KDnuggets Home » News » 2017 » Nov » Tutorials, Overviews » 8 Ways to Improve Your Data Science Skills in 2 Years ( 17:n45 )

8 Ways to Improve Your Data Science Skills in 2 Years


Two years. Two years is the maximum amount of time you should spend focused on your learning, education and training. That’s exactly why this guide is focused on honing the most beneficial skills in two years.



By Kayla Matthews, Productivity Bytes.

Improve your skills

Data science is one of those fields where constant growth is possible and always welcome. Just do a simple web search for the “top data science skills” and you’ll find hundreds if not thousands of blogs, articles and videos on the topic.

Ferris Jumah, a full stack data scientist at LinkedIn, published a post a couple years ago detailing the most popular skills data scientists have, based on their LinkedIn profile data. It’s outdated now, so you can bet there are skills and tools of the modern scientist that are not included in the list.

The first question that comes to mind when looking at lists like Jumah’s is how many of these skills you actually need to thrive in the industry. Is there really anyone out there who can do all of them?

Quite possibly, but probably not. What matters most for you is how many of these skills you can pick up and become adept with. And perhaps more importantly, how quickly can it be done?

It doesn’t have to feel like a lifetime sentence. Keep reading for tips to kick-start your data science, machine learning or statistician career.

 

Step One: Admitting You Have a Problem

 
This sounds like the first step of a recovery program — and in a way, it kind of is. No matter who you are, there’s always an ambition to be at the absolute top of your field.

Data science especially fosters this kind of attitude, because even if you aren’t constantly learning new things, you still need to remain informed about the skills and processes you work with regularly. It’s an industry that’s always moving and evolving, which means you need to do the same.

But that doesn’t mean you have to know or be involved with everything. Therein lies the rub. Step one is admitting to yourself that you won’t be able to specialize in every skill a data scientist can use. Yes, you need the core skills — analytics and data processing, mostly — but everything else is more of an aside.

So? Narrow down your focus. Choose the skills you use most and the ones which will be most beneficial to your career. If your plan is to work with Python or Hadoop-based systems, for instance, learning other languages can improve your knowledge base but won’t really help you improve your core skills.

 

Step Two: Fast Track Your Learning

 
Two years. Two years is the maximum amount of time you should spend focused on your learning, education and training. Yes, you can enter the workforce before then if you’re new to the industry, but the point is you never want to go more than two years without actual field experience.

That’s exactly why this guide is focused on honing the most beneficial skills in two years. The goal is to dedicate about fifteen to twenty hours per week to developing your knowledge and skills. There are several ways to do this:

  1. Take online courses through Coursera, Treehouse, Lynda and CodeSchool that focus on the skills you want to learn. The courses don’t necessarily have to be related to data science. A programming course in a relevant language, like Python, works too.
  2. Read as much as you can on data science. This includes academic papers, textbooks and other educational materials and even current industry reports.
  3. Become involved with a data science or development community. There are many to choose from. Pay attention to the posts and material the community shares and be a part of the most important discussions. You can learn a lot from your peers.
  4. Find a mentor, if you can, who either works in the industry or has experience in data science. This includes programmers and developers, data scientists, statisticians, engineers and more. Meet with them regularly, pose questions and hear their experiences.
  5. Pay regular visits to the UCI Machine Learning Repository and participate in data problems. R, Excel and similar platforms are your friend. Don’t be discouraged if it takes you longer than the quoted timeframe to finish a problem. Stick with it and you’ll pick up the pace over time.
  6. Stick to scripts, pre-processed data and automated tasks and lean heavily on frameworks or databases. This will minimize the amount of time you need to spend creating algorithms and code for yourself and increase the time spent actually conducting data science and machine learning.
  7. Get an entry-level job working with machine learning, data science, analytics or basic stats. Data migration is an excellent opportunity for entry-level or beginner data scientists. You can lay the groundwork for your knowledge with these projects, and then easily automate some of these tasks once you understand the foundations.
  8. Get involved with an open source data science or machine learning project that interests you. Turn it into a hobby or side project you can enjoy working with in your free time.

Also, dig in and begin working with some of the more popular data science tools when you can. By the time you enter the workforce, you’ll already have basic knowledge and experience with the platforms your employer uses.

 

Step Three: Never Stop Growing

 
It’s important to understand that you will never become an expert in two to three years no matter how hard you work and how much you learn. It’s just not a feasible goal. But that doesn’t mean you shouldn’t strive to continue growing and improving your skills. Quite the opposite!

Even after you spend two years in training — which will go by fast, believe it or not — you’ll want to continue growing your skills and experience in new areas. If you don’t have the desire to learn new things, that’s fine — just make sure you stay on top of your current skills and projects.

It sounds redundant here, but stay involved with those development and machine learning communities and keep your ear to the ground for new courses and online resources.

From 2006 to 2016, for instance, popular data scientist designations have grown from just “data or business analyst” to include data scientists, business analysts, big data specialists, machine learning specialists, data visualization experts and much more. That growth is now happening faster than ever before, as major brands and organizations adopt this technology and implement new machine learning and data science initiatives.

In short, that will put more pressure on the professionals in the industry to continue their growth and become more skilled and more experienced in a broader spectrum of topics and skillsets.

It all translates to you having to stay on top of your core knowledge and skill base.

No matter what, never stop growing and learning.

Otherwise, you may find yourself left in the dust compared to the rest of the industry.

 
Bio: Kayla Matthews discusses technology and big data on publications like The Week, The Data Center Journal and VentureBeat, and has been writing for more than five years. To read more posts from Kayla, subscribe to her blog Productivity Bytes.

Related: