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BU.510.650.K2– Data Analytics
发布时间:2023-12-09
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Data Analytics
2 Credits
BU.510.650.K2
Fridays 1:30 – 4:30 PM
10/27/2023 – 12/22/2023
Fall 2, 2023
Room 215, Harbor East, Baltimore, MD
Instructor
Mohammad Ali Alamdar Yazdi
Contact Information
Office Hours
Wednesdays 5 - 7 PM
https://jhucarey.zoom.us/j/917963295?pwd=RkxnVWRRcVdTMnpMQ1lSYzg1YTBNdz09
Required Texts & Learning Materials
There is no required textbook: All required class materials will be available on our Canvas website. However, some books are very useful if you want to learn more about data analytics and its applications. The best way to learn is by doing (especially for R programming)
Optional Textbook 1 (solid primer, with theory and explanation):
An Introduction to Statistical Learning with Application in R, by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani;
Publisher: Springer (2021); ISBN-13: 978- 1071614174;
The book is free at: https://www.statlearning.com/
Optional Textbook 2 (a great advanced text):
Elements of Statistical Learning: Data Mining, Inference, and Prediction, by Trevor Hastie, Robert Tibshirani and Jerome Friedman;
Publisher: Springer (2009); ISBN-13: 978-0387848570;
It requires some mathematical sophistication and goes beyond the material we will be covering. The book is free at: https://hastie.su.domains/ElemStatLearn/printings/ESLII_print12_toc.pdf
Software:
. We require the R Statistical Software, which is powerful and free. R can be downloaded at the link below:http://www.cran.r-project.org/
. Rstudio is a free platform for both writing and running R, available atwww.rstudio.org.Some students find it friendlier than basic R (especially in windows OS).
. The learning curve is very steep. Students can become proficient in a few weeks. Some manuals are very helpful to learn R, e.g.,http://cran.r-project.org/manuals.html
• I provide limited software instruction, in-class demonstration, and code to accompany lectures and
assignments. We do not assume that you have used R in a previous class. However, this is not a
class on R. Like any language, R is only learned by doing. You should install R as soon as possible and familiarize yourself with basic operations.
• Additional resources:YouTubeintros to R.
Course Description
This course prepares students to gather, describe, and analyze data, and use advanced statistical tools to make decisions on operations, risk management, finance, marketing, health care management, etc. Analysis is done targeting economic and financial decisions in complex systems that involve multiple partners. Topics include probability, statistics, hypothesis testing, regression, clustering, decision trees, forecasting, and unsupervised learning, etc.
Prerequisite(s)
Learning Objectives
By the end of this course, students will be able to:
1. Gather sufficient relevant data, conduct data analytics using scientific methods, and make appropriate and powerful connections between quantitative analysis and real-world problems.
2. Demonstrate a sophisticated understanding of the concepts and methods; know the exact scopes and possible limitations of each method; and show capability of using data analytics skills to provide
constructive guidance in decision making.
3. Use advanced techniques to conduct thorough and insightful analysis, and interpret the results correctly with detailed and useful information.
4. Show substantial understanding of the real problems; conduct deep data analytics using correct methods; and draw reasonable conclusions with sufficient explanation and elaboration.
5. Write an insightful and well-organized report for a real-world case study, including thoughtful and convincing details.
6. Make better business decisions by using advanced techniques in data analytics.
To view the completelist of the Carey Business School’s general learning goals and objectives, visit theCarey website.
Attendance
Attendance and class participation are part of each student’s course grade. Students are expected to attend all
scheduled class sessions. Failure to attend class will result in an inability to achieve the objectives of the course. Excessive absence will result in loss of points for participation. Regular attendance and active
participation are required for students to successfully complete the course.
Class participation is an important part of learning. If you have a question, it’s likely that others do as well. I encourage active participation, and course grades will consider students who make particularly strong contributions.
Assignments
Late submissions— including assignments, and exams—will not be accepted.
Assignment |
Learning Objectives |
Weight |
Attendance and participation in class discussion |
|
10% |
Individual Homework |
1, 2, 3, 4, 5, 6 |
50% |
Final Exam |
1, 2, 3, 4, 5, 6 |
40% |
Total |
|
100% |
Study Groups (not required, but highly recommended)
Many students learn better and faster when working in a group, so I encourage collaborative learning. You can work together in a study group with several students to discuss class materials and homework assignments on a weekly basis. However, each student must write the homework assignment individually; your text should reflect your own understanding of the materials.
Grading
The grade of A is reserved for those who demonstrate extraordinary performance as determined by the
instructor. The grade of A- is awarded only for excellent performance. The grades of B+ and B are awarded for good performance. The grades of B-, C+, C, and C- are awarded for adequate but substandard
performance. The grades of D+, D, and D- are not awarded at the graduate level. The grade of F indicates the student’s failure to satisfactorily complete the coursework. For Core/Foundation courses, the grade point
average of the class should not exceed 3.35. For Elective courses, the grade point average should not exceed 3.45.
Tentative Course Calendar
Instructors reserve the right to alter course content and/or adjust the pace to accommodate class progress. Students are responsible for keeping up with all adjustments to the course calendar.
Week |
Date |
Weekly Objectives/Topics |
Recommended Reading (Textbook 1) |
Assignments |
1 |
10/27 |
Introduction to Statistical Learning |
Chapter 1 |
|
2 |
11/03 |
Data Summarization and Visualization |
Chapter 2 |
HW 1 |
3 |
11/10 |
Linear Regression |
Chapter 3 |
HW 2 |
4 |
11/17 |
Decision Trees |
Chapter 8 |
HW 3 |
- |
11/24 |
Thanksgiving Break |
||
5 |
12/01 |
Clustering |
Chapter 12 |
HW 4 |
6 |
12/08 |
Logistic Regression |
Chapter 4 |
HW 5 |
7 |
12/15 |
Model Selection |
Chapters 5, 6 |
HW 6 |
8 |
12/22 |
Final Exam (class time and remote) |
Carey Business School Policies and General Information
Please note that failure to become acquainted with Carey policies will not excuse any student from adhering to these policies.
Canvas Site
A Canvas course site is set up for this course. Each student is expected to check the site throughout the
semester as Canvas will be the primary venue for outside classroom communications between the instructor and students. Students can access the course site athttps://canvas.jhu.edu/.
Technical Support
24/7 technical support for questions regarding Canvas, Zoom, and other technical issues is available. Please refer to Carey’sAcademic Resources webpagefor contact information and other details.
Students with Disabilities - Accommodations and Accessibility
Johns Hopkins University values diversity and inclusion. We are committed to providing welcoming, equitable, and accessible educational experiences for all students. Students with disabilities (including those with
psychological conditions, medical conditions, and temporary disabilities) can request accommodations for this course by providing an Accommodation Letter issued byStudent Disability Services. Please request
accommodations for this course as early as possible to provide time for effective communication and
arrangements. For further information or to start the process of requesting accommodations, please contact Student Disability Servicesat the Carey Business School.
Academic Ethics Policy
Carey expects graduates to be exemplary global citizens in addition to innovative business leaders. The Carey community believes that honesty, integrity, and community responsibility are qualities inherent in an exemplary citizen. The objective of the Academic Ethics Policy (AEP) is to create an environment of trust and respect
among all members of the Carey academic community and hold Carey students accountable to the highest standards of academic integrity and excellence.
It is the responsibility of every Carey student, faculty member, and staff member to familiarize themselves with the AEP and its procedures. Failure to become acquainted with this information will not excuse any student,
faculty, or staff member from the responsibility to abide by the AEP. Please contact theOffice of Student Affairsif you have any questions. For the full policy, please visit theAcademic Ethics Policy webpage.
Student Conduct Code
The fundamental purpose of the Johns Hopkins University’s regulation of student conduct is to promote and to protect the health, safety, welfare, property, and rights of all members of the University community as well as to promote the orderly operation of the University and to safeguard its property and facilities. Please contact
theOffice of Student Affairsif you have any questions regarding this policy. For the full policy, please visit the Student Conduct Code webpage.
Commitment to Respect
Respectful behavior creates an environment within the Carey Business School where all are valued and can be productive. Carey defines respectful behavior as conduct that, at a minimum, demonstrates consistent
courtesy for others, including an effort to understand differences. As such, all in the community agree to the Carey Commitment to Respect, which states that we all strive to show that we value each other’s human
dignity and our differences, and to choose behavior and language that demonstrates mutual respect. Please visit theCommitment to Respect webpageto learn more about the expectations and resources available.
Classroom Policies for All On-Site and Remote-Live Classes
Carey is committed to maintaining the highest standards of excellence in all forms of instruction. To that end,
we have developedpolicies and procedures for all classes offered in on-site and remote-live formats. These policies will govern all courses occurring in these formats, and all students are expected to familiarize
themselves with and adhere to these policies.
Student Success Center
The Student Success Center offers assistance in core writing and quantitative courses. For more information, visit theStudent Success Center webpage.
Other Important Policies and Services
Students are encouraged to consult theStudent Handbook and Academic CatalogandStudent Services and Resourcesfor information regarding other policies and services. For your convenience, there is a singular
website students can visit to learn about allJHU and Carey policies.
Copyright Statement
Unless explicitly allowed by the instructor, course materials, class discussions, and examinations are created for and expected to be used by class participants only. The recording and rebroadcasting of such material, by any means, is forbidden. Violations are subject to sanctions under theAcademic Ethics Policy.