in the field, but more importantly, they provide fantastic opportunities for networking with other data scientists. You can generally find interesting talks and workshops to attend as well!
Take professional development courses: Online courses are another effective way to stay up-to-date on the latest technologies and techniques in data science, plus, they allow you to learn at your own pace, making it easier to balance your professional and personal commitments.

Learn in your browser: Educative has professional development covered with a massive library of content. Each course comes with a pre-built development environment, so you can code right in your browser.

How long will it take to become a data scientist?

The time it takes to become a professional data scientist will vary greatly depending on your existing level of education, experience, and portfolio.

Regardless of your individual level of preparation, it is wise to budget enough time to gain the necessary skills and experience to be competitive in the job market.

As you plan, consider the following questions:

What is your work background? Consider your previous work experience and the industries you're interested in working in as a data scientist.
What are your career goals? Determine the level of seniority you want to achieve, and what specific skills are needed to get there.
What technical knowledge do you already have? Identify the skill set you already possess and how it can be applied to different data science tasks or specializations.

Based on your answers to these questions, you can get a rough estimation for the time it will take to become a data scientist. Here are some example timelines based on different starting levels:

Bachelor's degree in data science: Starting completely from scratch, it will take you approximately four years to complete a bachelor's degree in data science.
- If you're able to land an internship during this time, you can gain additional experience and potentially earn some time back, but this will likely take between four and six years before you have a formal role as a data scientist.

Advanced degree programs: A data science master's degree typically takes two years to complete, and a PhD program takes four to five years.

Certificate program and gaining relevant skills: If you already have an engineering degree and some relevant experience, you can budget significantly less time than if you were seeking a bachelor's.
- Depending on your existing schedule, this stage typically takes a year or two.
Portfolio building and interview prep: As you might expect, this is the shortest block of time. That said, contributing to open source projects, building a portfolio, and interview prep shouldn't be rushed– especially if you're preparing for a full technical interview loop that will help you break into a data science career.
- Generally speaking, we recommend spending three months bolstering your online portfolio, and another three months preparing for interviews.

Typical salaries for data scientists in the US

Data scientists have some of the highest starting salaries of any profession in the United States. According to Glassdoor, the median total compensation for data scientists of any seniority is $127,000. This total includes a median base salary of $104,000^[1], with an average annual bonus of $23,000. In addition, the US Bureau of Labor reported an excellent job outlook for this career path, projecting a growth rate of 36% from 2021 to 2031^[2] — much faster than average.

Several factors can influence a data scientist's salary in the US:

Education: Data scientists typically hold at least a bachelor's degree in a relevant field, however, having a master's or Ph.D degree can increase the earning potential.
Experience: Salaries tend to increase with years of experience, and data scientists with 5 years or more of experience can command significantly higher salaries.
Geographic location: The cost of living and demand for data scientists can vary significantly depending on where you live. High cost of living cities like San Francisco, New York, and Washington DC tend to provide higher salaries for data scientists as a result.
Industry: Data scientists can be found in just about every industry, but are most frequently found in technology, finance, healthcare, and government.

The next steps to becoming a data scientist

Congratulations! You now have a plan for how to become a successful data scientist. The next step is to start executing it. Depending on your current level of experience, you may need to pursue different forms of education, but for now, it's time to take action and start learning.

One sure thing is that you'll need to know at least one programming language. One of the most popular beginner programming languages in the world, Python, is also one of the most popular and versatile languages used in data science.

You can get started from scratch with Python, all while coding in pre-built dev environments right in your browser on Educative. Our path Zero to Hero in Python for Data Science is the perfect hands-on introduction to both Python and data science. You'll start at the very beginning, but in no time you'll be learning real, practical data science programming skills like data analytics and data visualization. Get started today!

Happy learning!

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