How to get into Data Science without having an engineering degree?
In the technical world, new skills and roles keep on growing faster than our traditional academic studies. One such field is Data Science; the most attractive feature of this domain is it doesn’t require any prior technical knowledge or a degree in engineering. An individual who is open to work in a competitive environment and wants to learn advanced skills has welcomed this field with open arms.
There is a common myth that one can become a Data Scientist only after completing an engineering degree from a renowned university or Institute. Well, there is no compulsion to start a career in the domain, if you are a student or even a working professional you can spread your wings easily. We have delineated the right approach to commence the journey in the domain if you do not possess an engineering degree.
What exactly is Data Science and its Job Profiles?
The initial and most crucial step is to get a basic understanding of Data Science, and the different job roles the domain offers. In the simplest form, Data Science is a field of collecting and analysing data assisted by technology.
Data Scientist is not the only job offered in this domain; there are several other profiles that one can choose, this includes:
- Data Analyst
- Business Analyst
- Big Data Engineer
- Data Engineering
Before moving ahead towards Data Science, one needs to identify the right job profile that can match their capabilities well.
It is preferable to have a clear idea of which kind of industry one can adapt to in the current dynamic environment. As you will be starting a journey right from the scratch, thus it is of utmost crucial to list down all the profiles for you, as one mistake can be catastrophic for your career.
The skillset required to be a Data Scientist
- Mathematics and Programming Languages: To be an effective Data Scientist, the requirement of Mathematics has always been the topic of debate. Some say mathematics is required while others say one just needs statistical information; there is no need of having in-depth topics knowledge. Well, the fact of the matter is that you require strong command over major mathematical concepts like Algebra, Linear Equations, probability, numerical ability, and others.
- Programming Languages: The ability to program can help Data Scientist in numerous ways. They can easily write automated scripts to perform the most time-consuming tasks in the domain i.e. cleaning, preparing data for analysis. They can easily write down scripts to transform data into different formats. The most trending programming languages that one must learn are R, Python. Being comfortable with R and Python is ideal for future internships and jobs.
- Machine Learning: Machine learning is a means by which our computers can learn to do tasks quickly without being programmed by someone. To make your decisions more effective, you train your hands-on machine learning, as it also helps in doing the right predictions on the data. You could use Machine Learning to tackle fraudulent and non-fraudulent transactions as training data for a particular model as well.
- Big Data: Along with the above mentioned, Data scientists need to access data in big amounts all the time. Thus knowledge of databases such as MYSQL is very important to perform daily based tasks. One needs to generate data from multiple sources at a high velocity which can be managed by the relational database.
What After acquiring skills?
- Creating Portfolio- After gaining theoretical knowledge, having practical knowledge as well will help you to be successful. People can take certificates from anywhere and can add fake skillset in their resumes. So, to make your portfolio, experts recommend you to make projects on WordPress and Github. Your portfolio would help you to get real-time projects, which will then assist you to sharpen your skills further.
- The Data Science Community Always look for conferences and summits aiming to impart knowledge on Data science. Not only would you learn from these kinds of events, but will also get an opportunity to build a solid network. Even the local Data Science community will help you in building networks and in getting projects as well.
- Prepare for Data Science Interview After thoroughly understanding the data science and its job profiles, update your resume as per the desired job role. Start reaching out to the network you have created and highlight all the personal objects without too many buzzwords in it. It is good to seek mentorship for a better understanding of Data Science and always watch out to enhance your skills.
- Conclusion As the technology doesn’t remain constant, professionals must stay updated with the latest trends and skills that are going to take over the market in near future. We recommend you to read blogs related to Data Science and check the job requirements to get aware of the demands and shortcomings. Learning should never stop as it helps to grow both professionally and personally.
As W. Edwards Deming says, “In God we trust. All others must bring data.”
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