Is Data Analytics Hard? Tips for Rising to the Challenge

With this advice on how to meet the challenge, you may learn new abilities, push through the inevitable stumbling blocks, and boost your confidence as a data analyst.

If you’re thinking about learning data analytics, it’s not unusual to have some concerns about the technical skills involved. Data analysts rely on skills like programming in R or Python, querying databases with SQL, and performing statistical analysis. While these skills can be challenging, it’s totally possible to learn them (and land a data analyst job) with the right mentality and plan of action.

Five tips for learning in-demand data skills

Build new skills, push through the inevitable rough patches, and increase your confidence as a data analyst with these tips on how to meet the challenge.

1. Remember that data skills are an investment in your future.

Demand for skilled data analysts is growing — the World Economic Forum Future of Jobs 2020 report listed this career as number one in terms of increasing demand. And hiring data analysts is a top priority across a range of industries, including technology, financial services, health care, information technology, and energy.

According to the Robert Half Salary Guide, data analysts in the US make an average of $106,500, depending on skills and experience. That means the energy you invest now could pay off later with an in-demand, well-paying career.

Learning new skills takes time and energy. Think of these expenditures as an investment in your future self. Each time you write a new line of code, have an “aha” moment for a tricky math concept or finish a data project for your portfolio, you’re laying the foundation for a successful career in data. 

2. Build foundational skills with an online course.

If you’re new to data analysis, it can help to start with a structured program that covers the basics and introduces you to some of the tools of data analytics: 
  • Data types and structures
  • Processing and preparing data
  • Methods of data analysis
  • Data visualization and storytelling
  • Using data to answer questions
By getting a broad overview, you can assess what skills you already have and identify areas for improvement. 

3. Set aside a little time for your data skills each day.

You don’t have to drop everything and study full time to start making progress toward a career in data. You might be surprised by how much you can accomplish with as little as 15 minutes a day. 

Set yourself up for success by planning out how your learning will fit into your life. As you’re making a plan, ask yourself these questions:
  • When do I feel most focused? When do I have the fewest distractions?
  • To what part of my day can I anchor my learning time? Right after my first cup of coffee? During my lunch break? Just after dinner?
  • Where can I work with few to no distractions?
  • Have I blocked out this time on my calendar?
  • Can I set an alarm to remind myself of my commitment?

Who do I need to inform of my plan to avoid interruptions? Roommates? Family members? Colleagues?

4. See mistakes as learning opportunities.

There will be times, especially early on, when a small error in your code causes your program to crash. Or maybe you spend time building a database only to realize you could have modeled it more efficiently. That’s okay! Give yourself permission to make mistakes. This is how we learn.

Accuracy is certainly important once you’re on the job, but while you’re learning, embrace the fact that you will mess up. You will feel frustrated at times, but you’ll also learn from those struggles and become a better analyst by working through them.

5. Develop your data analyst skillset bit by bit.

After you’ve built a foundation in data analysis with some form of structured overview, pick one skill and dig deeper. Choose to build confidence with a skill you already have some proficiency in or tackle your biggest weakness head-on. 

Here are some ideas for places to start:
  • Learn the basics of python or R programming.
  • Start interacting with data using SQL (Structured Query Language).
  • Brush up on your spreadsheet skills with an Excel class.
  • Get a refresher in statistics or linear algebra.
Data analytics is a highly rewarding career choice and there are many different pathways into the field. We hope this article has helped shed some light on the many learning options available to you. If you’d like to discuss your future in data with a real person, schedule a call with Syntax Technologies.

Our data analytics courses provide students with the remarkable opportunity to evolve as experts in the field and consequently, enter one of the most sought-after domains of the tech industry.

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