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Data Science and Data Screaming

US Marine Corps Air Station Miramar_Jeff M. Wang_1561698986
(U.S. Navy Blue Angels, U.S. Marine Corps Air Station Miramar, Jeff M. Wang)

Data Science is About Extracting Knowledge from Data!



We’re living in the age of data. We have access to more data than ever before. And we’re using it in a lot of ways. From analyzing and understanding customer behaviors to collecting insights for software QA companies, organizations of all kinds are using large datasets on a daily basis.


Data Science


- What is Data Science?

Data science is a big umbrella covering each aspect of data processing and not only statistical or algorithmic aspects. Data science includes:

  • Data visualization: It is a general term that describes any effort to help people understand the significance of data by placing it in a visual context.
  • Data integration: It is the process of combining data from different sources into a single, unified view. Integration begins with the ingestion process, and includes steps such as cleansing, ETL mapping, and transformation.
  • Dashboards and BI: A business intelligence dashboard (BI dashboard) is a data visualization tool that displays on a single screen the status of business analytics metrics, key performance indicators (KPIs) and important data points for an organization, department, team or process.
  • Distributed architecture: data architecture is composed of models, policies, rules or standards that govern which data is collected, and how it is stored, arranged, integrated, and put to use in data systems and in organizations.
  • Data-driven decisions: It is an approach to business governance that values decisions that can be backed up with verifiable data.
  • Automation using ML: It represents a fundamental shift in the way organizations of all sizes approach machine learning and data science.
  • Data engineering: It is the aspect of data science that focuses on practical applications of data collection and analysis.


- Extracting Knowledge from Data

Data Science is about extracting knowledge from data. It is about methods to turn high-volume data and fragmented information into actionable knowledge. How can we design robust, principled models to combine complex data sets with other knowledge sources?  How can we design models that summarize and generate hypotheses from such data?  How can we characterize the uncertainty in large, heterogeneous data to provide better support for decisions? Data science techniques are scalable architectural approaches, software, and algorithms which alter the paradigm by which data is collected, managed and used.

Data science, also known as data-driven science, is an interdisciplinary field about scientific methods, processes, and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining.  It can be thought of as a basis for empirical research where data is used to induce information for observations. These observations are mainly data (or big data) related to a business or scientific case. 

Insight, the data products of data science, is extracted from a diverse amount of data through a combination of exploratory data analysis and modeling. However, data science is not static. It is not one time analysis. It involves a process where models generated to lead to insights are constantly improved through further empirical evidence, or simply, data. Using data science and analysis of the past and current information, data science generates actions. This is not just an analysis of the past, but rather generation of actionable information for the future (or a prediction), like the weather forecast.

Machine learning is the core step in data science in which we deploy machine learning methods and statistics methods to get knowledge and to learn models from the data. So these models could be either classification models, clustering models, regression, density estimation, and so on and so forth.


Building a Big Data Team and Strategy


In reality, data scientists are teams of people who act like one. A data science team often comes together to analyze situations, business or scientific cases, which none of the individuals can solve on their own. There are lots of moving parts to the solution. But in the end, all these parts should come together to provide actionable insight based on big data. Being able to use evidence-based insight in business decisions is more important now than ever. Data scientists have a combination of technical, business and soft skills to make this happen.

When building a big data strategy, it is important to integrate big data analytics with business objectives. Communicate goals and provide organizational buy-in for analytics projects. Build teams with diverse talents, and establish a teamwork mindset. Remove barriers to data access and integration. Finally, these activities need to be iterated to respond to new business goals and technological advances.



[More to come ...]



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