Learn how data mining and analytics work by analyzing different aspects of these two fascinating disciplines of data science. Explore core areas of data mining and analytics, such as text retrieval, classification, prediction, and clustering.
- [Instructor] Data mining and analytics involve…a myriad of data manipulation techniques.…Text retrieval is one of the most well-known…data mining techniques.…It builds on many foundational concepts and methods…developed by Natural Language Processing, or NLP.…Classification constructs a model…that labels a group of data objects…into a specific category.…In the classification model,…the classes with their own labels are discrete in nature.…
For instance, the same classification model can categorize…people into groups of trustworthy and untrustworthy users…of an online banking system.…Prediction builds a model that produces continuous…or ordered values that form a trend.…For instance, a prediction model can provide…estimated mean time to failure or MTTF values…for a computer.…Clustering is a process of grouping similar data objects…into a class.…
Clustering helps reveal features that distinguish…one class of data objects from the other,…leading to new discoveries on a dataset.…Uses of clustering analysis range from pattern recognition…
Jungwoo Ryoo is a professor of information science and technology at Penn State. Here he reviews the history of data science and its subfields, explores the marketplaces for these fields, and reveals the five main skills areas: data mining, machine learning, natural language processing (NLP), statistics, and visualization. This leads to a discussion of the five biggest career opportunities, the six leading industry-recognized certifications available, and the most exciting emerging technologies. Along the way, Jungwoo discusses the importance of ethics and professional development, and provides pointers to online resources for learning more.
- A history of data science
- Why data analytics is important
- How data science is used in fraud detection, disease control, network security, and other fields
- Data science skills
- Data science roles
- Data science certifications
- The future of data science
Skill Level Beginner
Insights on Data Science: Lillian Piersonwith Lillian Pierson, P.E.23m 51s Intermediate
1. Define Data Science
6. Future of Data Science
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