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It is all about Data Analytics and Data Science

“Everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.”  This concept applies to a great deal of data terminology. While many people toss around terms like “data science,” “data analysis,” “big data,” and “data mining,” even the experts have trouble defining them. Here, we focus on one of the more important distinctions as it relates to your career: the often-muddled differences between data analytics and data science.  Data Analytics vs. Data Science  While data analysts and data scientists both work with data, the main difference lies in what they do with it. Data analysts examine large data sets to identify trends, develop charts, and create visual presentations to help businesses make more strategic decisions. Data scientists, on the other hand, design and construct new processes for data modeling and production using prototypes, algorithms, predictive models, and custom analysis.

How Using The Force Of Job Connectivity Can Help Your Team Work Better

“Well, the Force is what gives a Jedi his power. It’s an energy field created by all living things. It surrounds us and penetrates us; it binds the galaxy together.”   Obi-Wan Kenobi When it comes to working in Hong Kong, the employment market is our galaxy, and we, hopefully, are the stars within it. For the first time, we can start to  visualize  the Force that binds our companies together, that helps hold our relationships with colleagues, teams and departments. We can also begin to understand potential individual pathways for navigating companies, and which directions are possibly the most open and closed for us to take. The Force graph below represents the top career networks that exist between job functions in the  entire  Hong Kong Job Market. The size of the arrow illustrates the frequency of movements. Hong Kong Career Network Force Diagram (Source: Richard Hanson) There are a number of interesting observations to be made from studying this data; 1. M