Being a data scientist is one of the most sought-after jobs in recent years, and why shouldn’t it be. Considering how much data scientists make right after graduation and the myriad of other benefits, it only makes sense that more and more people want to join the field. However, as with anything worth pursuing, data science is not an easy field to get into. It requires a substantial amount of hard work and commitment. Data science is such a diverse field that, unlike other areas, you would have to learn multiple skills. These include – programming, mathematics, statistics, machine learning, AI, and business.

The good news is that currently, you have multiple different resources to pursue data science if you think you are a good fit. You can either go down the formal route of acquiring a graduate degree, or you can self-learn by taking online courses. Whichever option you choose, the important thing is that you have the correct aptitude for learning the relevant skills.

This article will tell you how you can become a data scientist and ask some critical questions to determine if you are the right fit.

What is data science? 

Data science uses scientific methods, mathematics, statistics, and algorithms to derive considerable data sets insights. A data scientist’s job is to use many skills to analyze data and then predict trends based on the data’s insights. Facebook, Google, Microsoft, Twitter, and other tech and web companies rely heavily on data science to optimize their infrastructures. Private businesses and industries also use data science to predict profits and sales by analyzing big data sets.

Can I become a data scientist?

Before I dive into how you can become a data scientist, you must know what it takes to become one. I always advocate that a person should follow their natural aptitudes. We have been told this lie for all our lives that we can do anything we want. However, I think that statement should be followed with some caveats.

The truth is we are all different, and this is not something to shy away from. Instead, it should be embraced because our diversity is what makes us unique. If a person has a natural tendency for art, it would be counter-productive to thrust them into a field they do not incline, for example, molecular science.

Anyone interested in becoming a data scientist should do it for the right reasons. If your goal is a high-paying job only, then that is not a proper reason. However, if you love working with numbers and have a curious mind, then data science might be a suitable choice for you.

Straight-forwardly put, data science is an incredibly quant-heavy field. As previously mentioned, data science relies on various areas such as mathematics (algebra in particular), statistics, programming, machine learning and, some level of business acumen.

Not only will you be required to organize and manipulate extensive data sets, but computers would surround you at all times. You will need to utilize programming languages to solve problems and come up with innovative solutions.

If all this sounds like something you have a passion for, you are a good fit for data science. However, if you know that you lack in the areas above, then perhaps you should reconsider.

How To Become A Data Scientist?

Method # 1 Degree in Data Science

If you want to earn a formal education in data science, enroll in an undergrad data science program or a closely related field. Learn the required skills to become a data scientist and seek entry-level jobs in the field. Once you have worked in the area for a couple of years or more, you can study for a graduate degree in data science from one of the many schools that offer it.

There are no particular steps to becoming a data scientist if you go down this route. Your education will take care of all the skills you need to learn. You will have courses on programming, statistics, mathematics, and other relevant areas.

All you would need to do is score well on GRE, prepare a stellar application, and have some experience in the field to be eligible for a master’s program. It is paramount that you demonstrate your skills and abilities, which is why I recommend that you start working on projects as soon as you complete your undergrad.

If you don’t have an undergrad in data science and want to do a master’s in it, you would need to demonstrate to the admissions board why you are a good fit. This will vary from school to school, but you will be required to possess a strong understanding of the areas above. It would also help tremendously if you have relevant work experience in the field. This can even be independent projects or freelance work. As long as you can showcase your skills, you shouldn’t have a problem getting in.

Method # 2 Self-learning

Thanks to online courses, there is practically no limit to what you can learn. As with any other field, you can learn to become a data scientist by understanding the relevant skills online.

You can do this two ways. Firstly, you can enroll in a full-fledge data science online course that will award you a certification or a degree upon completion. You will learn all the relevant skills in a consolidated manner without having to use multiple resources.

This route is more expensive, but if you are willing to make that investment, the following are some of the best online courses to learn data science.

• Data Science Specialization – JHU @ CourseraIntroduction to Data Science – Metis
• Applied Data Science with Python Specialization – Umich @ Coursera
• Data Science MicroMasters – UC San Diego @ edX
• Dataquest
• Statistics and Data Science – MIT @ edX
• CS09 Data Science – Harvard 

Instead, if you want to learn the different skills required for data science from various resources, you can follow the plan below. Most of these resources are free but do keep in mind that it will take longer, and you will not receive any certification upon completion.

Math

• Khan Academy Math Track
• MIT Open Courseware: linear algebra and calculus
• Udacity: Intro and Inferential Statistics

Data Science Toolkit

• Edx: DAT201x — Querying with Transact SQL
• Coursera: Automate Boring Stuff with Python
• Udemy: Complete Python BootCamp

Machine Learning

• Coursera: Machine Learning by Andrew Ng
• Coursera: Applied Machine Learning (U Michigan)
• Book: Python Machine Learning (2nd Edition) by Sebastian Raschka

Big Data

• Udacity: Intro to Hadoop and MapReduce
• IBM: Hadoop Fundamentals Learning Badge
• Edx: UC Berkeley Spark Courses