5 D’s of Data Science

Here are the 5D

5 Ds of Data Science

  1. Data
  2. Digitalization
  3. Description
  4. Depiction
  5. Discovery

Data

In data science, the most needed is the data, the observations or examples. With this, we can describe how much, how strong, what are the value or measurement there is about a situation or a thing. Data existed when define the description of an event or if we measure something. This is the most important building block that we need to have in doing Data Science tasks. With data, we are able to show quantity and quality, and this will be the basis of our equations and statistics. We observe or sometimes use instruments or probe in order to gather data for our analysis or research.

Digitalization

We cannot process raw data when it is not digitized or put into a computer system or encoded into forms that can be processed. The format is not limited to text, graphics, spreadsheets, vectors, audio, video, we can use any digital format that we like. Through digitization, we can speed up the process of analysis and procedures being applied to gather the measures in statistics. We can then infer from the findings of things, and we can create more insight. Digitization makes the sharing of information easier as the data can be stored and retrieved for future use.

Description

Through the tools that we have, mathematical equations and statistics, we can describe the data that we have. We can determine if assumptions are right or wrong through hypotheses that we formulate. We can then deduce from what we have gathered, and those will help us understand more, and can guide us on the next steps on what we can do with data in order to solve a problem or understand a situation or use it to teach machines/computers. These machines in return will be put into practical use which can aid the human ability in different aspect of our lives, not limited to traffic, medicine, marketing, economics, planning, production, operations, understanding behaviors and many more.

Depiction

In Data Science, where use to do machine learning, we mine information, create training and testing sets, we can then depict or predict the future. Also with visualization, we can explain what we have just found out through insights. We can share the information available for consumption at a wide range of audience from academe, profession, medicine, science and the like. With depiction/visualization we can help different people understand what we have just found out. This is where data science becomes an art, a place of creativity and targeting with mass consumption.

Discovery

At the end of most research of a Data Scientist, a discovery from different insights is mostly been found or through the process clarity comes as the prize of hard work. The discovery from the tasks conducted can help to predict reality, give warnings and inform the people. Most stakeholders are the pharmaceutical company, doctors of medicine through BioStatistics and analysis, and some business or entreprise. The information uncovered can be a great help in making future decision on improving medicine, process, product or strategy such as those used in marketing campaign, designing educational things and also providing new products/services for the benefit of the people.

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