Types of Analytics

Types of Analytics

• Decision analytics: supports decision-making with visual analytics that reflect reasoning
• Descriptive analytics: provides insight from historical data with reporting, scorecards, clustering, etc.
• Predictive analytics: employs predictive modeling using statistical and machine learning techniques
• Prescriptive analytics: recommends decisions using optimization, simulation, etc.

Data Science, Social Science, and Computational Social Science

• Data science is used in government policies, people's mandates in elections, and hiring strategies in the private sector
• Information science involves understanding information in different contexts and circumstances related to data creation, generation, and sharing

Information vs. Data

• Data is something raw and meaningless, while information is data with meaning and purpose
• An example of data vs. information is the number "480,000" being a data point, but when explained as the number of deaths per year in the USA from cigarette smoking, it becomes information

Data Science and Statistics

• Data scientists should have at least three basic skills: knowledge of statistics, computer science skills, and data visualization skills
• The difference between data science and statistics lies in the invention and advancements of modern computers

Data Science and Computer Science

• Data science and computer science overlap and are mutually supportive
• Computer science algorithms and techniques, such as machine learning and data visualization, have contributed to the data science discipline

Data Science and Engineering

• Engineering in various fields has created demand for data scientists and data science methods
• Data science has benefitted from new software and hardware developed via engineering, such as CPU and GPU

Data Science and Business Analytics

• Business analytics (BA) refers to the skills, technologies, and practices for continuous iterative exploration and investigation of past and current business performance
• BA focuses on developing new perspectives and making sense of performance based on data and statistics
• There are four types of analytics: descriptive, predictive, prescriptive, and decision analytics

Data Science Definition and Importance

• Data science is a multidisciplinary blend of data inference, algorithm development, and technology to solve analytically complex problems
• Data science is a field of study and practice that involves the collection, storage, and processing of data to derive important insights and make better, fact-based decisions
• The "3V model" describes the characteristics of big data: velocity, volume, and variety
• Data science is important now because it is virtually everywhere, and its applications are numerous, including finance, public policy, politics, healthcare, urban planning, education, and libraries

This quiz covers the different types of analytics, including decision, descriptive, predictive, and prescriptive analytics, as well as their applications in data science and social science.