APANPS5205: Applied Analytics Frameworks and Methods II
应用分析框架和方法网课代修 All written assignments must use APA citation, cite sources, and be submitted to the course website (not via email).
Schedule
Dates: January 11, 2021 – April 15, 2021 Time: Thursdays, 8:10 pm – 10:00 pm ET Location: Click on Course Info for details
Credits 应用分析框架和方法网课代修
Office Hours By appointment
Response Policy During the term, the easiest way to reach me is via email. You will usually get a response within 48 hours. If you have a question about an assignment, you are advised to email me several days before it is due; if your email arrives within 24 hours of the due date, you may not receive a timely response.
Associate: Peter Ronga
Email pr2595@columbia.edu
Office Hours By appointment
Response Policy During the term, the easiest way to reach me is via email. You will usually get a response within 48 hours. If you have a question about an assignment, you are advised to email me several days before it is due; if your email arrives within 24 hours of the due date, you may not receive a timely response.
Associate: Michael Keith 应用分析框架和方法网课代修
Email mak2296@columbia.edu
Office Hours By appointment
Response Policy During the term, the easiest way to reach me is via email. You will usually get a
response within 48 hours. If you have a question about an assignment, you are advised to email me several days before it is due; if your email arrives within 24 hours of the due date, you may not receive a timely response.
Course Overview 应用分析框架和方法网课代修
Today, most organizations have an unprecedented amount of data at their disposal. The question facing these organizations is no longer on how to get more data but what do to with it. Analysis solutions popular in the past may not work as well on newer forms of data such as text, spatial, and unlabeled data. Addressing analysis needs of today’s organizations calls for new analytical frameworks.
Building upon the tools and foundational concepts from Frameworks and Methods I, this course introduces analytic techniques to handle less traditional forms of data, as well as more specialized analytic techniques to help organizations dig more deeply and comprehensively to create value from their data. 应用分析框架和方法网课代修
This course covers unsupervised learning techniques, including clustering, to examine unlabeled data and also covers natural language processing procedures, such as tokenization, to analyze text data.
The course further introduces neural networks and other specialized analytics frameworks. Students learn to integrate the techniques that they learned over Parts I and II of the Frameworks and Methods sequence and have the opportunity to apply these tools to real-world problems across topics or industries based on their areas of interest.
The pedagogical philosophy of this course is built around the principle of learning by doing. Each analytical technique will be discussed at a high level. Next, the analytical technique will be used to address an organizational problem in class in an interactive manner. This will be followed by a hands-on assignment in which you will use the same technique to tackle a different problem. Finally, you will combine different analytical frameworks to address an organizational problem in a team-based project.
Learning Objectives 应用分析框架和方法网课代修
L1: Identify and leverage analytical techniques to address organizational problems
L2: Construct analytical models to generate actionable insights from structured and unstructured data
L3: Implement analytical techniques using R
Readings
All required readings can be accessed freely through the Columbia library or the publisher using the links below.
Bivand, R., Pebesma, E., & Gómez-Rubio, V. (2013). Applied Spatial Data Analysis with R. Springer International Publishing. Retrieved from: h ttps://clio.columbia.edu/catalog/7935086
(https://clio.columbia.edu/catalog/7935086) . ISBN-13: 978-1-4614-7618-4. (Referred to as “Spatial Text”) 应用分析框架和方法网课代修
Chapman, C, & Feit, E. M. (2015). R for Marketing Research and Analytics. Springer International Publishing. Retrieved from: h ttps://clio.columbia.edu/catalog/11485551 (https://clio.columbia.edu/catalog/11485551) . ISBN-13: 9783319144351 (Referred to as “Analytics Text”)
Chollet, F. and Allaire, J. J. (2018). D eep Learning with R
(https://clio.columbia.edu/catalog/13262520) . Manning Publications Company. ISBN-13: 9781617295546 (Referred to as “Deep Learning Text”). Only available in hard copy form in Butler Library. Not a required reading.
Gorakala, S. K., & Usuelli, M. (2015). Building a Recommendation System with R. Packt Publishing. Retrieved from: h ttps://clio.columbia.edu/catalog/13674495 (https://clio.columbia.edu/catalog/13674495) . ISBN-13: 9781783554492 (Referred to as“Recommendation Text”)
Hyndman, R. J., & Athanasopoulos, G. (2016). Forecasting: Principles and Practice. Heathmont: OTexts. Retrieved from: h ttps://otexts.org/fpp2/ (https://otexts.org/fpp2/) . ISBN-13: 9780987507112 (Referred to as “Forecasting Text”)
Silge, J., & Robinson, D. (2017). Text Mining with R: A Tidy Approach. Beijing: O’Reilly. Retrieved from: h ttps://www.tidytextmining.com (https://www.tidytextmining.com/) . ISBN-13: 9781491981658 (Referred to as “Text Mining Text”)
Resources
Columbia University Information Technology
Columbia University Information Technology (https://cuit.columbia.edu) (CUIT) provides
Columbia University students, faculty and staff with central computing and communications services. Students, faculty and staff may access U
downloads (https://columbiait.onthehub.com) .
Columbia University Library 应用分析框架和方法网课代修
niversity-provided and discounted software
Columbia’s extensive library system (https://library.columbia.edu/) ranks in the top five
academic libraries in the nation, with many of its services and resources available online.
SPS Academic Resources
The Office of Student Affairs (http://sps.columbia.edu/student-life-and-alumni-relations/academic- r esources) provides students with academic counseling and support services such as online tutoring and career coaching.
Course Requirements (Assignments) 应用分析框架和方法网课代修
Assignments (20%)
Assignment 1: Clustering (5%)
Use clustering and dimension reduction techniques to determine underlying structure of the data.
Demonstrate application of methods for clustering and dimension reduction. Performance is assessed by correct responses to multiple choice and fill in the blank questions.
Assignment 2: Text Mining (5%)
Parse textual data to conduct predictive modelling.
Apply skills for text mining. Performance is assessed by correct responses to multiple choice and fill in the blank questions.
Assignment 3: Association Rules and Recommender Systems (5%)
Use association rules and recommender systems to make product recommendations.
Apply skills in application of association rules and recommender systems. Performance is assessed by correct responses to multiple choice and fill in the blank questions.
Assignment 4: Time Series Analysis (5%) 应用分析框架和方法网课代修
Construct a time series forecasting model and interpret results.
Construct a time series forecasting model and interpret results. Performance is assessed by correct responses to multiple choice and fill in the blank questions.
Project (35%) 应用分析框架和方法网课代修
A key feature of this class is applying what you have learnt in the curriculum to a real-world dataset. The project requires you to work in a group to perform all of the steps of a typical data analysis project over the course of the semester. Your report is expected to be independently developed by your team. You are free to select a problem, relevant data, and the types of analyses that you will perform. We encourage you to apply your own judgments in deciding how to frame the problems, structure the analysis, and select appropriate methods. This project should be something more than repeating the homework assignments on a different data set; you should aim to make a comprehensive and compelling analysis of an important problem in a domain or application of your choice. The project contains three graded components
Project Proposal (10%)
Identify research problem, find data, and prepare data for analysis.
Final Report (20%)
A comprehensive report of analysis, findings, and recommendations.
Presentation (5%)
A group presentation highlighting the research problem, analysis conducted and conclusions.
Class Participation and Engagement (0%)
The weekly class session is our opportunity to review the week’s material, work through applied exercises, address your questions, and – just as important – get to know each other. It’s therefore important to set aside enough time and space (both physical and mental) for our collective task. To earn full participation points during each week’s class session, you will be asked to do all of the following:
Arrive on time
Remain in the entire session throughout (excluding any breaks)
(Online sections) Use your webcam throughout (excluding any breaks) Take part in chats and polls
Contribute meaningfully in class discussions and breakout sessions.
Respond to instructor questions and requests to share opinions, key takeaways, experiences etc.
Final Exam (45%)
Performance is assessed by responses to a set of short answer questions.
Evaluation / Grading 应用分析框架和方法网课代修
The final grade will be calculated as described below:
GRADE CALCULATION
| ASSIGNMENT | WEIGHT | |
| Assignments | 20% | |
| Project | 35% | |
| Class Participation and Engagement | 0% | |
| Final Exam | 45% | |
|
FINAL GRADING SCALE |
||
| GRADE PERCENTAGE |
A+ 98–100 %
A 93–97.9 %
A- 90–92.9 %
B+ 87–89.9%
B 83–86.9 %
B- 80–82.9 % 应用分析框架和方法网课代修
C+ 77–79.9 %
C 73–76.9 %
C- 70–72.9 %
D 60–69.9 %
F 59.9 % and below
Course Policies
Participation and Attendance
You are expected to complete all reading and assignments, and attend all class sessions. If you miss a class for any reason, you must provide a written explanation, and in advance if possible. Each absence from class will usually result in a one-grade level deduction.
Late Work
No credit is granted to any written assignment submitted after the due date. Late assignments must receive prior permission from me, and a penalty will be assessed. All written assignments must be submitted to the course website; email submissions or re-submissions are not accepted. 应用分析框架和方法网课代修
Citation & Submission
All written assignments must use APA citation, cite sources, and be submitted to the course website (not via email).
School Policies 应用分析框架和方法网课代修
Copyright Policy
Please note—Due to copyright restrictions, online access to this material is limited to instructors and students currently registered for this course. Please be advised that by clicking the link to the electronic materials in this course, you have read and accept the following:
The copyright law of the United States (Title 17, United States Code) governs the making of photocopies or other reproductions of copyrighted materials. Under certain conditions specified in the law, libraries and archives are authorized to furnish a photocopy or other reproduction. One of these specified conditions is that the photocopy or reproduction is not to be “used for any purpose other than private study, scholarship, or research.” If a user makes a request for, or later uses, a photocopy or reproduction for purposes in excess of “fair use,” that user may be liable for copyright infringement.
Academic Integrity
0Columbia University expects its students to act with honesty and propriety at all times and to respect the rights of others. It is fundamental University policy that academic dishonesty in any guise or personal conduct of any sort that disrupts the life of the University or denigrates or endangers
members of the University community is unacceptable and will be dealt with severely. It is essential to the academic integrity and vitality of this community that individuals do their own work and properly acknowledge the circumstances, ideas, sources, and assistance upon which that work is based. Academic honesty in class assignments and exams is expected of all students at all times. 应用分析框架和方法网课代修
SPS holds each member of its community responsible for understanding and abiding by the S PS
Academic Integrity and Community Standards (http://sps.columbia.edu/student-life-and-alumni-
relations/academic-integrity-and-community-standards) . You are required to read these standards within the first few days of class. Ignorance of the School’s policy concerning academic dishonesty shall not be a defense in any disciplinary proceedings.
Accessibility
Columbia is committed to providing equal access to qualified students with documented disabilities. A student’s disability status and reasonable accommodations are individually determined based upon disability documentation and related information gathered through the intake process. For
more information regarding this service, please visit the U
(https://health.columbia.edu/content/disability-services) .
Class Recordings
niversity‘s Health Services website
All or portions of the class may be recorded at the discretion of the Instructor to support your
learning. At any point, the Instructor has the right to discontinue the recording if it is deemed to be obstructive to the learning process. 应用分析框架和方法网课代修
If the recording is posted, it is considered confidential and it is not acceptable to share the recording outside the purview of the faculty member and registered class.
Course Schedule
| Module / Week |
Topic |
Readings |
Activities / Assignments for this Module |
|
1 |
Introduction | No readings | Session
Discussion: Getting Acquainted |
| 2 |
Basic Clustering |
Analytics Text (https://clio.colum bia.edu/catalog/11 4 85551) , Ch.
11 (p. 299-338) |
Resources Session Lecture Notes
Discussion: Basic Clustering |
| 3 | Advanced Clustering | Analytics Text (https://clio.colum bia.edu/catalog/11 4 85551) , Ch.
11 (p. 299-338) |
Resources Session Lecture Notes
Discussion: Advanced Clustering Assignment 1 |
| Module / Week |
Topic |
Readings |
Activities / Assignments for this Module |
| 4 |
Dimension Reduction |
Analytics Text (https://clio.colum bia.edu/catalog/11 4 85551) , Ch. 8 (p.
195-223) |
Resources Session Lecture Notes
Discussion: Dimension Reduction |
| 5 | Text Mining | Text Mining Text | Resources |
|
(https://www.tidyte xtmining.com/) , Ch. 1-5 应用分析框架和方法网课代修 |
Session Lecture Notes
Discussion: Text Mining, Part 1 |
||
| 6 | Text Mining | Text Mining Text | Resources |
|
(https://www.tidyte xtmining.com/) , Ch. 6-9 |
Session Lecture Notes
Discussion: Text Mining, Part 2 Assignment 2 |
||
| Project Proposal | |||
| Discussion: Project Proposal | |||
| 7 | Association Rules | Analytics Text (https://clio.colum bia.edu/catalog/11 4 85551) , Ch. 12
(p. 339-361) 应用分析框架和方法网课代修 |
Resources Session Lecture Notes
Discussion: Association Rules |
| Spring Recess | NO CLASS | ||
| 8 |
Recommender System |
Recommendatio n Text (https://clio.colum bia.edu/catalog/13 6 74495) , Ch. 1-4. | Resources Session Lecture Notes
Discussion: Recommender System Assignment 3 |
| 9 | Time Series | Forecasting Text | Resources |
|
(https://otexts.org/ fpp2/) , Ch. 1, 3, 5, 6, 7, and 8 |
Session Lecture Notes
Discussion: Time Series Assignment 4 |
| Module / Week |
Topic |
Readings 应用分析框架和方法网课代修 |
Activities / Assignments for this Module |
| 10 | Neural Networks | Deep Learning T ext
(https://clio.colum bia.edu/catalog/13 2 62520) , Ch 1-4. (recommended, not required) |
Resources Session Lecture Notes
Discussion: Neural Networks |
| 11 |
Spatial Analysis |
Spatial Text (https://clio.colum bia.edu/catalog/79 3 5086) , Ch. 1-4,
10 |
Resources Session Lecture Notes
Discussion: Spatial Analysis |
| 12 | Final Exam | No readings | Session
Final Exam |
| 13 |
Presentation on Final Project |
No readings | Session
Project Presentation Project Final Report |
更多代写：计算机网课作业代写 金融网课代考 历史Essay的代写 历史Essay的写作思路 理科论文代写 数据结构和算法代考
