代做ITI- 04.547.421 Data Analytics Summer 2024代写数据结构语言

School of Communication and Information

Data Analytics

ITI- 04.547.421

Summer 2024

Catalog Description

Data Analytics linked to storage, curation, management, and mining with attention to alternative methodological approaches. Course will demonstrate various methods to explore how big data might be analyzed, stored, and retrieved.

Pre-requisites: None.

Course Description:

Introduction to issues confronting information professionals when analyzing large data sets for inclusion into repositories. Students will propose and examine models to analyze data using software designed for such purposes. Data mining related methods will emphasize intended uses of such data. Individual and group assignments will focus on specific data analytic methods. Application areas for data analytics include requirements by funding agencies for data that is expected to be    organized and shared.

Major Course Topics:

1.   Introduction to Data Analytics

a.   What is Big Data? What is Data Mining? What is data curation?

b.   Basic Statistical Descriptions of Data

c.   Datafication and Data Visualization

2.  Value, Risk, Control.

3.   Machine Learning, Accuracy, Effect size issues.

4.   Predictive Analytics

    a.   Privacy

    b.   Responsibility

    c.   Crowdsourcing

5.   Data Curation

    a.   Integrating data

    b.   Cleaning and quality checks

6.   Model construction, analysis

    a.   SPSS

    b.   Other software programs (e.g. Excel)

7.   Methods

    a.   Correlations

    b.   Bilinear Regression

    c.   Multilinear Regression

8. Next Generation Data Science & Data Scientists

Instructional Objectives

This course will:

1.   Enable students to develop knowledge of statistics to understand the implications of extending smaller samples to massive data sets.

2.   Emphasize how to create a model to depict large data sets which might then be appropriate for the storage and curation of such data.

3.   Emphasize the concepts and terminology of data analytical methods as they apply to data analysis, curation and management.

4.   Cover the theoretical underpinnings of big data analytics to include such topics value, risk, and control as well as machine learning and accuracy.

5.   Provide an explanation of how large data sets differ by discipline and application areas and how these, in turn, influence and create issues for data management  and repository policies and practices.

6.   Explore predictive analytics in terms of core issues such as privacy, responsibility and crowdsourcing.

7.   Introduce the structures of different computer program packages appropriate for large data analyses, storage, and curation.

8.   Cover the objectives and goals of such methods as descriptive patterns, cluster analysis, and logistic regression.

9.   Explore applications of data science in areas to include social networks and data journalism.

10. Examine pertinent literature chronicling data curation at national and international levels.

11. Present alternatives in how large data sets might be curated in an organizational setting.

12. Provides an overview of the current and future roles of data scientists.

Learning Objectives [Corresponding Assessment(s)]

By the time this course is complete students will be able to:

1.   Use models and structures to depict large data sets within a data analytics environment. [Assignment 1]

2.  Apply statistical models to data using SPSS and related software programs.  [LinkedinLearning (previously called Lynda.com) tutorials and Assignment 3]

3.   Summarize, explain and, where appropriate, provide examples of predictive

analytic issues, language issues, algorithms, analytic methods, curation issues, and/or applications of data analysis to social networks. [Assignments 1,2,3]

4.   Create a tutorial guide to explain concepts and practices of structuring data sets, using software to analyze data, and depositing and making assessable big data  sets. [Assignment 3]

5.  Assess how different statistical models might be used to extract meaning from particular data sets. [Assignment 3]

6.   Specify an approach or approaches to manage and curate large sets within a particular application area, including issues such as storage, security, and privacy. [Assignments 2, 3]

7. Implement appropriate frameworks to identify the overall model for a big data set, how such data might be analyzed, curated placed it in a repository to provide appropriate access points for data set retrieval. [Assignments 1,2,3]

Organization of Course and Course Calendar

Note: Subject to change based on class progress and student interests.

Course Timetable and Schedule
Week		Topic	Readings/Activity	Assignments
				Due
05.28-	1	Course Overview.	Obtain and browse
06.02		Introduction to Big Data Topics: What is Big Data?	through the text. Introduce Yourself.
		Current state.	M/C Ch 1-2
06.03-	2	Early applications in history.	M/C
06.09		Precision of results. Relationships among data elements/variables. Datafication, Value, Implications, Risk, Control,	Ch 3-10
		Future Implications
06.10-	3	- Revision of Statistics	LinkedinLearning	Hands On
06.16		- Data Modeling	(LiL) tutorials - Excel Statistics - SPSS Tutorial	Exercise (HOE) #1: Variables and Associations
			-Instructor will obtain authorization to access SPSS
06.17-	4	- Sampling: Confidence Intervals	-Notes	Assignment 1:
06.23		-Data Curation	-LiL tutorials	Data Modeling
06.24-	5	Z-tests and Hypothesis Testing	-Notes	HOE #2: Z-
06.30			-LiL tutorials	scores
07.01-	6		-Notes	Assignment 2
07.07		Two Variable Analytics	-LiL tutorials
07.08-	7		-Notes	HOE #3:
07.14		Multiple Variable Analytics	-LiL tutorials	Correlations
07.15-	8	Reconciling Big Data and	-Notes	Assignment 3
07.19		Statistics. Note that class ends on Friday, July 19	-Readings

Assignments, Exams, and Methods of Assessment

See explanations of each requirement in later sections of this Syllabus and in Canvas

Assignment 1: Data Analytics Model: 30%

Assignment 2: Data Curation + Discussion Board: 30%

Assignment 3 (doubles up as Final Project) + Discussion Board: 30%

Hands-On Exercises X 3: 3%

Discussion Boards: 7% (1% for each week starting week 2)

Note: The content of each paper will also be included as a core topic in the Discussion  Boards. Students will be given a score for their participation in Discussion Boards. The  Discussion Boards provide a link between the text and related readings with the papers due in the course.

Grading Scale

A         92-100 (Please note 91.99 is not an A.)

B+       87-91.99

B         82-86.99

C+       76-81.99

C         70-75.99

F          <70 Grad / < 65 Undergraduates