What is Data Science?

Data Science is a field in which people use different methods like algorithms, scientific methods, processes, and so on to extract information from sets of data. For example, looking at customer ratings and extracting the reasons why they are loyal or unloyal.

First, we have to get some buzzwords out of the way to go deeper into how information is extracted for example the differences between Analysis and Analytics.

Analysis: Is too look at data in small chunks of a data set and see how they relate to one another. But the important thing is that Analysis is always performed on information that has already happened.

Analytics: Is the application of Logical or Computational reasoning to the analysis in order to find a pattern and explore future events. It is also divided into two categories Qualitative, which is the use of the analysis plus intuition, and Quantitative, where formulas and algorithms are used. Some people use the term Analytics to mean Analysis + Analytics.

The whole process can be separated into two parts Data and Data Science.

Types Of Data

Data can be separated into 2 categories Traditional data and Big data. Traditional data is always structured and occupies small storage space so it can be saved in one computer. One example is a list of employees where all their data is saved (birthdays, nationality, etc).

While Big data can also be structured or unstructured plus it can be in a variety of ways like audio files, videos, photos, and so on. That is why it uses a lot of storage space so usually is stored in more than one computer. Its size is usually stated as 3Vs, 5Vs, 7Vs, 11Vs. One thing to remember ist data is always in the past so its something that already happened.

###Data Scince###

Data Science is separated into 3 parts in the course Business Intelligence, Traditional Methods, and Machine Learning.

Business Intelligence includes all technologies-driven tools involved in the process of analyzing, understanding, and reporting available past data. So is the next step of the process after gathering the data you need for the project. Usually to report the data usually Business intelligence reports or Business intelligence dashboards. BI is used to make decisions to extract insights and extract ideas.

The next step is to try and forecast future events and that is why Traditional Methods or Machine Learning come into play.

Traditional Methods is predicted using advanced statistics. it can be very accurate if used correctly. popular examples are Regression, Cluster, or factor techniques.

Machine Learning is the utilization of AI (Artificial Inteligence) to predict future events. Basically using a combination of Math and computing power.

Those two are the last step of the process next week I am gonna learn of popular methods for each of the scenarios and steps before going into the actual statistics and math.