With the increase in data, job opportunities for data scientists are skyrocketing. This is why students today are exploring the field of data science to make a rewarding career for themselves. Students find it easy to become data scientists if they have a strong background in the following subjects.
- Science
- Technology
- Engineering
- Mathematics
But if you are from a non-math-oriented or tech background, then it should not deter you from dreaming of becoming a data scientist. The career journey for non-tech students to become data scientists might be complex and full of hurdles; however, it will require a lot of effort and proper guidance to acquire the necessary mathematical, statistical, and coding knowledge.
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Can non-IT students become data scientists?
It is not so straightforward to enter the tech field of data science if you’re from a non-IT background. Non-IT students can become data scientists if they manage to acquire all the skill sets and knowledge base to work with large datasets. One of the best ways to make a career in data science is to get the basics right before directly jumping into higher applications. You can engage with the data science community, connect with industry veterans or mentors to learn about this tech domain, and keep abreast of all the trends and developments.
Tips to learn data science for non IT students
Following are the simple steps that you can follow to get into a data science career if you’re coming from a non-technical or non-IT background.
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Learn data science skills by enrolling in a certification course program.
The career field of data science is vast and complex, encompassing various algorithms, models, methods, practices, and software. To kickstart your career in data science as a beginner with little or no coding language, you need to upskill yourself by enrolling in a well-recognized certification course program that top course providers or educational institutions impart. The data science course curriculum includes the following topics that help non-IT students to grasp the fundamental aspects of data science
- Deep learning
- Basics of programming languages such as Python and Java
- Statistics and probability
- Data visualization
- Handling big data
One of the most organized and easiest ways to approach data science learning is to enroll in a professional certification course instead of researching alone, as it will waste your time gathering relevant study materials and resources. These certification courses are offered by industry experts who will guide you and help you to acquire the necessary data science skills. After completing the course, you will be awarded a data science certificate that your hiring manager or recruiter will recognize. In addition, the data science certification course has many benefits, such as placements, career counseling, mentorship programs, etc.
2.Acquire programming skills
The job of a data scientist requires them to use various tools, technologies, and programming skills. Programming packages like Python and R help them to derive correct interpretations from vast volumes of data. If you come from a non-tech background, choosing the right tool or programming language may be intimidating or overwhelming. Taking offline or online classes will help you get introduced to programming languages and tools. Enrolling in the course program will help you pick up programming skills and tools quickly. You can use these tools and programming languages while handling data science projects to get a true feeling of how to handle complex and massive volumes of data and perform the job role of a data scientist efficiently.
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3.Look for a mentor
It is essential to find a good mentor for yourself who will help you guide you throughout your data science career journey. A mentor is someone who can help you reach your career destination. With the guidance and suggestions given by your mentor, you will find it easy to solve your doubts and enter the data science industry. Once you find your mentor, you can ask all your doubts and questions without any hesitation. You can also accompany them to seminars, networking events, boot camps, and certification courses to get started in this domain.
4.Hand real-life data science projects
Gaining practical experience and training is essential to secure a good data science job in top multinational companies. For this, you must build a portfolio of all the data science projects you have worked on and solve real-world business inefficiencies and bottlenecks. Going for a focused project learning process is essential to stand out in a crowd and get a competitive edge. Real-life data science projects will help you acquire theoretical concepts in the practical fields and add more credentials to your resume. Enrolling in a data science certification course also allows you to handle practical projects and get exposed to the data science industry. These courses will help you develop theoretical knowledge to apply in data science-driven projects.
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5.Fill up an entry-level business analyst or data analyst role.
The role of a data scientist requires many skills and knowledge. He or she must devote years of practice to mastering all the data science techniques. One of the best and most effective ways to get started in the field of data science is to fill up the entry-level data analyst or business analyst job role that will help you acquire practical experience. You will learn a lot while working in these job roles, which will hone your experience.
6.Keep learning and reading.
The tech domain of data science is constantly evolving with each passing day, and more innovative and modern tools and technologies are coming up. You need to keep learning and reading informational and non-fiction books to keep yourself updated about this feeling. Reading long-form journalism articles and papers written by data science industry veterans will help you to get insights about the latest industry trends and grab good job opportunities. It will also help you to know what data science skills recruiters are looking for in their employers, and in this way, you can streamline your learning goals.
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