What Does Analytics Mean in Data Analytics – Data Science Jargon for Beginners

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Updated November 24, 2017

Whenever you’re exploring a new industry there are a lot of buzz-words and technical jargon that you need to familiarize yourself with in order to become proficient within your research and interest.

Analytics is our Buzz-word of the day.

When it comes to the Data Science industry analytics is all the rave. Let’s look at analytics and its three counterparts.

Analytics: To research, observe raw information, and come to a conclusion concerning the discovered data. This research is executed by turning obscure data and numbers into practical solutions. Data Analyst must be good at communicating their research in practical direct applications to their clients. They must tell a story with the data. “Here is what you target market/patient is doing, and this is how you can reach/help them more”. Analytics is research, formatting, synthesizing, and communication. There are three main types of analytics…

  • Descriptive Analytics: The technique of summarizing the data into a simply story or chart. Rather than showing and explaining every piece of data bit by bit the analyst breaks it down and describes in simple terms the conclusion of the analyzed data set.
  • Predictive Analytics: By studying past stock market trends or buyers habits analyst can develop a firm grasp on predicting the future in specific markets. This type of analytics is not 100% accurate but it is insightful. This type of analytics involves using statistics and machine learning techniques to formulate data predictions.
  • Prescriptive Analytics: Known as the most powerful type of analytics. By leveraging the power of predictive analytics, it is most important for data analyst to prescribe a solution. This is where data is turned into an action that impacts real decisions within an industry. Having the ability to formulate data in a way that creates highly successful solutions is the reason companies are shelling out millions of dollars to data analysts. They realize that accurate prescriptions can yield exponential dividends.

As a Data Analyst, Business Analyst, or Data Scientist you will be performing these functions on a regular bases. That is why it is so important to have a firm understanding in four key areas:

  • Programing
  • Technical Tools
  • Mathmatics
  • Communication

For more info about the skills in demand check out this post: Top 4 Skills in Demand for Data Analytics

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