The importance of data-analytic thinking

What is data science?

Data science is a set of fundamental principles that guide the extraction of knowledge from data. Data science proceeds by importing data, tidying up data, then, after that, transforming, visualizing, and modelling data.

Finally, it includes communicating the result. In the process, we repeat the cycle of transformation, visualization, and modelling.

What does data science involve?

As you can see, data science involves many fields, like computer science, mathematics, and statistics. What is data mining? Data mining is the process of extracting previously unknown information from big data sets using analysis tools and algorithms.

Although your professional field may not be in data science, it is crucial to understand the process of data analytics. Whether you are seeking methods to solve your problems at work, or you have a small spark of curiosity for data science, the training on data analytics will help.

Data analytics

Here is a problem that you could be interested in. Let’s talk about customer retention. Cell phone service often has a fixed contract period. After that, some customers switch to other providers’ services. It calls churning. The percentage of customers that stopped using your company’s product or service during a specific time frame calls churning rate.

If you could predict the customer churn, you could come up with some ideas to prevent it. There are so many similar problems. Think of Netflix and the newly offered Disney streaming products. How many of Netflix’s existing customers will switch to new Disney’s streaming product?

The importance of data analytics

We can solve those business-related problems by analysis of data, not by pure intuition. We call this process data-driven decision making. Data-driven decision making needs data analysis, but before then, we need to engineer and process the data. In this process, big data technology plays a critical role.

You might have heard the names like Hadoop, HBase, and Mongo. All of these are data technologies.

Making data-driven decisions

How will you make data-driven decisions well? First of all, you must understand the fundamental concept of the problem you like to solve. For example, you will not be able to find the right method for the data analysis unless you understand the issues. Let’s go back to our cell phone churning problem.

You have to understand what the exact issue is first. Second, you have to have a data analysis framework. What will happen if you do not have data analytic thinking?

If you’d like to learn more about the importance of data-driven decisions, check out the full online course, from Sungkyunkwan University (SKKU), below.