代做Global Data Analytics 34:816:637:01 Spring 2024代写留学生Python语言

Global Data Analytics

34:816:637:01

Spring 2024

Catalog Description: The theoretical and operational selection of policy models used to assess the impacts of regional, national, and global socio-economic and environmental policies.

Course Overview: The course Global Data Analytics covers many key materials and methods needed by public informatics students. Given the target set of students for the program, the course highlights two key topics of interest internationally: (1) the measurement by industry of energy resource use as well as the production of solid waste, effluents, and air pollution and (2) changes in levels of international exchange, particularly of the trade of goods. This is done by familiarizing students with UN protocols for measuring gross domestic product (GDP) and, hence, national accounts, particularly supply and use tables for detailed sectors and commodities. Through a series often (10) assignments, students learn how to read, write, manipulate, and update arrays in the R language, relying on their knowledge of MS Excel in the process. They will learn how to estimate and use subnational accounts as well. They will perform economic impact analysis, summarize results properly, identify industries important to an economy, discover proximate causes of change in an economy, and estimate the relative importance of an   industry in an economy to global supply chains. The policy relevance of all aspects of the course is discussed. Students present subject matter of interest to them that is connected to course material.

Course Learning Objectives include:

•    Developing a critical understanding of a core interindustry and interregional modeling method in public data analytics.

•    Demonstrating an ability interpret the modeling that they perform.

•    Understanding differences and similarities among key interindustry data sources as well as the analytical requirements and practical challenges encountered with them.

•    Gaining facility in basic computational matrix methods using the R language, which is translatable to related languages like Matlab, Python, and Octave.

Course Learning Achievements - Students will:

Demonstrate their ability to appropriately select and operationalize computable models for use with global data sets by:

1.   defining the system of equations;

2.   retrieving, entering, and analyzing required data;

3.   estimating key coefficients and parameters;

4.   testing sensitivity of the results to model assumptions; and

5.   interpreting their results.

Assessment: Students complete ten laboratory exercises that demonstrate their understanding of the theoretical foundations and best practice applications of various simulation models. (They are graded on the best eight.) They also will have demonstrated that they can program in the R language and document what they have done.

Effectively communicate the results of the models in written and oral formats.

Assessment: Student skills and confidence with modeling techniques develop through the exercises. Oral and written communication is assessed from student-led discussions and presentations.

Grading Strategy:

•     Ten (10) lab exercises 80%

•     Class presentation & participation 20%

Presentation:

Class participants will have 10-15 minutes to present the preliminary content of their paper in class using 5-10 slides. This aspect of the course will be graded upon the content of the presentation material, which will be submitted online prior to the class hour in which it is orally presented. Grading will be largely based upon the use of novel visuals and the participant’s ability to reply to questions. Ability to present that material itself will be a secondary consideration.

If, instead, you prefer to produce an application rather than present, the application must be extremely well internally documented and be accompanied by a User’s Guide that is more than five (5) pages long.

Required Textbooks

Davies, TM. (2016) The Book of R. No Starch Press, San Francisco.

Miller, RE and Blair, PD. (2022) Input-Output Analysis: Foundations and Extensions, 3rd  ed.

Cambridge University Press: NYC. (a prior edition is available as a course file on Canvas)

There is an RU library site for learning R that includes links to online video resources. Rutgers also has access to certified courses for R on LinkedIn.