#3. DATA SCIENCE: INTRO TO BUSINESS ANALYTICS, DATA ANALYTICS AND DATA SCIENCE.

Hello Steemians,

In today's post, I will shed more light on the most popular disciplines in the data science field and how they all intertwine. As you know, everything is not particularly clear-cut, but the following explanations will help untangle the naughty mess of activities in the data science and business world. Let's dive in.

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Let's begin with a list of terms covering a few aspects of the business sphere:
Business case studies
Qualitative analytics
Preliminary data reports
Reporting with visuals
Creating dashboards, and
AB testing.

Let's go ahead to explain them, what do you think they all have in common? they are all part of the business world but then how many of these terms involve working with data? if we decide to think forward and place the activities for which it is essential to have data available there which ones, if any, would overlap with the business world? the answer is some. That is because some of the business activities are data-driven, while others are subjective or experience-driven. Of course, there are disciplines that will overlap, we will place them in between business and data. So let's see, which of the subfields listed above represents a business activity that involves data? well, you would need data to create a preliminary report, a visual representation of the performance of your company for last year, a business dashboard and to do AB testing, that is to choose the best next versions of your products. So these four labels can sit comfortably in the overlapping area. Now the other two terms, business case studies and qualitative analytics get left behind as they belong only to the sphere of business. they are not related to working with data meaning quantitative data.

Let's explore why, business case studies are real-world experiences of how business people and companies succeed or fail. you don't need a data set to learn from business cases. The same can be said for qualitative analytics. As I discussed in my last post, this is all about using your intuition and knowledge of the market to help in the future planning process.

Now, Let me introduce a timeline to our topic, this is because some of the terms you see refer to the activities that aim to explain past behavior, while others refer to activities used for predicting future behavior, let me take you through it. well, the next set of activities will be regarding analytics, future planning, and forecasting because the last activities talked about were related to the analysis of past events or data.

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Business case studies examine events that have already happened. For instance, one could learn from them and attempt to prevent making a similar mistake in the future. As a result, we could say studying business cases is part of our analysis, so this activity refers to the past. Now qualitative analysis includes working with tools that help predict future behavior, therefore, what we have now is qualitative analytics which belongs to the area of business analytics. while learning from business case studies is part of your business analysis. However, this is not what many professionals would say in practice, analytics has become a term comprising both analysis and analytics.

Let's talk about the rest of the terms we listed before, that is preparing a report or dashboard, these always reflect past data, so these terms will remain untouched. Although AB testing involves analyzing past data, it is a future-oriented activity that aims to predict future outcomes. it is about hypothesizing which of two potential product versions or treatments or policies, A and B would be more sensible to implement next, so moving AB testing to the analytics side ensures it remains within the intersection of business analytics and data. Furthermore, it is common practice to use the encompassing term data analytics to refer to both the analyses and analytics. while we know what the terms mean now, to keep things simple following the standards of other business professionals is a good idea.

The most sparkly of them all is Data science. Data science is a discipline reliant on data availability, particularly quantitative data. while business analytics does not completely rely on data. However, data science and corporate data analytics are mostly the parts that use complex mathematical, statistical, and programming tools. so data science does not overlap with data analytics completely, but it will reach a point beyond the area of business analytics. this means that the preliminary data report, reporting with visuals creating dashboards and AB testing are of interest to a data scientist.