This story was originally published on Towards Data Science and republished with the permission of the author, Admond Lee.
If you’re a data science student looking to carve a career in data science, here are some things you ought to know.
Admond Lee, big data engineer at Micron Technology provides all the answers to the questions he has received over the years.
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1. Start by Understanding Current Trend In Data Science
The International Data Corporation (IDC) predicts that worldwide revenues for big data and business analytics will reach more than $210bn in 2020.
According to the LinkedIn WorkForce Report in August 2018 for the United States, there was a national surplus of people with data science skills in 2015. Three years later, the trend has changed tremendously in the opposite way as more companies are facing shortages of people with data science skills with big data being increasingly used to generate insights and make decisions.
Economically speaking, it is all about supply and demand.
The good news is: The “tables” are now turned.
The bad news is: With rising job opportunities in data science, still, a lot of aspiring data scientists are facing challenges in getting their foot in the door simply because of their lack of data science skills gap relative to the requirements in the current job market.
Seek Out The Required Skills
Let’s start out by highlighting that it’s impossible for a data scientist to learn all the skills (whether technical or soft). But, with experience, data scientists can learn and adapt to new skills depending on their company’s portfolio.
However, in general, these are some core skill sets that data scientist have to possess. Let’s start with the technical skills:
A. Mathematical, Statistical and Programming Skills
This one’s pretty obvious, so we won’t elaborate too much on it. However, if you’d like to learn a little more on math and statistics, check this website out by Randy Leo. Randy has been helping aspiring data scientists for years and the information his website provides is a total gold mine.
Alternatively, we recommend books like An Introduction to Statistical Learning — with Applications in R. We highly recommend this textbook for beginners as it focuses on the fundamental concepts of statistical modelling and machine learning. Those with a vested interest in Mathematics should consider picking up The Elements of Statistical Learning.
Furthermore, schools like UpCode Academy provide courses for data science in Python that give aspiring data scientists a head start in the industry.
With regards to programming, the usefulness of languages like Python and R are usually up for debate in the industry but, what’s truly important is seeing how you — as a data scientist – are able to help real life problems.
B. Business Acumen
Established data scientists would know that while it’s important to extract data from a data set, it’s even more important to be able to apply that knowledge into a business perspective. Having this ability means that, data scientists can help the companies they work for think of well-thought out plans and solutions for the future.
C. Communication Skills
Lastly, the most important soft skill to learn is communication. LinkedIn surveyed 2,000 business leaders and the soft skills that they’d most like to see their employees have in 2018 are: Leadership, Communication, Collaboration and Time Management.
2. Look For The Right Bootcamps And Online Courses
With the hype surrounding AI and data science, plenty of schools are jumping on the bandwagon to offer both online and offline courses in those areas of expertise.
However, instead of signing up for those classes immediately, learn a bit more about them and find out if they are suitable for you.
So the question is: How to choose the learning materials that are suitable to you?
Here’s a process we highly recommend you try:
A. Understand that no single course can cover all the materials you want
Some courses overlap in certain areas so there isn’t always a need to shell out additional cash for other classes.
B. Know What You Need To Learn In The Very First Place
Don’t make the mistake of signing up for a course just because it sounds fancy and exciting.
Instead, make it a point to check out the course description and find out if it helps with the technical skills you might need.
C. Research The Best Courses Offered By Different Platforms
Once you’ve shortlisted a few courses that suit your needs, check out their respective reviews (very important!) by others before you pull out your wallet and get enrolled.
3. Learn From Open Sources Too
Learning from open sources could be sufficient to get yourself started in data science.
4. Once You’ve Gotten Some Work Experience In, Make Sure To Include It Into Your Resume
While work experience is important, it has to be said that it’s commonly used as a stepping stone to help you get to the next stage of the job application – the interview.
Therefore, learning how to write work experience in resume is truthfully important to get the entrance ticket. Studies have shown that the average recruiter scans a resume for six seconds before deciding if the applicant is a good fit for the role. In other words, to pass the resume test, your resume only has six seconds to make the right impression with a prospective employer.