How to become a Data Scientist with just A Bachelor’s Degree? It is feasible, but improbable. According to the Bureau of Labor Statistics, a master’s degree in computer science or a related discipline is frequently required for employment in the field of data science.
Note that although sometimes used interchangeably, data analytics and data science are distinct concepts. Data analytics focuses on the interpretation of data, whereas data science focuses on the tools, resources, and models supplied to analysts.
Rather than delving too deeply into data science ideas, the data analytics courses for undergraduates are meant to acquire analysis-specific knowledge and skills.
How to become a Data Scientist with just A Bachelor’s Degree?
Data science is largely concerned with the discovery of profound information through data exploration and inference, and a skilled data scientist must have both the statistical expertise and computer abilities required to solve complicated problems. This field focuses on the application of mathematical and computational tools to solve some of the most analytically complicated business problems, utilizing vast quantities of raw data to uncover the hidden insight that lies under the surface.
The heart of the subject of data science focuses on accurate, often-detailed analysis, the development of solid decision-making skills, and can at times be a quiet and solitary endeavor. However, data scientists must also possess exceptional verbal, written, and visual communication skills, as they will likely be required to communicate their findings and analyses to their superiors, coworkers from different teams, and company stakeholders who may or may not understand complex statistical jargon. A data scientist must effectively communicate what they’ve discovered, how they know they’ve discovered it, and what must be done now that the knowledge is known, in a manner that is both thorough and simply understood. not usually a simple task
On any given day, a data scientist could be extracting data from a database, preparing the data for various analysis, constructing and evaluating a statistical model, or generating reports with clearly comprehensible data visualizations. Despite the fact that data science projects and duties can vary based on the organization, there are core job functions that are universal to all data science professions,
- Such as collecting and transforming enormous volumes of data.
- Using data-driven strategies and technologies to address business-related difficulties.
- Utilizing a variety of programming languages and tools to collect and analyze data.
- Having extensive knowledge of analytical techniques and instruments.
- Through the use of compelling data visualizations and in-depth reports, conveying findings and providing guidance.
- Data analysis that identifies patterns and trends and provides a plan for implementing improvements.
- Analytical forecasting; anticipating future demands, events, performances, trends, etc.
- Contributing to data mining infrastructures, modeling standards, reporting, and analytic techniques.
- Creating novel algorithms for problem-solving and analytical tool development.
- Recommend adjustments to existing procedures and techniques that are cost-effective.
Also Read: How to Become a Data Analyst
Five ways to Becoming a Data Scientist
Without a question, a job in data science may be both personally and financially rewarding, but you shouldn’t enter it carelessly. Undoubtedly, being a data scientist requires time, effort, and commitment. However, if you are motivated to make your imprint in the industry, we can help you get started! Following are the steps you’ll need to take in order to enter the field of data science.
- Choose a Path: Data science is a vast profession with infinite prospects for development and success. If you wish to become a data scientist, it will be beneficial to determine the industry in which you wish to work. Across industries, data scientists typically carry out similar responsibilities. However, the type of information you will handle, the data you will analyze, and the manner in which you will present your results will differ depending on your company and specialty. If you intend to become a data scientist, it may be beneficial to have a basic concept of where you want to work; nevertheless, you should avoid limiting yourself too soon in your career.
While data science is utilized in many industries, there are five key sectors where data scientists tend to thrive: retail, healthcare, finance, manufacturing, and transportation. These businesses have a strong demand for data scientists because they must collect enormous volumes of data, conduct precise, efficient analysis to discover trends, provide insights that aid in corporate decision making, and improve processes for their operations, clients, and consumers.
- Brush up on Essential Skills: Skills in mathematics and computer science are among the most important requirements for a data scientist position. Most data science courses assume you have a grasp of applicable principles and practices prior to coming to class; if it has been a while since you took a class or if you don’t regularly apply mathematical knowledge in your current career, refreshing your memory and studying relevant topics will be immensely beneficial.
- Get an Education:Data scientists need to have advanced degrees in the field. Even if you’re great in math and statistics, getting a job will be difficult because you lack practical experience and theoretical grounding in the field.
Obtaining a degree in data science is now simpler than ever. The three most typical educational tracks taken by data scientists are outlined here. There are advantages and disadvantages to each option, so give some serious thought before making a choice.
You can read on: 30 Best Data Science Colleges in the world 2022
- Know What to Expect from an Entry-Level Role:You should know what to expect from an entry-level data science position before applying for available openings. Data scientists typically provide analysis to enhance cost-effectiveness and financial performance, shape plans, control risk, and evaluate the success of a product or service, though the specific manner in which businesses use data will differ. You should be competent with the standard equipment of your industry, and ready to show off your skills at a moment’s notice.
- Boost Your Hiring Potential: However, while data scientists are in high demand, the field is also quite competitive. Just because you have the ability and motivation to succeed does not mean that you will automatically get noticed over other candidates.
How to Become a Data Scientist Even If You Don’t Have a degree, according to the Pros
Professionals agree that a bachelor’s or master’s degree is helpful for a data scientist career, but that it is not required. It is unfortunate that some employers favor candidates with advanced degrees, but this does not negate the general consensus among those in the field that a degree is not required.
According to data science manager William Chen, “candidates coming out of particular MS / Ph.D. programs may have advantages in data science,” but if one can already acquire data science internships/job offers, then there is no need to pursue an extra degree to increase career prospects.
In a separate Quora thread, Chen offered advice for high school dropouts who want to break into data science.
Andrew Ng, co-founder of Google Brain and an expert in artificial intelligence, was asked in an Ask Me Anything (AMA) thread on Reddit if one needed a graduate degree to work in machine learning. “No way!” he said. Earning a doctorate degree is, in my opinion, a fantastic method to educate yourself on the subject of machine learning. However, many leading experts in the field of machine learning do not hold a doctoral degree in the field.
To expand on this point, he said, “newer companies—ones that know how to evaluate machine learning talent—care more about your abilities and less about the certification (such as MS or Ph.D.).”
This topic was discussed by Daniel Carroll, principal data scientist at Aetna, during a recent Springboard Q&A.
So, I only have a bachelor’s degree, which is really unusual. When it comes to artificial intelligence and data science, I have found that many of the most talented people I have worked with had doctoral degrees. I agree that there is a preference for those with strong quantitative backgrounds. In addition, those with only bootcamp experience may face prejudice. I don’t see many fully-fledged capabilities in Ph.D. candidates, but one thing a bootcamp can provide that a respectable university can’t is a solid understanding of how to construct something from ideation to deployment.
That’s a real selling point, so be sure to highlight it in your resume and in your interviews.
Naturally, the Springboard alumni network is replete with success stories of individuals who entered the data science workforce without formal training in the field.
David Gibson was a film major who switched to marketing and then began taking the Introduction to Data Science course while still in school. He moved on to the Data Science Career Track and now works as a data and operations coordinator at a mobile-first advertising company.
After graduating with a bachelor’s degree in chemical engineering, Melanie Hanna worked in a variety of manufacturing settings before pivoting to data science.
George Mendoza came from a liberal arts background, having majored in history and economics. Shortly after completing the Data Science Career Track, he got a job as a data scientist at an AI consulting startup.
An advanced degree “certainly helps,” he said. “It’s a good signal that you can commit to something long-term, you obviously have some academic rigor behind you. But once you’re in the room with somebody, whether that’s a hiring manager or a client, it’s just you up there. It’s not you and your degrees.”
It’s no surprise that more and more people are switching careers to take advantage of the opportunities presented by the IT industry’s ongoing growth in recent years, which have resulted in the creation of exciting new positions with attractive pay.
A rising number of people are entering the digital profession without any prior experience in the field, opting instead to begin from fresh by enrolling in intensive training programs or “bootcamps” designed to teach them the skills they’ll need for their new careers. Indeed, it seems to be effective. Course Report showed that “when compared to a CS degree,” coding and data bootcamps “take less time, less money, and deliver virtually equivalent wages.” Some of the world’s most prestigious technology corporations have made it clear that they place a higher value on a candidate’s demonstrated ability and character than on their academic pedigree.