We will return to these issues later in the course.ġ5 Big Data The launch of the Data Science conversation has been sparked primarily by the so-called “Big Data” revolution. Discussion and good policy regarding privacy, security, and the ethical use of data about people lags behind the methods of collecting, sharing, archiving, and analyzing data. One thing that is new is the greater variety of data and, most importantly, the amount of data available about humans. There are many representations of the this lifecycle.ġ1 Data Curation Data curation is a term used to indicate managementĪctivities related to organization and integration of data collected from various sources, annotation of the data, and publication and presentation of the data such that the value of the data is maintained over time, and the data remains available for reuse and preservation.ġ3 What is Missing? Most definitions of data science underplay or leave out discussions of: Substantive theory Metadata Privacy and Ethicsġ4 Privacy and Ethics Data, the elements of data science, and even so- called “Big Data” are not new. These same new visions treat the process as dynamic – archives are not just digital shoe boxes under the bed. New visions of this process in particular focus on integrating every action that creates, analyzes, or otherwise touches data. This lifecycle generally refers to everything from collecting data to analyzing it to sharing it so others can re-analyze it. Virtually all data analysis focuses on data reduction Data reduction comes in the form of: Descriptive statistics Measures of association Graphical visualizations The objective is to abstract from all of the data some feature or set of features that captures evidence of the process you are studyingĩ The Data Lifecycle Data science considers data at every stage of what is called the data lifecycle. There are many visual representations of Data ScienceĨ Data Analysis We analyze data to extract meaning from it. Focuses on the skills needed to collect, manage, store, distribute, analyze, visualize, reuse data and on data storytelling. There are many, but most say data science is: Broad – broader than any one existing discipline Interdisciplinary: Computer Science, Statistics, Information Science, databases, mathematics Applied focus on extracting knowledge from data to inform decision making. Presentation on theme: "Introduction to Data Science"- Presentation transcript:Ĭontains new material that will be discussed in November 2018Ģ What is Data Science? What words come to mind when you think of Data Science? What experience do you have with Data Science? Why are you taking an Introduction to Data Science / Data Mining Class?
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |