How to Become a Data Scientist in 2022 — Roles and Responsibilities

The claim that data underpins the modern world might be an overstatement — but not by much. Every commercial sector needs people who can gather, clean, assess, and draw insights out of data. If you’re a natural problem solver with a knack for analysis, you may want to consider starting on the path to becoming a data scientist.

Learning how to become a data scientist can be broken down into five steps. These include:

  1. Brushing up on your math fundamentals
  2. Identifying necessary programming languages and skills
  3. Obtaining an education via college, a data science bootcamp, or self-directed study
  4. Honing your soft skills
  5. Applying to jobs

Breaking into this highly technical and competitive industry will take serious time, effort, and dedication; although a data science bootcamp may help reduce the overall time commitment — but the journey will also afford you some exceptional career opportunities. Read on to see how you can begin making your way into the field!

Steps to Becoming a Data Scientist

No two data science career paths are exactly the same. However, you can expect to hit the following five milestones as you make your way into data science, regardless of where you land.

1. Brush up on your math fundamentals

All science fields rely on mathematics to some extent, and data science is no exception. To excel in data science, you must have a solid grasp of math fundamentals and, if necessary, expand your basic knowledge and mathematical skill set.

Data scientists work in a particularly math-heavy field. Below, we’ve listed a handful of the concepts and topics you should familiarize yourself with before enrolling in a formal data science program. Remember that most programs will assume that you have at least some fluency in the below subjects, so you should brush up on your knowledge sooner rather than later.

Functions, variables, equations, and graphs

You can’t skip over algebra and geometry if you want to make it as a data scientist. Functions, variables, equations, and graphs are often applied when analytical professionals assess data and develop insights. If your knowledge base is rusty — or absent — you may need to invest some time and effort into honing your basic mathematical skills.

Most algorithms use linear algebra to operate, so a fundamental understanding of algebraic functions is recommended. Linear algebra is particularly critical when dealing with machine learning, so aspiring professionals with interest in ML (machine learning) and AI (artificial intelligence) should prioritize the topic.


Statistics is an integral part of data science. Simply defined, statistics allow scientists to apply mathematics during data analysis in a way that empowers them to arrive at concrete and usable conclusions. Often-used statistical skills include (but are not limited to) probability distributions, hypothesis testing, Bayesian concepts, and the Central Limit Theorem.


Like linear algebra, calculus is used in many data science pursuits, particularly in teaching algorithms and AI to recognize patterns. Calculus functions like derivatives and gradient descent often arise in data science, so a solid understanding of calculus is recommended.

Discrete math

Modern data science operates with the use of computer systems. Given that discrete math is the backbone of such systems, data scientists must understand its relevant principles and practices. Proof techniques, logic, and data structures are all concepts data scientists should recognize and understand.

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2. Identify necessary programming languages and skills

Besides having a demonstrated proficiency in mathematics, data scientists must also be fluent in several programming languages, technologies, and skills. While specific skill requirements may vary between jobs and companies, most professionals will need to know some, if not all, of those listed below.


As one of the most common programming languages, Python proficiency is a must for data scientists. It is relatively versatile and, as such, can be applied to a variety of pursuits such as developing statistical models and conducting research.


R is a programming language often used for data analysis due to its statistical and graphical capabilities. Its expansive libraries encompass countless useful tools such as machine learning algorithms, time series, machine learning algorithms, and more.

Structured Query Language

SQL — which is colloquially pronounced as either see-quel or S-Q-L — is a query language that can be used to access, modify, and remove information in relational databases. It is often used in web database management and development.


Java is a popular object-oriented programming (OOP) language. OOPs are designed to allow programmers to structure, organize, update, and modify programs efficiently. Although it was initially invented to develop programs for handheld devices, Java is now often used when creating web applications. Within data science, Java is used to clean, export, analyze, and visualize data.


Hadoop is an invaluable tool for data management and storage. This open-source software framework has significant storage capacity and processing power. While other cloud-based tools like Hive and Pig serve similar purposes, Hadoop is generally considered the industry standard for data exploration, filtration, sampling, and summarization.


MATLAB is a programming language and platform that was designed with engineers and researchers in mind. With MATLAB, data scientists can create models, build applications, develop algorithms, and analyze data.

Data wrangling

In data science, industry professionals must be able to manage and comprehend massive amounts of unstructured data. Data wrangling refers to the process of cleaning, unifying, and understanding the available data. Data scientists also identify the patterns, trends, and outliers while wrangling a data set.

Data visualization

Along with data wrangling and analysis, data visualization is one of the most important skills a data scientist can develop. Analytical professionals must be able to communicate information in straightforward, concise, and comprehensive ways — and for that, data visualization is a crucial skill. Scientists may use interfaces such as Tableau, an expansive and interactive visualization platform, to facilitate clear communication.

AI and machine learning

Data scientists must have a working understanding of artificial intelligence and machine learning fundamentals, since much of modern data science utilizes AI and machine learning to improve efficiency and accuracy in data analysis. While you may not need this knowledge in your first entry-level job, brushing up on AI and ML may help you when you need to have a greater understanding of unsupervised and supervised algorithms and deep learning technology.

Having even a basic understanding of the above topics will help you when you start your formal data science education — so prioritize your self-study!

3. Obtain an education

If you’re planning on making the leap into data science, you will eventually need to invest in an education. There are many paths to explore, but here are three primary paths into the field: obtain a relevant college degree, complete a data science bootcamp, or pursue self-directed study. The route you choose will depend heavily on your existing educational background, needs, preferences, and individual situation.

Before you commit to an educational route, consider the following questions:

  • What is your budget?
  • How long do you expect your education to take? Or, put another way, how much time can you reasonably dedicate to the process?
  • How much support do you need from instructors, if any?
  • Do you plan on being a full-time or part-time student? Do you plan to work full-time while attending classes?
  • Do you have work or personal obligations that may require flexible learning arrangements (e.g., night/weekend classes, remote learning)?

Once you’ve taken the time to answer the above questions, you can determine the learning path that will work best for you. To assist in the selection process, we’ve labeled each of the below options to reflect time, budget, and flexibility.

A graph showing how the top educational options for data scientists compare to one another.


Commitment: Full-time
Time: 4–7 years
Budget: $$$

College is a popular route into data science. According to a recent report from Burtch Works (PDF, 1.1 MB), a full 83 percent of predictive analytics professionals (PAPs) have an advanced degree (68 percent Masters, 15 percent doctorate). However, postgraduate study is not strictly necessary for all aspiring data scientists. The same study also found that the number of PAPs who obtained a bachelor’s as their highest degree had increased by three percent over the prior five years.

Data science degrees are popular among data scientists because the programs are comprehensive, thorough, and accredited. More colleges offer data science and data analysis programs now than ever before. Cross-disciplinary tracks allow students to get the data science skills they need while also providing them with universally applicable tools that can help them succeed in any field where they can conduct data analysis.

All this said, college programs aren’t the go-to educational answer for everyone. Multi-year programs are long and often more costly; they may not suit those who need to balance their education with a full-time job or family responsibilities. If your current circumstances limit your availability or financial flexibility, you may want to consider an alternative educational route.


Commitment: Full-time, part-time
Time: 6 months
Budget: $$

If you want a formal education, but a college program is not appealing or feasible, you might consider participating in a data science bootcamp. Bootcamps are well-suited for those who already have an undergraduate degree or have relevant experience and need to brush up on their skills, and they tend to be more economical, faster, and more accessible than more extended programs.

These programs are intensive, as they typically fit a comprehensive curriculum into the short span of a few months, allowing learners to quickly gain the skills necessary to pursue an entry-level data science role. If you have a background in data science, mathematics, or other relevant studies, a bootcamp may be ideal as it will provide you with an efficient, guided program that expands on your existing knowledge and exposes you to the programs and skills you need to become a data scientist in an entry-level role.

Those who do not have the time or funds to commit to a full college program may want to enroll in a bootcamp to achieve their career aspirations more quickly.

Self-Directed Study

Commitment: Full-time, part-time
Time: Variable, depends on personal motivation and scheduling
Budget: $

For the intrinsically motivated, a self-guided education may be sufficient. The internet is host to plenty of resources, both free and paid, that you can complete on your own time. If you are disciplined, hard-working, and committed to educating yourself, a self-guided approach could allow you to learn according to your own timetable.

Keep in mind that, of the three options presented, independent study is generally the least attractive to potential employers — if only because such an education does not come with formal credentials or accolades. Self-directed study is, as the name states, entirely determined by you.

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Whichever educational path you choose, keep in mind that you will likely need to put in a considerable amount of time and effort to master the languages, technologies, and other skills and experience necessary to become a data scientist.

4. Hone your soft skills

side from technical skills, data scientists also need a number of soft skills to do their jobs well.

Take critical thinking and problem solving as examples. Both soft skills are essential because data scientists must be able to view data with a critical eye to ask the right questions, identify patterns, address challenges, and make accurate conclusions about the data in question.

Additionally, as data visualization is an essential part of the data scientist role, effective communication must be a top priority; data scientists need to convey their conclusions in a manner that is simultaneously comprehensive and easily understood by their audience.

The best way to develop soft skills is through practice — aspiring professionals should make a habit of thoughtfully analyzing problems, asking good questions before drawing conclusions, and making a dedicated effort to be clear, honest, and thorough in their communication efforts.

Training for such skills is often baked into formal programs such as college courses and bootcamps. However, individuals who study independently may need to make a more intentional effort to seek out soft skill practice.

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5. Apply to jobs

Once you have cultivated the necessary skills, proficiencies, and experience, you can begin your job hunt. While the industry is competitive, the data science sector’s rapid growth means there are plenty of opportunities available. Finding one that suits your preferences, passions, and skill set will be key.

When looking for a job, you should set clear goals and specific intentions; holding yourself accountable will be easier to accomplish if you feel passionate about the process, so tailor your search to positions or companies that appeal to you.

Don’t be deterred if you feel underqualified for some entry-level postings. Imposter syndrome is normal, so don’t underestimate your value; apply anyway. At worst, you’ll receive a polite decline — and at best, you’ll get a new job!

Be sure to routinely update your LinkedIn profile and resume; you may even want to make edits to your resume between each application if those changes make you a more appealing candidate for a particular role. Remember that you are allowed to make updates to better suit each application you submit. Doing so is strategic; what you omit from one application could make a big difference on another.

How Long Does it Take to Become a Data Scientist in an Entry-Level Role?

Timelines matter. Below, we’ve outlined how you might upskill within several reasonable time frames. As you begin pursuing a career as a data scientist, you should keep these time projections in mind.

4+ Years

Without a background in mathematics, science, or programming, the timeline you should account for will likely span at least four years, or longer, if you pursue an advanced degree.

Starting without any prior knowledge will require a more thorough and comprehensive education. This process can take between four and six years for anyone who is just beginning their data science education. Those who do not already have an undergraduate degree should anticipate this and plan accordingly.

2 Years

Pursuing a self-guided education may be an appropriate option for someone with a background in a relevant field. Still, depending on your availability and employment or education status, it may take a considerable amount of time for you to learn all of the essential languages and skills. Learners should expect to devote at least two years of hard study before attempting to find a job as a data scientist.

3–6 Months

If you have a background in relevant studies, you may be able to gain the foundational skills to enter the data science industry and find an entry-level role on a more accelerated timeline than those without any previous experience. By enrolling in an intensive data science bootcamp program, for example, you can pick up the skills and proficiencies you might lack, and most bootcamps can be completed within six months.

Roles and Responsibilities in Data Science and Analytics

While data scientists all possess a similar set of tools and proficiencies, the roles you can fill within the industry can differ significantly. These include (but are not limited to) the following:

Data Analyst

Generally speaking, a data analyst is responsible for interpreting data and conveying their findings to help make broad decisions for their company or organization. This role is primarily focused on analysis and communication, and their responsibilities include:

  • Gathering and assessing data
  • Identifying patterns in data sets
  • Reporting data conclusions to other relevant parties
  • Establishing new data collection, analysis, and dissemination processes


Statisticians operate similarly to data analysts but are primarily focused on the collection and analysis of numerical data. Their responsibilities may vary depending on their employer, but some everyday duties include:

  • Creating surveys and questionnaires
  • Conducting data scrubbing to improve accuracy
  • Undertaking research and development tasks
  • Performing statistical analysis to identify trends and relationships within data sets
  • Combining statistical findings to convey to relevant parties

Data Engineer

Data engineers play an integral role in data science by managing and improving the processes of collecting, storing, and sharing data. Common responsibilities include:

  • Developing and testing architectures
  • Improving data acquisition, accuracy, and quality
  • Integrating advanced methods and programs, often with machine learning applications
  • Identifying hidden patterns and opportunities for automation

Data Scientist

Data scientists assess massive amounts of structured and unstructured data to provide relevant parties with important insights. Their role is meant to supply meaningful conclusions from raw data, and specific responsibilities include:

  • Using appropriate software to manage, manipulate, and analyze large data sets
  • Applying algorithms to predict outcomes
  • Identifying variable relationships and assessing statistical performance

Machine Learning Engineer

While data scientists and machine learning engineers both deal with large data sets, machine learning engineers primarily work on developing, testing, and integrating self-running software that utilizes AI and machine learning. Their responsibilities typically include:

  • Researching and developing automated technology
  • Managing large data sets to improve self-running software
  • Identifying and solving algorithm errors as they arise

How Much Do Data Scientists Make in New Jersey, Compared to Nationally?

Generally speaking, data scientists enjoy relatively high salaries thanks to their highly technical and in-demand skill sets. Some people may earn more or less depending on their applicable experience and knowledge base.

On average, data scientists earn $100,560 annually, or $48.35 hourly. That said, wages can vary across geographical areas; the mean wage for New Jersey, for example, is $115,480 — significantly higher than the national rate.

A graph showing how much data scientists make in New Jersey compared to the national average.

As you look for data science employment opportunities, it may be beneficial to keep the remote work trend in mind. As more workers relocate to lower cost locations during the pandemic-prompted remote work revolution, salaries may adjust accordingly (PDF, 1.1MB) to make the job market more equitable across geographies.

Because of the nature of data science work, much of what you will be doing can be done remotely; your employment options may be even more plentiful and flexible as the remote work trend continues.


A career in data science is expected to be profitable for the foreseeable future due to a consistent demand for skilled candidates. If you have a background in math, science, programming, or other related industries, you will likely be a good fit to start on the path to a data scientist role.

Similarly, if you are excited by the prospects of data science and are planning to pursue this industry at the start of your professional life or make a permanent shift in your career, your hard work and dedication will surely pay off over time.

Want to get started? Take the first step on your career journey by checking out Rutgers Data Science Bootcamp.

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