Introduction to Data Science

Introduction to Data Science

1. Data science

   • A branch of big data that aims to extract and interpret information derived from the huge amount of data gathered by a particular company, whether for their own use or for operations they might carry out with third parties. (Ironhack, (s.f.))
     • Divided in: computer science, statistics and domain expertise
   • Application
     • - Search engines, which use algorithms to provide better responses to users' queries and in the shortest time possible.
     • - Artificial intelligence.
     • -Development of recommendation systems. In terms of primarily visual content, such as Amazon, Netflix. They offer customers much more accurate recommendations, which greatly enriches the user experience.
     • Data scientists are masters in predicting the future.
   • Required skills
     • -Probability & Statistics - Multivariate Calculus & Linear Algebra -Programming, Packages and Softwares -Data Wrangling -Database Management -Data Visualization -Machine Learning / Deep Learning -Cloud Computing -Microsoft Excel -DevOps
   • Relationship with
     • Statistics
       • It is a branch of data science, which allows to analyze data, finding patterns, making predictions depending on what happened in the past.
     • Big Data
       • It makes no sense to recollect and count on a big amount data and make nothing out of it. You can exploit it by identifying patterns that can lead to a better decision making, depending on your area.

2. Data Analytics

   • More specific and precise application of data science. The analysts' role has been to search for unprocessed sources of information in order to try and find trends and metrics that could help companies make more accurate decisions and obtain better results. (Ironhack, (s.f.)) It is more about interpretation.
     • Divided in predictive, prescriptive and descriptive.
   • Application
     • -Sectors such as healthcare, allowing health centres to care for their patients more efficiently.
     • Industries such as energy management, since, thanks to data analysis, they can optimise where resources are used and even choose to automate certain services, thus avoiding unnecessary costs
     • -Hospitality industry, as they can help hotels discover travellers' preferences and offer them alternatives that best suit their tastes and needs.
     • Data analysts extract the most relevant information from the same data sets.
   • Required skills
     • -Structured Query Language (SQL) -Microsoft Excel -Critical Thinking -R or Python-Statistical Programming -Data Visualization -Presentation Skills -Machine Learning
   • Relationship with
     • Big Data
       • It makes no sense to recollect and count on a big amount data and make nothing out of it. In here, you just interprete, don't really go any further
     • Statistics
       • It refers to the data analyzed, that can be from different periods of time and scenarios

3. Movie

   • Both movies had a evolve around the fact that both protagonists had found an unbelievable event, related to their working fields, that came from a deeper analysis in the data that they got, making predictions according to certain behaviors observed in the past. This refers to the importance of taking actions out of data, and of course starting by analyzing it, because we commonly gather big amounts of it and don’t even bother on taking a look at it, ignoring the fact that it is can throw valuable information.