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How to become a data scientist in 2023

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How to become a data scientist. If you’re interested in learning how to become a data scientist, you definitely have a passion for statistics. Perhaps you appreciate the challenge of arithmetic problems, or perhaps you take pleasure in reading data and analyzing spreadsheets that your peers find tedious or difficult to understand. You’ve always enjoyed solving these riddles, but now you want to put your analytical talents to the test and see if you can turn them into a career.

Don’t give up if this description doesn’t exactly describe you. Whether you are an experienced, educated mathematician or simply a hobbyist with an interest in analysis, you can become a proficient (and employable!) data scientist with the correct tools, enough time, and effort.

Also Read: Berkeley computer science acceptance rate

5 Steps on How to become a data scientist

Without a doubt, a career in data science has the potential to be both personally fulfilling and financially rewarding, but you shouldn’t make that decision lightly. Make no mistake: it takes time, effort, and dedication to become a data scientist.

We can assist you in beginning your journey if you’re determined to leave your mark on the field, though. The steps you must take in order to become a data scientist are discussed below.

1. Choose a Path

Data science is a wide area with a ton of room for development and success. It will be helpful if you decide the industry you want to work in after becoming a data scientist to know that.

Across industries, data scientists typically carry out comparable tasks. However, depending on your company and sub-field, you may manage different types of information, analyze different things, and present your findings in different ways. Though you should be careful not to limit yourself too early in your career, having a basic notion of where you want to work may be helpful if you wish to become a data scientist.

The majority of industries employ data science in some capacity, but there are five main industries where data scientists tend to do very well: retail, healthcare, finance, manufacturing, and transportation. Because they must gather enormous volumes of data, do precise, effective analysis to spot trends, offer insights that help with business decision-making, and optimize processes for their operations, clients, and consumers, these sectors frequently have a strong demand for data scientists.

2. Brush up on Essential Skills

The two fields of computer science and mathematics are at the top of the list of prerequisite abilities for a data scientist position.

Refreshing your memory and studying pertinent topics will be very helpful if it has been a while since you last took a class or if you don’t frequently use your mathematical knowledge in your current career. This is especially true for data science courses, which typically require you to have a firm understanding of relevant principles and practices before enrolling. Among the math abilities future businesspeople can brush up on are:

  • Linear Algebra
  • Statistics
  • Calculus
  • Discrete Math

Technically speaking, knowledge of coding languages including Python, R, Java, SQL, NoSQL, and HTML will be required. Additionally, competence with sector-relevant software like Tableau, which enables data scientists to separate data based on variables, or Hadoop, which is excellent for data management and storage, can increase the marketability of aspiring data scientists to potential employers.

Soft skills like communication, critical thinking, and problem-solving are also very important. Data scientists should be adept at spotting problems, coming up with and testing solutions, and effectively communicating their conclusions. You may want to set aside some time to study if you don’t already have a firm grasp on the mathematic, technical, or soft abilities described above in order to be ready for your data science training. It will be beneficial to make a concerted effort to deepen your comprehension and hone your skills, even if you already have some relevant knowledge or experience.

  • Get an Education

Data scientists must have specialized education. No matter how talented you are in linear algebra or statistics, you won’t get a job if you don’t have the necessary practical experience and academic background.

Thankfully, it’s simpler than ever to get a data science degree. We’ve included the three typical academic routes that aspiring data scientists frequently take below. Think carefully before choosing a path because each has its own set of advantages and disadvantages.
Although it’s not always necessary for prospective data science workers to hold a formal degree, doing so is a popular educational choice and can help make up for a lack of work experience. Predictive analytics professionals (PAPs) hold advanced degrees in the majority (83 percent), with 68 percent holding a master’s degree and 15 percent holding a doctorate, according to a 2020 Burtch Works report.

You must make sure you have adequate knowledge of pertinent ideas and practices if you want to conduct a successful data science profession. A Master’s in data science may be an appropriate option for you if you have prior experience with math or coding. Such programs are an excellent alternative if you want to obtain a thorough and recognized education in the area; many even provide flexible online choices for working students these days.

Don’t panic, though, if you’re just getting started and want to enroll in an undergraduate program. The Burtch Works study also found that the value of extra education has decreased recently because more people now hold bachelor’s degrees as their greatest level of education, up 3% annually. Without a graduate degree, you might be able to obtain an entry-level data science degree.

Naturally, this educational route is the most costly and time-consuming, so if these factors are constraints for you, you might want to look into other options.

Read Also: Best 12 Data Analytics Certifications Online for 2022

  • Know What to Expect from an Entry-Level Role

You should be aware of what an entry-level position entails before applying to open data science positions.

Data scientists frequently provide analysis to improve cost-efficiency and financial performance, influence strategies, manage risk levels, and assess product or service quality and success. The ways that businesses use data will vary. It is necessary to know how to use the tools of the trade, so you should have confidence in your skills and be able to prove them if necessary. The primary responsibilities of an entry-level data scientist include:

  • Setting up and managing studies to facilitate data collection
  • Building tools to automate data collection
  • Reformatting data to make analysis easier
  • Parsing large data sets for usable information
  • Using data to develop strategic business conclusions
  • Creating reports and presentations

Industry requirements are an additional factor to take into account; when looking at job descriptions, you should note which data science components are utilized in the position. By processing and extracting knowledge from vast amounts of data, data scientists offer a crucial service to businesses, governments, and other organizations. Having said that, each company or organization may have different priorities, objectives, and constraints, so any prior knowledge you may have will be helpful.

In the end, data scientists must comprehend data in order to perform entry-level tasks, including how to process, gather, and analyze data as well as understand its origins. Even though each open position will undoubtedly have its own quirks, entry-level data science jobs typically have a consistent set of requirements. Before submitting a job application, you can more effectively tailor your resume, professional portfolio, and other materials with these in mind.

  • Boost Your Hiring Potential

Although there is a growing need for data scientists, the field is also very competitive. You need to stand out from your peers in addition to having the abilities and motivation to use them.

Let’s go over a few techniques for doing this. In order to further your career, networking is essential in any industry.

One of the simplest and most efficient ways to establish professional connections is to connect with data science experts on social media platforms like LinkedIn or Handshake, where you may connect with graduates from whatever schools you have completed. When compared to doing an Internet search or putting a resume on a job board, this will open up more chances for you.

You can still network through digital conferences and seminars even when the pandemic has temporarily made certain conventional networking activities useless. Attending or taking part in such events can, at the at least, increase your knowledge and help you gain a better understanding of the industry as a whole. Additionally, you don’t have to wait until you’re prepared to look for work. While pursuing your data science studies, keep an eye out for any pertinent happenings.

FAQS on How to become a data scientist in 2022

Who Can Become a Data Scientist?

There are no restrictions on who can acquire the knowledge, training, and experience necessary to become a data scientist, which is fantastic news for anyone wishing to switch careers to the field of data science. Your potential is not constrained by your age, subject of study, or job. If you put in the necessary effort, have the correct mindset, resources, and education, you can succeed as an entry-level data scientist whether you've just graduated from college or are getting near to retirement age.

Even though you will need to put in extra effort and it is advantageous to have a background in math or computer science, it is possible to gain the necessary skills and knowledge to work as a data scientist at an entry-level position without a formal degree. If you already possess some pertinent experience and education, the process of getting hired for an entry-level data scientist position might go more quickly. On the other hand, there is no imminent deadline for the start of your data science profession.

How Much Do Data Scientists Make in Atlanta, GA, Compared to Nationally?

You should think about how much money you may anticipate to make in a new position before committing to a career shift, particularly one that will call for more education. This procedure can assist you in learning about the employment opportunities in your new field; by doing this research, you can more effectively plan your financial future.

In the United States, data scientists currently make an average salary of $100,560. Salary possibilities can be highly variable and influenced by a wide range of elements, such as background, experience, and geography. The most data scientists are employed in states like California, Illinois, and New York. Data scientists in these states typically earn more than the national average.

The mean compensation for data scientists in Georgia is $81,520, which is lower than the national average. Despite this, data scientists in Atlanta's urban center should anticipate to earn more than the state average - the city's typical data scientist income exceeds $90,040. Atlanta, Georgia has generally lower compensation possibilities, although the industry is predicted to grow quickly, boosting the possibility of a more competitive work market and greater wages.

What’s the Difference Between Data Science and Data Analytics?

In a way, data science is an extension of data analytics, expanding on the duties of storing and analyzing data to include the development, creation, and upkeep of machine learning models intended to speed up data analysis and prediction.

It's common for data scientists to start out in the field as data analysts. Since programming, analysis visualization, and other abilities necessary for the former role are equally applicable to the latter, it makes sense. The key distinction is that data scientists employ algorithms, predictive models, and prototypes to create and build new data modeling methods, whereas data analysts concentrate on finding trends and drawing strategic conclusions from massive data sets.

Although there is considerable competition for both positions, demand is often stronger for data science than for data analytics. A data scientist will be preferred above an analyst by businesses that wish to complete specialized tasks including integrating cutting-edge technology, artificial intelligence, and machine learning into their data models. Analysts, though, might find more employment chances with businesses that don't place as much emphasis on technology.

Why is there a demand for data scientists?

There has always been data. We didn't have computers that could store and process enormous volumes of data back then. Therefore, we concentrated on developing tools and systems that are capable of processing and storing vast amounts of data. We improved the speed and efficiency of our machines, but we did not keep up with the rate of professional development. This gradually widened the gap between those working on technical advancement and those who were not. Data scientists are present here. When you think about the career path of a data scientist, supply and demand is a straightforward equation. There is currently an enormous amount of data that needs to be interpreted, yet there aren't enough experts to properly calculate this data. This has created a sizable gap that qualified applicants can fill by picking up the necessary skills.


Being a data scientist can be a great choice if you’re hoping to make a substantial career move this year. If you have some background knowledge or experience, are committed and disciplined enough to keep yourself accountable, you can make this move over a few months.

You can significantly improve your chances of landing an entry-level data scientist position in 2022 by paying attention to the aforementioned advice. The job prospects, salary range, and opportunity to start meaningful changes within this role are undeniably appealing.

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