Data Science in the Financial Sector

Please respond to the lecture on Data Science in the Financial Sector (Lecture 8: Matthew Nagowski and Eric Hanson) answering the base questions asked in the course information. (50 points of the 80 C+R points in the rubric).
prepare a short (~2-3 pages, 12 point single space) report that addresses as many of the following questions as are relevant:
• Describe the market sector or sub-space covered in this lecture.
• What data science related skills and technologies are commonly used in this sector?
• How are data and computing related methods used in typical workflows in this sector? Illustrate with an example.
• What are the data science related challenges one might encounter in this domain?
• What do you find interesting about the nature of data science opportunities in this
domain?
In addition,
(i) According to the lecture, what are the types of technical and business questions that are considered to evaluate the validity of a model for a banking application? (10 pts of the 80 C+R points in the rubric)
(ii) Describe how the clustering model meets the business purposes of M&T Bank, and what characteristics of the bank's portfolio were being captured. (10 pts of the 80 C+R points in the rubric)
(iii) Also, answer the following multiple-choice questions: You can list the question number and the letter corresponding to the correct choice as Answer in your report, (2x5 = 10 pts of the 80 C+R points in the rubric)
Q1: Based on the lecture, there are risks in using data modeling and analytics in commercial banking, select ALL the mentioned potential risks of data model implementations.
1. Model may be poorly built due to bad design choices and inappropriate business assumptions
2. There could be “bad” data that does not represent the entire population, or has poor data quality
3. Errors can occur in model implementation
4. Model could be misused.
5. There is inherent risk in using models since they do not exactly reflect reality.
A. 1,2,3,4 B. 1,2,4,5 C. 1,2,4,5 D. All of them
Q2: Based on the lecture, there are certain key technical skillsets used in data modeling and analytics at commercial banks such as M&T Bank. Which of the following skillsets is commonly used for data reporting and management.
A. Excel, LaTeX
B. Tableau, Qlikview
C. SQL, SSRS
D. SAS, Stata

Q3: The lecturers introduced several projects in M&T bank, with different data analytical models applied to different projects. Which model was used in the Reduce Duplicate Alerts in Operational Processes Project.
A. Generalized Linear Model
B. Non-Linear Regression Model
C. Geometric Lagged Regression Model
D. Random Forest Machine Learning Model

Q4. Based on the lecture, Clustering models are used in the bank to analyze Commercial Deposits. Which of the following about Clustering models is NOT true.
A. Clustering is a method by which an algorithm can group similar objects together
B. Clustering is useful in exploratory analysis in order to find patterns in data that are not know a priori
C. Clustering can be used to estimate relationships between a dependent data variable and some other independent data variables
D. With slight modification, clustering algorithms can handle a time component
Q5. Based on the lecture, Dynamic Time Warp (DTW) algorithm was introduced as a clustering analysis model for grouping time series patterns. Which of the following is NOT true about the DTW algorithm.
A. An advantage of DTW is that it does not need any kind of distance calculation when matching between patterns
B. DTW finds optimal match between two time series patterns by stretching and compressing sections of each
C. Clustering can be computationally heavy, so often in practice, choosing a section of time period instead of the whole data history can be good enough
D. Distance calculation is necessary in clustering analysis