Is Data Analytics Hard? Tips for Rising to the Challenge

Key takeaways

Data analysts typically need skills acquired through continuous learning opportunities to succeed in this in-demand field.

• Skilled data analysts are among the most in-demand jobs between 2025 and 2020 [1].

• Data analysts need to know the basics of programming languages and spreadsheets like Excel.

• You can build your data analyst skills by taking courses, practicing what you’ve learned, and committing to lifelong learning.

Learn more about data analytics and tips for building your skills. Then, consider enrolling in the Google Cloud Data Analytics Professional Certificate. In as little as two months, you’ll have the opportunity to develop skills in the five key stages of visualizing data in the cloud. Upon completion, add this shareable credential to your resume or LinkedIn profile.

Is data analytics hard? 10 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 2025 report listed this career as one of the top jobs in terms of increasing demand between 2025 and 2030 [1]. Hiring data analysts is a top priority across a range of industries, including technology, financial services, health care, information technology, and energy.

According to data from Glassdoor as of January 2026, data analysts in the US make a median total salary of $93,000, depending on skills and experience [2]. This figure includes base salary and additional pay, which may represent profit-sharing, commissions, bonuses, or other compensation. 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. 

You can complete hands-on projects for your portfolio while practicing statistical analysis, data management, and programming with Meta's beginner-friendly Data Analyst Professional Certificate. Designed to prepare you for an entry-level role, this self-paced program can be completed in just five months.

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 in 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.

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:

• 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 course.

• Get a refresher in statistics or linear algebra.

Is it very difficult to become a data analyst?

The level of difficulty of becoming a data analyst may depend on your personal experience and prior knowledge of statistics and mathematics. To become a data analyst, you typically need to complete a bachelor’s degree in a subject such as computer science or a related field of study and to gain foundational skills such as programming and logical reasoning. You may earn your bachelor’s degree through a four-year degree program or take as much time as you need. 

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:

• Analyze what factors influence the popularity of a video on YouTube.

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

• Visualize which countries were using which COVID-19 vaccines (and at what rates) with this daily updated data.

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

• Practice cleaning and normalizing this data set of more than 200,000 Jeopardy questions from Reddit.

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, a 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.

Read more: What Is GitHub? Use Cases and FAQ

8. Focus on your workplace skills.

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. 

No-fluff help for when you’re stuck

Join Career Chat on LinkedIn to stay current with the latest trends in your career field. Continue your learning journey with data analytics with our other free digital resources:

• Watch on YouTube: 7 Essential Data Analytics Skills for Beginners

• Find a course: Which Data Analysis Course Should You Take? Find Out in 1 Minute

• Learn from experts: 8 Questions with an Expert: Google Financial Data Analyst

Whether you want to develop a new skill, get comfortable with an in-demand technology, or advance your abilities, keep growing with a Coursera Plus subscription. You’ll get access to over 10,000 flexible courses. 

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