A data analyst or scientist does the job of looking at the big picture while the others look at the small picture of running day to day operations. In the modern world, there are zettabytes of data floating around and they are multiplying at mind-boggling rates. As of 2017, there was approximately 2.7 zettabytes of data accumulated around the world and the projected rise is estimated to be nearly 163 zettabytes by 2025.

A course for data analytics teaches you to collect data, segregate the data based on their value to your goal and then make certain conclusions from it in an easy-to-grasp form that an ordinary layman might not otherwise understand. A data analyst is someone who looks at the big picture in the sense that they try to improve the existing structure of things or look for areas that can be explored for greater profits in a certain field or highlight trends or patterns that emerge from the careful utilization of the accumulated data. The techniques or processes that are involved in data analysis for the most part are automated which crunch raw data, making it useful for humans by helping them make their systems or businesses more efficient and structured.

As mentioned above, a data analytics certification will teach you how to do all of these things and more. The field of data analysis is huge and growing at a rapid pace with each passing day. There is heavy demand for professionals in this field as would be evident from the very famous case of Cambridge Analytica where the results of data analysis were being sought after by governments and private players alike and all of them ready to shell out some large amounts of money for the information. And aside from good training, every achievement depends on your skills and efficiency as a data analyst/scientist, and if you’re good at it the sky is the limit package-wise.

certification on data analytics can be broken down into 4 parts:

1. Descriptive Analytics: As the name suggests, this type of analysis generally tells you what happened over a particular period of time, whether the sales went up or if the consumption reduced in a specific area around a specific time.

2. Diagnostic Analytics: This process focuses on understanding why something happens and whether there exists a cause and effect relationship between them. For example, some may try to understand the effect that a new marketing strategy might have upon the sales of the company etc. and to this purpose, it might involve fair bits of deductions.

3. Predictive Analytics: These try and make predictions as to what can happen in the near term by analyzing the data from the past where similar situations were prevailing. For example, someone may try to predict what happens to sales in a hot summer by studying the effects that a hot summer has had on sales in the past years.

4. Prescriptive Analytics: These on the other hand, focus on putting together a course of action by gathering and examining the available data. Like if there’s a high probability of there being a hot summer, then how many tanks should be added in a brewery or how should work hours be adjusted to not lose out on productivity.

A data analytics course Malaysia would help you understand all these parts thoroughly.

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