How to Launch Your Data Science Career – Finding Skill Gaps
A “how to” series by data science leader Piyanka Jain, President and CEO of Aryng
From Part 1 of this series, you know “loving” data science isn’t enough to land you a dream job; you might also realize you don’t even know what that dream job is, exactly. But you did your homework and found you’ve got the all-important aptitude for analytics. Plus, you’ve identified a short list of jobs that look like a good match for your strengths and interests. You feel great. And maybe just a little overwhelmed about how to land one of those jobs. You’re a good fit for a data science career, but are you a good fit for a specific job?
It’s time to take a close look at the skills you already have, and those you’ll need to succeed.
Step 2—Identify Your Data Science Career Skill Gaps
Every type of data science career is multi-faceted, as you know from reading job descriptions. Your challenge is to identify which boxes you already check off with your education, experience, and skills—and which you don’t.
First, decode the jargon
The most sought-after job skills can be grouped into seven buckets. Watch for these requirements in the job description, along with some common verbiage to describe each skill:
- Analytics Skills
- “Passion for data analysis supported by personal and professional experiences”
- “Strong familiarity with statistical concepts”
- Tool Skills
- “Comfort with Excel and other standard productivity tools”
- “Technical skills such as SQL, Python, R/SAS/Stata a plus”
- “Knowledge of Omniture and analytics required”
- “Bachelor’s degree in a quantitative or technical field”
- Problem-Solving Skills
- “Troubleshoot and prevent technical issues”
- “Creatively solve problems while operating in a dynamic environment”
- Communication Skills
- “Proficient written and verbal communication”
- Functional Background
- “Previous work experience in performance marketing, media buying, lead generation, or related spaces”
- Industry/Work Experience
- “Previous work experience in financial services, consulting or other quant/data-driven fields”
- “Up to 3 years prior work experience”
Next, create a skill gap matrix
Choose some job descriptions that interest you and create a matrix of job requirements against your background to identify gaps. Here’s an example to use as a guide for building your personal skill gap matrix. Make sure to not only fill your data science skill gaps with the tools required for the role but all seven categories listed above.
MORE: Read Aryng’s five-step framework for business leaders, learning and development leaders, marketing and product managers, and analysts to create profitable insights from data.
Identify your gaps
It’s possible that your matrix will show gaps for data science and tool skills requirements, since your experience in these areas may be limited. You might already meet education requirements since they are often broad. However, if you don’t have the precise education required, you may be able to document online coursework you’ve completed to meet the requirement.
REMEMBER: Be aware that, increasingly, hiring managers find certification to be a plus for job seekers, if not a requirement.
If you fall short in the industry and work experience category for a role on your skill gap matrix, consider taking this advice: finding a job in your current organization is usually easier than moving to a new company. Knowledge of the industry and organization are attractive to hiring managers, and a lateral move has the added benefit of retaining your seniority and compensation. Another option for addressing this category is to look for roles within an overlapping industry or function. For example, if you are currently an IT professional supporting operations at a university, consider a role in operations in another educational institute. Once you have 2-3 years of experience in analytics, it will likely be easy for you to move to other industries and functions.
Your next step
If your matrix reveals more skills gaps than you expected, don’t worry. Those gaps can be filled by a wide range of resources to help you prepare for your career transition. In my next blog, I’ll show you how and where to acquire new skills to fill in your skill gaps and move forward transitioning to a data science career.
Learn More About a Career in Data Science:
- [Blog Post] How to Launch Your Data Science Career – Part 1
- [Blog Post] Top Machine Learning Trends – The Path to Realization
- [Podcast] Why This Chief Data Officer Sees Failure as a Learning Cycle
A highly regarded industry thought-leader in data science, Piyanka Jain is a frequent keynote speaker on using data-driven decision-making for competitive advantage at both corporate leadership summit as well as business conferences.
To learn more about Piyanka: