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In our previous articles, we have  explored the importance and vast opportunities in data analysis. We have  highlighted how data is becoming the new currency of the world,  which we  know has sparked  questions about how one can become a data analyst. Many wonder if a computer science background is necessary or if being a math expert is sufficient. Mr. Rasheed Kareem, one of the able  instructors at Eko Institute of Technology with over 10 years experience, suggests that critical thinking is the key requirement for becoming a data analyst. Ability to think beyond the obvious.

In this article, we will cover the steps needed to become a data analyst. You can also check out our previous articles for more insights on the importance of data analytics. Let’s get started!

The need for skilled data analysts is growing rapidly. They’re essential for deciphering complex data and providing useful insights that drive decision-making in different industries. If you’re thinking about a career in data analysis, here are the basic steps to help you succeed.

  1. Acquire the right education: While self learning is an option, navigating through online resources can be overwhelming. Although many professionals suggest starting with the basics independently, it is just like searching for a needle in a haystack. Self learning can be challenging and exhausting without clear guidance, It is like entering a vehicle without having a clear destination in mind. Instead, consider enrolling in a bootcamp, data program, or pursuing a degree in fields like mathematics, statistics, computer science, or data science. Universities offer specialized programs tailored to aspiring data analysts, providing in-depth knowledge and practical skills. I highly recommend the EIT Data Analytics program, which boasts a robust curriculum, experienced instructors, and follows the 70:20 learning approach.
  2. Getting hands-on experience: While academic qualifications are essential, hands-on experience is equally valuable. One issue with the education system is that many people have theoretical knowledge but struggle to apply it. Not everything can be learned within the confines of a classroom or virtual setting. Look for internships, part-time roles, or volunteer positions that let you apply your knowledge in real-world situations. Building a portfolio of projects showcases your skills to potential employers. Another advantage of joining the EIT Data Analytics program is the opportunity for internships with our partner organizations.
  3. Building technical skills: Get acquainted with commonly used data analysis tools and software, such as SQL for database queries, Excel for manipulating data, and visualization tools like Tableau or Power BI. Continuous learning and keeping up with new technologies are essential in this fast-changing field. However, there’s no need to worry, with consistent practice will ensure your technical skills build up naturally. So Practice interpreting data trends, identifying patterns, and formulating hypotheses to drive decision-making processes.
  4. Effective communication: Strong communication is vital for explaining your findings to non-technical stakeholders. Work on improving your written and verbal skills to present complex information clearly and concisely. Using visual aids like charts, graphs, and reports can help others understand and make decisions based on your insights.
  5. Building Connections: Your network is crucial. Join data analysis communities, attend industry events, and connect with professionals to broaden your knowledge and opportunities. At EIT, we foster a vibrant community and utilize the Brain Trust Group, an accountability system, to help you reach your goals.
  6. Keeping Up-to-Date: Data analysis is always changing, with new methods, tools, and approaches emerging regularly. Stay curious, keep learning throughout your life, and be ready to embrace new technologies and trends to stay ahead in the field.
 By following these steps and continuously refining your skills, you can embark on a rewarding journey towards becoming a successful data analyst. 
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