Launch your data analytics career and close your skill gaps
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How to Launch Your Data Science Career – Closing Your Skill Gaps

How to Launch Your Data Analytics Career – Part 1A “how to” series by data science leader Piyanka Jain, President and CEO of Aryng

In Parts 1 and 2 of this series, we’ve tackled how to tell if data science is right for you and match your skills to those required to do that job you’re eyeing. Most likely, you found some disconnects between your skills and those sought by your prospective employer. Never fear! Read on to learn how to fill those skill gaps and choose the best resources to help.

Step 3—Closing Your Skill Gaps

Data science requires certain skills, yes, but also a special mindset. You’ve already demonstrated both a passion and an aptitude for analytics. Capitalize on these qualities by seeking out training that not only teaches the skills you lack but helps you develop your analytics mindset.

Experience is the best teacher

As you look at programs to help you close skill gaps, ask yourself, “Has this instructor walked the walk?” I encourage you to look beyond programs based on theoretical academics to those taught by experts who’ve worked with real data and real organizations—and driven real impact.

Check whether the training program offers hands-on experience with a real-time data science project. Will you work with stakeholders, lay out your analytics plan, get buy-in? Will you get the chance to execute your analysis and influence the stakeholders with your insights?

Or, will you simply be analyzing publicly available datasets? Remember, the skill you need to analyze a dataset is just part of the picture. To do data science correctly, strategically and successfully, you need to start with questions, not data. Data doesn’t speak; it responds—to intelligent questions asked by stakeholders.

With that advice, let’s revisit your Skills Gap matrix from the last blog.

Mastering the skills you need

You’ve got your eye on jobs that require skills in business analytics, A/B testing, and advanced statistics. Unfortunately, you don’t have formal training or experience to demonstrate proficiency in any of those areas. Your grid might look like this:

Data Analytics Career skills gap

Don’t worry. You can fill these gaps with training in Business Analytics, A/B Testing, and Predictive Analytics. But be aware that training programs pop up every day, so do your homework to find specialized, in-depth training programs.

So, how do you know if you’re selecting a solid training program? Consider these factors:

  1. Will your work be hands-on or theoretical? Look for hands-on training with a strong focus on analytics and/or testing as applied to business. Academic courses just don’t bridge the gap from statistics to business. And, make sure the program gives you hands-on experience with a real-time data science project.
  2. Will you have access to experts and mentors? One-on-one interaction with an data science expert is invaluable in helping you develop the analytics mindset. The expert helps clarify your questions, guides you through data science exercises and cases and acts as your mentor for a real-time project. In addition, your interactions can help enrich your understanding of analytics.
  3. Does the program offer career transition assistance? If you are looking for an external job opportunity (vs. an internal transfer), consider whether the program helps you make the transition to your new career through mentoring, resume preparation assistance, and interview coaching.
  4. Will you receive certification after completing coursework? Certification is becoming one more way managers can differentiate between candidates to make the best possible hire. Options such as ACAP Professional Certification in data science demonstrate you’ve successfully passed the qualifying analytics aptitude assessment, completed the course content, passed multi-category knowledge checks and individual checkpoints, and used your new skills and knowledge to tackle practice case studies, Capstone projects, and a real-time client project.
  5. Will you have access to a supportive data science community? Transitioning your career to data science is a multi-month project, you won’t transition overnight. You will have good, highly motivated days and you will have days filled with doubts and questions. So chose a program that offers you not only access to experts and mentors but also a community of fellow learners and alumni’s who can help you both in the coursework as well as in your career transition and keep you motivated to complete the journey.

Finally, conquer the tools

Your skills matrix probably also shows some holes when it comes to using certain tools. Again, you’re not alone. If your matrix looks like this, rest assured low-cost or free online training can help.

Data Analytics Career tools and programming languages

Check out these options to learn or brush-up on commonly used tools:

  1. Learn spreadsheet programs for FREE from online portals.
  2. If you have programming experience, you can learn tools online easily and inexpensively. Several online portals offer foundational training. You can also learn free of cost using online tutorials. If you don’t have any programming background, consider enrolling in a local hands-on course.
  3. Just like statistical tools, you can also learn programming online and inexpensively.
  4. If you are familiar with one BI tool, it is easy to pick up another on the job. If you don’t have experience with a GUI-based BI tool, I recommend downloading a free trial version, importing a public dataset, and playing with it. Don’t spend money learning a BI tool as part of your career transition since the plethora of tools means you never know which you’ll be using in your new job.

Finally, practice, practice, practice. Use your newly acquired skills as much as possible. Do you have any friends in the product, marketing, operations or sales functions who need help using data to drive decisions? Offer your help to provide free analysis. You’ll get great experience and a beefier resume.

There’s no reason to fear skill gaps when there are so many ways to close them. So, what’s next? Landing that dream job, of course! In my next blog, I’ll give you tips for creating an attention-getting resume and acing your interview.

Learn More About a Career in Data Science :


How to Launch Your Data Analytics Career – Part 1A 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:

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