An organisation’s challenges and business questions can be solved with data, and this is what it means to do a data analyst’s work. The human element in converting numbers into clear results and recommendations is provided by data analysts. Any business concerned with using data to make better business decisions, from staff productivity to product pricing, requires them to gather, process, and analyse data. It should come as no surprise that 59% of organisations intend to grow the number of employment requiring data analysis abilities over the next five years.
The job of a data analyst may be ideal for people with a strong sense of curiosity as well as mathematical and analytical aptitude. It necessitates having logical thinking along with the capacity to speak clearly and simply with team members who are not familiar with statistics.
Additionally, you can use your certificate to apply directly for the position. From the outside, everything appears to be in order, but there are a few things you should think about before enrolling in this data analyst course Malaysia. Five of them are listed below.
1.Courses are too much introductory .
Yes, you heard correctly. The majority of the courses are very basic, leaving you to wonder how much you actually learned. Data analytics and a number of success stories of data analysts are the only topics covered in the first and second courses. It is undoubtedly for beginners, but it takes too much time to understand very basic concepts like what data, the cloud, and other terms mean. Quizzes tend to be more concerned with definitions and glossaries than they are with imparting useful knowledge.
2. An uneven distribution of theoretical and practical knowledge
There is far too much emphasis on theoretical knowledge and not enough on real-world applications.
As a result, you will end up acquiring ideas and definitions rather than practical abilities. Hands-on exercises are particularly lacking in the sixth course, “Share Data Through The Art of Visualisation.” The focus of the entire course is on telling a compelling tale, knowing the audience, and distinguishing between effective and ineffective visualisations. But very little is taught about how to really create good graphs. You merely learn a few Tableau drag-and-drop features (visualisation platform). More sophisticated Tableau features and tutorials on how to use them, in my opinion.
3. Too many complex subjects are covered all at once.
In general, the certification programme is quite beginner-friendly. Some of the courses, nevertheless, move along quite quickly. Sometimes it seems like a roller coaster. You start out by learning the very fundamentals of data, spreadsheets, and SQL. You then find yourself unexpectedly perplexed about certain complex SQL and spreadsheet concepts. This is particularly true for the sixth course. The first four courses are too basic, but the fifth one is too extensive. They will cover too many complex things in too little time without providing adequate practise or quizzes. It is challenging for novices to follow.
4. The absence of unity
The majority of the courses are well-structured and are based on the ask, prepare, process, analyse, share, and act philosophy. This logic, however, starts to fall apart in the seventh course, which is about R programming. You initially begin to study things in a systematic manner. You will acquire abilities that are crucial to the entire process. Then, in the last courses, they’ll start teaching you R all of a sudden. Additionally, you are not required to use any prior knowledge you have acquired up to that point. You study the fundamentals of R programming. It seems redundant or needless to me. Learning R will undoubtedly be beneficial to you. But the timing is all off and the route is out of place. If they had started it earlier, they could have done a better job. For instance, they did not reserve the entirety of the course for SQL or spreadsheets; rather, they were present with us from the start to the finish. As you move from one course to another, these tools are introduced and improved. I wish they had treated R the same way.
5. Insufficient statistics
My belief is that a data analyst must have a working grasp of statistics, albeit the precise level of that expertise may vary based on the requirements of your particular position. There aren’t many statistics lessons in the Data Analytics Specialisation. Only a few times were the sample size and margin of error given. But at the very least, they ought to have addressed the fundamentals of statistics. They ought to have included lessons on topics like probability, mean, median, standard deviation, hypothesis testing, and perhaps regression. Because each of these items is essential to the toolkit of a data analyst.
Conclusion
Overall, this specialty is a good course for someone who wants to change careers or is just beginning their data analytics adventure. I solely discussed the negative aspects of this programme here. If you were unaware of this course before. After reading this blog, you could think it’s extremely terrible. It’s not really that horrible, though. In fact, it’s fantastic. You should think about the topics I just listed before enrolling in the course. Even though the course has certain negatives, you will overcome them with the advantages you gain from it.
This article is posted on Top Edge News.