Starting a Career in Data Science

Starting a career in data science can be a daunting, yet exciting and fulfilling journey. Transitioning from a different career into data science is particularly tricky and being a person who transitioned successfully, there are certain considerations that could have made the transition much easier. In hindsight, if I had to do it all over again, Here are some tips that would be very helpful.

Acquire the required skills: To become a data scientist, you need to have a strong foundation in mathematics, statistics, and programming. The Maths and Stats part is not an absolute necessity and you can definitely be a data scientist without being a math genius. Having functional knowledge of maths and Statistics puts you in a position of huge advantage though as you will constantly need to work with concepts like regression, probability, set theory, calculus, etc, and knowing how these work will be very helpful to a pro data scientist, to say the least.

Learn a programing language: If you are serious about a career in data science you can't escape #python or #r and #sql . They make your life so much easier, so consider learning these as early as you can. Excel is great but if you are working with big data, you will find that MS Excel has its limitations.

Gain experience: The best way to gain experience is to work on projects. You can find publicly available data sets at #kaggle, #zindi, etc, and get the opportunity to apply what you have learned to real-world problems. You can also contribute to open-source projects, which will help you to build your portfolio. #omdena can be really helpful in this regard.

Take advantage of free resources: There are tons of free resources out there that can help you do way more than you can if you go without them. google collab provides free GPU compute resources that you may not have access to as a budding data scientist otherwise, GitHub provides a free website resource for personal branding, and google meet is a resource for free remote meetings. Linkedin offers a lot of free training resources, and there are many free courses out there that provide so much value for free. Use them!

Build your portfolio: Start building a portfolio from day 1. Open an account with GitHub and stash all of your projects in your repository. Even if you feel unsatisfied or unsure of the project's quality, set the project to private so only you can access it, but put it in your repo while you work on it. You can set the access back to public when you are satisfied with the quality of your work. This will prove to be a huge asset as your career progresses.

Sign up for Hackathons and other competitions: The primary goal is not to win (though it wouldn't hurt if you do), but to help to hone your skills and help find use cases for all that you are learning. Whenever you learn a new skill, use it. You quickly lose skills you do not use.

Network: Attend meetups and conferences in your area and connect with other data scientists and professionals. This will help you stay updated with the latest trends and technologies, as well as potentially lead to job opportunities. Again your local Omdena chapter can be helpful in this regard.

Consider getting a certification or a degree: While certifications and degrees are not a requirement to become a data scientist, they can help you stand out and demonstrate your commitment to the field. #exploreai #udacity , #coursera , #Data365 etc offers some wonderful career-advancing training content. Some of these are offered for free while you may need to pay for others but trust me, this will be money well invested.

Look for job opportunities: Once you have gained some experience and have a portfolio to show, you can start looking for job opportunities. You can also consider applying for internships or entry-level positions, which can give you a chance to work on real-world projects and gain valuable experience.

Form and Maintain an Accountability group: Being a part of a group of other aspiring data scientists would prove to be an asset. You can have a team of 3-8 budding professionals who meet regularly to set goals, appraise each other, develop strategies to study and grow, etc. This would ensure that you have a support system where you can share ideas, and aspirations as well as grow together.

Continuously learn: The field of data science is constantly evolving, so it's important to continuously learn and stay updated with the latest techniques and technologies.

In summary, building a career in data science requires a combination of acquiring the necessary skills, gaining experience, networking, and continuously learning. Good luck!