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Top 3 Mistakes a Newbie in Data Science Should Avoid

Whopping salaries, most in-demand jobs, and trending careers are some of the major reasons why most people tend to opt for careers in data science.

As much as the thought of it sounds alluring, starting a data science career is not a cakewalk. Despite the dearth of talent in the field, we often find aspiring data scientists committing certain mistakes while getting into the industry. Such mistakes may become a stumbling block to your career. Therefore, we will further address the type of mistakes and how to avoid them while seeking a career in data science.

  • Devoid of focus

Lack of focus does not mean you always have to be a go-getter, it simply means you’re required to have some kind of focus in making the right decision. You need to find the niche; data science is a complex field. However, you will need different types of key skills for every data science job you’re seeking to pursue. For instance, a data analyst’s job skills are not the same as it is for a data scientist nor a data engineer. The job roles are different and so will the skills be.

An ideal way to stay focus is by doing extensive research on the job role you’re looking to begin your career. Find what you’re looking for, stay focus, and find your niche. Most skills will remain the same, however, your emphasis might shift according to the job role you have in mind.

Not to mention, even the type of industry makes a difference for a data scientist. Data science professionals hired in the retail industry or the healthcare industry might find themselves working in a completely different environment. The working style may differ but the technical skills will be the same for every data scientist irrespective of the industry or domain.

  • Underestimate formal education

In the present day, most tech industries are on a hiring spree for data scientists with no formal education or little formal education. This is noticeable for junior positions. Such aspirants are seeking skills through data science certification programs. To be precise, any aspirant having knowledge of computer science can learn the fundamentals of programming languages within six months, thus, are quite eligible to land an entry-level job.

However, you should also know that nearly 40 percent of data science jobs require candidates with a master’s degree or even higher, says a report by Burning Glass. And the best part about a career in data science is that even data science universities and colleges have started offering online programs making the learning stage easier.

  • Neglecting projects or hands-on experience

Formal education is significant for you to start a data science career than most technical jobs. However, this also does not mean that the candidate neglects hands-on experience. Candidates having a portfolio of projects stands one step higher in preference by potential employers. You can do multiple things before applying for a data science job. For instance, get an internship from renowned data science companies. If not an internship, build your own project. For this, you can use Kaggle. Kaggle gives you the opportunity to learn and compete with other data scientists, perhaps work on a project together. Remember in data science having hands-on experience is a must-have.

Aspiring data science professionals can easily learn through multiple open source projects available online.

Stack Overflow and GitHub are other platforms wherein you can showcase and demonstrate your data science skills. Participate in competitions or stay engaged with different discussion groups. Doing so also keeps you in sync with the current data science affairs.

Begin working on projects and start building your portfolio.

Niti Sharma is a professional writer, a blogger who writes for a variety of online publications. She is also an acclaimed blogger outreach expert and content marketer. She loves writing blogs and promoting websites related to the education and technology sectors.

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