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You’ve stumbled across this career in data analytics and have been wanting to enter this field. But you’re not sure how to learn data analytics in 2024. If that’s the case for you, then this blog post would be a perfect guide for you.

Read on for my full roadmap on how to learn data analytics in 2024!

1. Research Necessary Skills

Before you go ahead and start learning data analytics, it’s important to research the necessary skills required for this field.

To help with your research, try looking at these places for resources:

  • YouTubers who share their journey
  • LinkedIn influencers and experts
  • Job descriptions for data analyst positions at companies you are interested in working for.
  • Online job portals such as LinkedIn, Indeed, or Glassdoor.

Some of the key skills that are currently in demand include:

  • SQL
  • Python
  • R
  • Data Visualization with Tableau or Power BI
  • Machine Learning
  • Data Mining and Data Cleaning

Make sure to do thorough research on each of these skills and identify which ones you need to focus on based on your career goals.

2. Data Analytics Courses & Certificates

Once you’ve decided on what tools to learn, it’s time to find the best courses and certificates that can help you acquire those skills.

There are many online platforms that offer data analytics courses, and one such example is Coursera.

Some of these platforms also offer certifications upon completion of their courses. Make sure to research the reputation and credibility of each platform before investing.

My personal favorite is Coursera, which provides a large variety of courses and certificates from top universities and industry experts.

You can choose to work on each tool individually in a topical manner.

For example, here are some good courses based on topic:

I like their professional certificates lineup, which is helpful in piecing together all the important tools and skills needed for a data analyst in one package.

Here are some useful certifications I can recommend:

Undecided on which works for you? The Google Data Analytics Certificate is the best one to go for.

If you’re an avid learner, then a Coursera Plus Subscription might be good too. Coursera Plus gives you access to over 7000 courses and all the above-mentioned Professional Certificates. So this will be a great option if you plan to take on several of them!

To make things better, they’re even having a $200 off New Year’s offer for a limited time, so now’s a good time to get started!

3. Guided Projects for Exposure

To help solidify and apply your newly acquired skills, guided projects are a great way to get hands-on experience with real-world datasets.

Some of the platforms that offer project-based learning include:

  • Kaggle
  • Coursera Guided Projects

These platforms also have a community aspect where you can connect with other data analysts and experts for guidance and support.

Some guided projects on Coursera I like include:

Through guided projects, you’re able to expose yourself to multiple tools and gain a more comprehensive understanding of data analytics.

This is an important step because this practical experience in working with a wide variety of tools makes you more aware of the skills you’ll need to land an entry-level role.

4. Personal Projects to Show Expertise

But of course, these exposure projects aren’t really enough to get you hired in a tough 2024 data analytics job market.

You’ll need to showcase your expertise with some personal projects that demonstrate your ability to use data analytics tools and derive insights.

Some ideas for personal projects include:

  • Create a visualization dashboard using Tableau or Power BI on a unique dataset like your activity tracking data.
  • Build a machine learning model to predict something, like housing prices or customer churn rate.
  • Scrape the web for data and clean it using Python.
  • Create a data pipeline using SQL to automate data processing tasks.

These personal projects will not only help you improve your skills but also provide tangible evidence of your abilities to potential employers. Make sure to keep track of these projects and showcase them in your portfolio or resume when applying for jobs.

5. Data Challenges & Competitions for Practical Experience

Once you’ve got some good personal projects in your belt, it’s time to test your skills in real-world scenarios.

Participating in data challenges and competitions is a great way to do this as it allows you to apply the techniques and tools you’ve learned on actual datasets with specific goals and metrics.

Some popular data challenges and competitions include:

  • Kaggle Competitions
  • Hackathons
  • Datathons

I found this especially useful in creating a simulated working environment to solve a business problem with data. It not only improves your skills but also helps in developing a problem-solving mindset and being able to present your findings effectively.

Moreover, these hackathons also help contribute to your resume, showing that you have some exhibited experience.

6. Network With Professionals

Next, don’t underestimate the power of networking. Connecting with professionals in the field can open up opportunities for mentorship, job referrals, and even potential job offers.

Some ways to network with data analysts include:

  • Joining LinkedIn groups or communities focused on data analytics
  • Attending industry events like conferences and meetups
  • Reaching out to professionals on LinkedIn for informational interviews

By networking with professionals, you can also gain valuable insights into the industry and learn about new tools or techniques that could give you an edge in your job search.

7. Apply for Internships and Mentoring Programs

With all the projects accumulated from the past few steps, you’re probably now all ready to kick-start your career.

You can begin with something simple, like an internship or mentoring program, to gain practical experience and make connections in the industry.

Some benefits of going for an internship include:

  • Gaining on-the-job experience
  • Building professional relationships
  • Learning new tools and techniques from experienced professionals

Furthermore, internships or mentoring programs can often lead to full-time job offers, so make sure to treat them as a stepping stone toward your career goals.

8. Do Coding Practices

While you apply for your full-time position, you can consider doing  some coding practice to improve your skills.

Practicing data analytics problems on platforms like LeetCode or HackerRank will help sharpen your analytical skills and prepare you for technical interviews.

Some other benefits of doing coding practices include:

  • Improving problem-solving abilities
  • Learning new techniques and approaches from others’ solutions
  • Building a portfolio of code that demonstrates your proficiency in data analysis tools

9. Use AI to Improve Technical Skills

Lastly, here’s a bonus step you can use even if you’re well into your data analytics career, and that is to use AI to improve your technical skills.

AI bots like ChatGPT and Bard can provide explanations of code to help you debug or understand a difficult problem that you’re stuck at.

These tools are also useful in speeding up your researching speed when learning about a new package. For example, boring and dry documentation can be scanned to see if it can solve your current error or data analysis problem.

Related Questions

Are professional certificates helpful in learning data analytics?

Yes, professional certificates can be incredibly helpful in learning data analytics. These courses often provide a structured curriculum that covers all the necessary topics and tools you’ll need to become a data analyst.

Moreover, completing a certificate course also shows potential employers your commitment to the field and can be a valuable addition to your resume.

How long does it take to learn data analytics?

The time it takes to learn data analytics can vary depending on your background and learning pace. Typically, it can take anywhere from 6 months to 2 years to become proficient in data analytics.

However, with consistent practice and dedication, you can start applying for entry-level roles within a year of starting your journey in data analytics.

Final Thoughts

Learning data analytics may seem like a daunting task, especially if you’re from a non-technical background. But with the right approach and mindset, it’s definitely achievable.

Hopefully, these steps have given you a clear roadmap to follow and motivated you to start your journey in data analytics. Remember, practice is key, and don’t be afraid to ask for help or seek out resources along the way. All the best in learning data analytics!