Data Science is an interdisciplinary field; it is the set of methods, techniques and theories to extract knowledge or a better understanding of the information in data coming from multiple sources, whether structured or unstructured. And focus their results in various fields: marketing and advertising, improvement of production processes, public services, scientific and medical research, business intelligence, etc.
This is first a work of Data Mining, and Data Analytics, which uses Big Data technology to cover a large amount of information and guide it to real applications such as those mentioned above. In fact, Data Science is a continuation of some fields of data analysis such as statistics, Data Mining itself, automatic learning or predictive analytics.
DATA SCIENCE PRACTICAL USE
People who engage in Data Science are known as Data Scientist. The Master in Data Science project defines Data Scientist as a mix of statistician, computer scientist and creative thinker. Companies can use the work of these scientists to guide their daily operations in aspects such as:
- Locate, interpret and merge data sources
- Generate data sets and ensure consistency
- Create visualization systems to understand the analysis of data previously made
- Create dashboards to streamline and relate decision making and business activity
- Provide ongoing and dynamic analysis for strategic critical aspects
- Contribute to understanding in real time the evolution of the market and consumers at the level of commercial and marketing strategy
BIG DATA AND DATA SCIENCE
Big Data is the process of collecting large amounts of data, continuous storage and real-time analysis; looking for repetitive patterns within the data to extract conclusions and proposals (find hidden information, new correlations, etc.).
Big Data is based on 5 key aspects:
- Volume: Locate and organize all information encompassed by companies or entities in order to be able to be treated and studied for strategic decision making
- Speed: Fast data collection is necessary for continuous storage and analysis and dynamically applicable to business decisions. Being compatible with a continuous update and extending the previous results and conclusions
- Variability: The data changes and is constantly generated. Various sources of data and methods for their subsequent storage and study should be found to provide complete reports on the strategic information they contain and at the same time be able to be treated and presented as a single set
- Value: The extracted data must present quality by its sources and methods of processing and study. Like Big Data can report great strategic benefits; The low quality of the sources and analysis can mean enormous sources of losses, internally and commercially for a company.
- Visualization: It should be possible to present the data obtained in a way that can be easily understood the aspects and key factors contained in the reports made. They are therefore important tools such as Dashboards to promote a visual and graphic knowledge that allows better understanding of many factors contained at a strategic level.
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