The vast field of data science comprises some of the most trending technologies like Data analytics, Machine Learning, Data Visualization, and modeling. Data science consists of various phases where data is collected based on hypotheses and building models to derive insights and analysis for businesses. Being a Data scientist is one of the most coveted jobs today, and Glassdoor ranks it as the second-best job in the united states in 2021. With the large volumes of data available, thanks to the vast outreach of the internet and smart devices, the need for data scientists is high. Many candidates across different domains aspire to make a career in this field.

But there are many common misconceptions associated with the role of a data scientist. The more people talk about technology, the more misconceptions and myths pile up. We’re going to debunk some of these popular myths and misconceptions revolving around data scientists.

Myth #1:You Must Have a Degree in Data Science to Become a Data Scientist.

This is one of the biggest misconceptions in the industry and academic sector. A college degree in computer science is not all that it takes to become a data scientist, and a degree is not a requirement to have a data science career. A better alternative path is to build an impressive portfolio with work experience and relevant certifications, which you can gain from MOOC courses. A Ph.D. or a master’s degree in Data science or computer science will be helpful, but working on real-world projects, building strong ties in the data science community will go way further and help you reach your goals.

Myth #2:You have to be an Excellent Programmer to be a Good Data Scientist

People wrongly assume that data science is synonymous with coding. It is a common misconception that being a data scientist means writing hundreds of lines of codes. Even though it is an important skill and prior programming knowledge can be helpful, you dont need to be an expert in programming to get started in data science. Most algorithms and models are readily available and can be used with minor tweaks. Picking up some programming skills will help you in the long run. A crash course in programming languages like python or R should get you going on your data science journey. When it comes to data science, your ability to organize data, identify patterns, analyze the data, create visualizations, and sometimes even do market research and recommend solutions is far more critical than your ability to write code.

Myth #3: Data Science is all about the tools, and learning a Tool is Enough to Become a Data Scientist.

The common assumption held by people is that using existing libraries and techniques in R, SAS, or Python, or any other tool is enough to make you a data science expert. These skills enrich your portfolio as a data scientist, but data science is not just about using tools. It is about applying your knowledge and skills to the specific data at hand for the particular problem statements. It’s about communication skills, work ethics, and problem-solving skills. Understanding how a specific technique or algorithm works will help you become a better data scientist. Equipping yourself with all these will make you a better data scientist and think outside the box when necessary. While mastering data science tools help you build solutions faster, knowing to use the right tool in the correct scenario is the skill you need to succeed.

Myth #4: You must be brilliant at mathematics and statistics to be a data scientist.

People think it’s necessary to be a genius mathematician to be a data scientist, and they couldn’t be more wrong. Data science uses mathematical concepts like probabilities, regression, and statistical analysis, which are fundamental skills that people tend to grasp quickly. A data scientist should strive for the ability to find patterns and analyze data. Being a data scientist involves working with a team. Data scientists are cross disciplinarians with their strengths and weaknesses in coding, statistics, business knowledge, and communication skills. It is indeed possible to become a data scientist with a basic understanding of mathematics and statistics and excellent coding skills.

Myth #5: “Business Analyst” and “Data Scientist” are the same job profiles.

A common misconception is that data science is just a new name for business analysis. While a data scientist also performs business analysis tasks, A business analyst is not a data scientist. A data scientist analyzes data and identifies patterns and insights, whereas learning about the customers and the audience is Business Intelligence. Data science analyzes the data to understand why something happened and whether it will happen again. If you want to study and predict how changing a specific process will affect your business, a data scientist can analyze and provide insights.

Myth #6:There’s no creativity in data science.

Data science might not seem like a creative field. Most people assume that data scientists follow a rigid system, where they load data, build a model and follow the steps to find a solution. But it would not be right to underestimate the role of creativity in data science. There is no solution where ‘One shoe fits all.’ There are endless ways of interpreting data. Creativity plays a significant role in manipulating, analyzing, and extracting meaning using predictive models and building solutions. Data visualization and exploration require creativity and innovation to communicate data insights effectively.

Myth #7: Data Science is a lifetime career.

It is a common misbelief among people to assume that data science is a lifetime career. Even though data science has multiple job roles and career paths, the domain will evolve over the years. Some decades ago, it was unfathomable to think about the artificial intelligence used daily in our lives today. Jobs like data entry, computer programing, and operators are already on the decline with the advent of Artificial intelligence and computer vision.No technology lasts forever. Data science will lead you to a great career path if you keep learning new skills and updating yourself.

Demand for Data Scientists is already sky high, and the aspirants need to make the right career move by equipping themselves with in-demand skills. But the amount of inaccurate information on the internet may discourage people from pursuing data science. We hope this article helped you get a realistic look into data science while busting some popular myths. Do not be disheartened by the myths, and continue learning and exploring this field to find what suits you the best.

Also Read: 3 Important Careers for Business-Focused Data Scientists