Is Data Analytics Hard? Tips for Rising to the Challenge

Written by Coursera • Updated on

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.

A woman sitting on the floor at her coffee table studying data analysis on her laptop

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.    

Read more: What Does a Data Analyst Do? A Career Guide

Ten 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 [1]. 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 [2]. 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. 

Google Data Analytics Professional Certificate

Begin building the job-ready skills and tools employers look for with this certificate program from Google that you can complete in less than six months. 

Learn how to clean and organize data with SQL and R, visualize with Tableau, and complete a case study for your portfolio—no prior experience or degree required. Upon completion, you can start applying for entry-level jobs directly with Google and more than 130 other US employers.


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?

Be realistic with the time you’re able to commit, then guard that time fiercely. This is your time to learn.

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.

Carrie, a research manager at Google, discusses how she overcame her early struggles with learning R in this video.

Carrie, a research manager at Google, offers advice for beginning data analysts just getting started with R programming.

5. Develop your data analyst skill set 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:

6. Practice your skills on real data projects.

You don’t have to wait until you have a job as a data analyst to start gaining experience. As you’re learning the theories behind the practice, apply them to the real world by practicing on real data. Look for courses that incorporate hands-on projects and assignments, or take a do-it-yourself approach by designing your own projects using free, open-source data sets. 

Pick a topic you’re interested in and start digging into the data to see what you can find. Here are some ideas to get you started:

  • Use Google Books Ngram to determine what words were used most frequently in books between 1950 and 1990. 

  • Use Python to create an SQLite database for saving your contacts (name, email, phone number, address, etc.).

7. Get involved in the data community.

It’s never too early to start building your network. Whether you’re working through a degree course, coding book, or your own data project, consider getting involved with a community of other learners and data professionals. When you hit a sticking point in a program you’re writing or can’t quite seem to figure out a statistical problem, you can turn to your community for ideas.

  • GitHub lets you post your code for feedback or collaborate on coding projects. Sometimes the projects you post can even attract the attention of hiring managers.

  • On Kaggle, one of the world’s largest data science communities, you can join competitions to solve real-world data problems and collaborate with other data professionals.

  • Reddit has several subreddits focusing on data topics. Some to consider include r/dataisbeautiful, r/datasets, r/learnpython, r/learnSQL, and r/DataScienceJobs.

8. Focus on your workplace skills too.

Successful data analysts leverage their technical skills on the job, but they also rely on human skills, like solid communication. As an analyst, you might be tasked with presenting your findings to decision makers who may not possess the same technical knowledge. The ability to translate complex ideas into easy-to-understand presentations can be a huge advantage.

Other workplace skills, like curiosity, problem solving, teamwork, and attention to detail, also appeal to employers. The good news is that you probably already have some of these skills.

9. Commit to lifelong learning.

Let’s talk about what this really means. It doesn’t mean you need to commit to a full time degree program or wait years to get a job as a data analyst. It’s possible to develop the skills you need to get an entry-level role as a data analyst in a matter of months. But getting a job doesn’t mean your learning should stop. In this field, you’ll have an opportunity to continue improving your skills over time. 

And you’ll keep getting better at it. Research has shown that learning is a skill. The more we practice learning, the faster and more efficient we become at developing expertise. 

10. Understand the how, what, where, when, and why of data skills.

It’s less critical to know everything there is to know about Tableau, Python Pandas, or a particular machine learning model and more critical to know how a particular tool works, what it does, and when and why you should use it.

The most popular data visualization software or programming language today might be obsolete five years from now. In an industry that’s changing all the time, learning should be less about memorizing specific bits of programming syntax or pieces of information and more about improving broader skill sets. 

Get started with Coursera

We’ve outlined some tips and considerations to keep in mind as you learn the skills of a data analyst. If you’re ready to take the next step, start exploring this in-demand career path with a seven-day free trial to the Google Data Analytics or IBM Data Analyst Professional Certificates.


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Google Data Analytics

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Spreadsheet, Data Cleansing, Data Analysis, Data Visualization (DataViz), SQL, Questioning, Decision-Making, Problem Solving, Metadata, Data Collection, Data Ethics, Sample Size Determination, Data Integrity, Data Calculations, Data Aggregation, Tableau Software, Presentation, R Programming, R Markdown, Rstudio, Job portfolio, case study

Frequently Asked Questions (FAQ)

Article sources

1. World Economic Forum. "Data Science in the New Economy," Accessed June 3, 2022.

2. Robert Half. "Technology 2022 Salary Guide," Accessed on June 3, 2022.

Written by Coursera • Updated on

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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