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Why do I try to get a Data Analyst certification

Updated: Nov 8, 2023

I am in need of some visible progress, so I decide to get a certification.

But why Data Analyst?

Excel has been my forte since my early career. That turns me into a data freak that my teammate can rely on, either to analyze data or create a dashboard to show insight to the stakeholder. Even in my current role, where I need to become more of a PowerPoint person, I try to integrate Excel into any activities.

But that is also when I found what I needed to learn more.

For a social activity or non-IT work, it was hard to create a habit of data-driven decision-making. The lack of data puts the burden of making decisions fall into the hands of people. But what if we can get the data?

That’s what I expect from the certification that I joined.

What is the nature of data that are collected for social activity? How we can typically get the data?

These questions guide me through the search for good training, and that’s what makes me decide on the Google Data Analytics certificate. A wide range of cases and also best practices from the biggest data owner are the thing that I expect from it, compare to the other local courses.

Besides that, with the rise of this role, there is a lot more common benefit as well that I expect to get later in my career

The demand for data analysts is set to grow substantially in the near future. From 2019 to 2029, the Bureau of Labor Statistics projects a 31% increase in employment for data analysts, much faster than the average. A major contributing factor to the growth of data analytics is the ever-increasing use in a variety of industries, such as healthcare, finance, and technology.

Data analysts are also paid well for their skills and expertise. The average salary for data analysts in Indonesia is 5.2 million rupiahs per month, with some analysts earning over 10 million rupiahs per year. The salary can vary based on factors such as location, industry, and experience. As the demand for data analysts continues to increase, the salary for this role is expected to rise.

Data analysts have the opportunity to work in various industries, including healthcare, finance, technology, and marketing. This means that data analysts can choose to work in an industry that aligns with their interests and passions. Additionally, data analysts can work in different roles within an organization, such as business analyst, marketing analyst, or data scientist.

The marketing industry is another example where data analysts are needed. With the increasing use of digital marketing, data analysts are needed to analyze and interpret data from various sources such as social media, email marketing campaigns, and website traffic. This data can help organizations make informed decisions about marketing strategies, such as which channels to use, what type of content to create, and how to optimize campaigns.

Why it is not for you?

While it is true that the demand for data analysts is growing, it is important to consider that the field is also becoming increasingly competitive. As more individuals enter the field, it may become more difficult for new graduates or those with less experience to find job opportunities. This is especially true in industries where data analytics are already well-established, such as finance and technology. In these industries, data analysts may be competing against more experienced professionals who have a proven track record of success.

In addition to the competition, the fast-paced nature of the industry means that data analysts must be willing to constantly learn and adapt to new technologies and techniques in order to stay competitive in the job market. This can be challenging for some individuals, particularly those who prefer a more stable and predictable work environment. In order to stay ahead of the curve, data analysts must be comfortable with change and able to quickly learn and apply new skills.

Furthermore, while the average salary for data analysts is quite high, it is essential to consider that this can vary significantly based on factors such as location, industry, and experience. In some areas or industries, data analysts may earn less than they would in others. This means that individuals who are interested in pursuing a career in data analytics must carefully consider their options and be prepared to make trade-offs between salary, location, and industry.

Overall, while the field of data analytics is undoubtedly growing and offers many opportunities for skilled professionals, it is important to consider the potential challenges and trade-offs involved. As with any career path, individuals should carefully evaluate their skills, interests, and priorities before deciding whether data analytics is the right choice for them.

What is my verdict on the certification

First Half verdict

The collaboration of google and a local education company makes this into a kind of acceleration program, where they give me access to limited-time courses, and push me to finish the course beyond the system-generated schedule.

Not much problem in the earlier courses, since it is very basic and I have the background knowledge needed.

With the paced speed, it was nice, and feels like we are making proper progress. The basic approach also brings perspective on the work itself and clarifies how it can be implemented outside IT areas. Also, there is a new tool of the works that I find very beneficial for me to learn, as an upgrade from Excel.

What is definitely new for me is how this training approach and handling ethical things, like under-represented ethnics or minority sects. Analysts are trying to gather facts and truth through data. But people are not just numbers, and what makes people, people, may not be represented completely through data. It requires human insight and knowledge on how to implement this insight through the analytic process to create a result that is not only true but also fair.

However, I have some concerns about the effectiveness of this program in preparing individuals for the complexities of the data analysis field.

  • Lack of real work activity

One of the primary reasons why the Google Data Analytics program is ineffective is the lack of real-world application. The program focuses on teaching learners how to use Google's suite of data analysis tools, such as Google Sheets, Google Analytics, and Google Data Studio. While these tools are undoubtedly useful, they are not enough to prepare individuals for the real-world challenges of data analysis.

They turn into a spreadsheet or SQL specialist, disconnected from the business side.

  • Lack of Practical Progress

Another reason why it is quite ineffective so far is that there is a lack of practice, where the student can make meaningful progress. That includes the standard there is used in the course to finish the work that basically can be finished with just logical thinking.

Without a strong foundation in statistics and data modeling, learners may struggle to navigate the complexities of data analysis. They may not be able to interpret statistical results accurately, or they may not be able to build effective models to analyze large datasets. This can lead to inaccurate or incomplete analyses, which can have serious consequences for organizations that rely on data analysis to make informed decisions.

  • Limited Career Opportunities

Finally, there is definitely this perception that training will definitely lead to good, proper work.

No, it is not.

Employers are looking for data analysts who have a deep understanding of statistical analysis, data modeling, and data visualization. They want individuals who can navigate the complexities of data analysis and draw meaningful insights from large datasets. The Google Data Analytics program may not provide learners with the necessary skills to meet these requirements. Again, these are just the basic.

Temporary Conclusion

In conclusion, the Google Data Analytics program may not be effective in preparing individuals to become good data analysts. The program lacks real-world application, does not emphasize statistics and data modeling, and may not provide learners with the necessary skills to pursue a career in data analysis. While the program may be useful as a starting point for learners who are new to data analysis, it may not be enough to prepare them for the complexities of the field.

However, if you already have some images of where you will implement these skills and knowledge, like me, the mechanism and learning flow makes it very easy to create a conjecture to your own areas of expertise/problem, especially if you have enough excel or IT experience. And finally, it gives you a humanitarian perspective on data and analytics results that will sharpen your insight.

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