Data Science

Data Science

1. Definition. Is the field of applying advanced analytics techniques and scientific principles to extract valuable information from data. It can be for business, politics, government, among others use.

   • Margin Call: In the video we can see how a chair of directors, specifically the CEO and the person in charge of collecting the statistics, analyze the possible scenarios. That is what Data Science is in charge of, it collects the necessary information for the Data Analyst to make decisions to see how to make sure that the position of the company is always at the best scenario. He says it well in the video, his mission is to see what stage will be at the moment, in the coming days and months.
   • The Big Shot: In this case, we found ourselves in a scenario in which everything seemed stable and worked, the mortgage bods were really inflated and it was a matter of time before the problem exploded. Therefore, here we have the principal, who is a data analyst, analyzing a scenario so that together with his boss they make the best investment decision. That is the importance of our subject, while the Data Science gathered all the information just to confirm that everything was fine, the Data Analyst found the problem and a way to win.

2. Data Analytics Definition. After you gathered the information, the Data Analysis collect, structure and process data. At the beginning you had unstructured data, now you have actionable intelligence.

   • So... what is the difference between this two? The Data Scientist asks questions and extracts information from various sources, and the Data Analytics, on the other hand, is responsible for answering them and only focuses on one source.
     • In other words, Data science is the previous step to Data Analytics, because without it you cannot study or analyze anything.
     • Skills Needed: Knowledge of probability and statistics for data analysis, Statistical programming for the construction of analysis models, Ability to synthesize data, Critical thinking to examine the information in depth, and Understanding of the company's business model.
   • Statistical Analysis Definition. Is a component of Data Anlytics, where they collect and interprate the data in order to uncover patterns and trends

3. Skills Needed: Math, Programming, Visualization, Predictive Modeling, and Business and Social Skills

   • As we can see, in Data Science the theoretical part is needed more, while in Data Analytics it is more skills of knowing how to interpret and with creativity.
     • Both can be used mainly in companies for strategies within a company, in governments for decision-making on which sectors to support, for example, among many others

4. Big Data Definition. Refers to massive and can be structured or unstructured data, where the most comon tools are inadequate.

   • Data analytics allows you to formulate hypotheses and speculate future scenarios to see which is the most convenient, while statistical analysis also allows you to test them. They collect, analyze and make conclusions from data, while analytics don´t try them, formulate hypothesize only.