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Debunking Data Science Myths: 5 Common Misconceptions To Unlearn

Debunking Data Science Myths: 5 Common Misconceptions To Unlearn

So you have set out to build your career in Data Science. But you are bombarded with a profusion of blogs, tutorials, and videos to choose from while doing your research. It’s easy for a beginner to get lost in the sea of uncurated information about Data Science. So, where do you start?

The most trending field in the tech sector is unfortunately rife with myths. The many misconceptions floating around about the field make it challenging, especially for people trying to get their basics right and break into the field.

Here are 5 myths about Data Science you need to unlearn:

  1. AI will Substitute Data Science Professionals

    This is one of the most popular misconceptions that build reservations in the minds of aspiring professionals. AI can certainly automate some of the tasks performed by these professionals. For instance, AI can detect relevant prediction features, build basic models, and produce hundreds of variations of models. But AI will not understand what a particular set of data means to an organization or its business. Data science will still require human judgment to turn data into valuable insights.

    Truth - AI is not a threat to aspiring data science professionals. They will rather turn out to be very effective resources that can facilitate complex data simulations. Besides, data science professionals are closely involved in the process of building AI.

  2. Data Science is Only Meant for Mathematicians and Statisticians

    Data Science is a combination of Mathematics, Statistics, Data Modeling, Computer Science and Programming, Visualization, and several other technologies. Yes, professionals with foundational knowledge in statistics and mathematics will have an edge in the field. But if they cannot utilize the formulae to build accurate models, their knowledge is of no value in the domain. A good data science professional needs to bring more than just statistics and mathematics to the table. He or she also needs to have good data intuition and sound business acumen among other core data science skills.

    Truth - A good statistician or mathematician does not necessarily become a good data scientist by default. Professionals from a diverse range of backgrounds can join this field.

  3. Data Science is Just Complex Programming

    Data science is all about collecting relevant data, understanding and leveraging it to form critical business decisions and strategies. The mission of a data science professional is to wrangle and analyze data using a wide range of techniques and tools. Programming is just one of those techniques employed by them. Having strong programming skills will certainly come in handy. But there is more to this domain than complex coding.

    Truth - Data Science is about building opportunities, identifying problems, and making data-informed decisions. Coding or programming is only one part of the whole process.

  4. Any Kind of Technical Experience is Sufficient to Break into Data Science

    Your previous experience and domain knowledge will help you comprehend business problems, ask the right questions, and provide quality solutions. However, in order to excel as a data science professional, you need to get your hands dirty and work with real data using the right kind of tools and algorithms. One credible way to acquire this expertise is by joining data science bootcamps; they prioritize practical learning over theoretical knowledge.

    Truth - Although your previous experience definitely adds value, you still need to acquire proper hands-on experience in relevant technologies to make a career in data science.

  5. A Full-Time Degree is a Must to Become A Data Scientist

    Of course, formal education is important and is a valuable credential in your resume. But most organizations are no longer rigid about candidates having formal degrees in data science. Regardless of your academic or professional background, credible certification programs and intensive bootcamps that offer a project-based, hands-on learning experience are effective ways of launching a career in data science.

    Truth - Formal conventional education in data science is not necessary to become a professional in this field of work. Learning to program, analyze, model, and wrangle data is definitely possible with the right guidance and hands-on learning.

    While choosing a career as a data scientist is a brilliant idea, it's important to get your hands on verified information. It's vital that you have an in-depth understanding of the field before beginning, so take some time to validate your knowledge; your understanding of the field will influence your important career decisions.

    Join OdinSchool to acquire credible hands-on experience in Data Science.

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