Financial Data Analysis and Practice


Prof. Luke Stein

View the Project on GitHub lukestein-classes/fdap


This course schedule will change during the semester. Ad hoc topic changes (unannounced) may be based on current events or class pace and interest. Announcement of any meeting changes will be distributed via Discord; please ensure that you are monitoring the #announcements channel there.

We will also have several combined meetings where multiple sections will meet simultaneously (typically to accommodate a guest). These will of course be announced in advance, but please aim to be available during all course section times (8:15am–2:30pm). Currently announced combined meetings are:

  1. Monday 10/24: Meeting 8:15–10:00am or 12:45–2:30pm (Professional ethics session with Glenn Migliozzi)
  2. Monday 11/07: Meeting 8:15–10:00am or 12:45–2:30pm (Professional ethics session with Glenn Migliozzi; note date change)
Meeting Date Topics/Modules Deliverable
  9/5 No class (Labor Day)  
1 9/7 Course introduction  
2 9/12 S1 HW1
3 9/14 P1 Peer review
4 9/19 cont. HW2
5 9/21 cont. Peer review
6 9/26 A1 HW3
7 9/28 P2 Peer review
8 10/3 A2 HW4
9 10/5 P3, D1 Peer review
  10/6   Excel CC 0
  10/10 No class (Indigenous Peoples’ Day) Excel CC 1
10 10/11 Tuesday class
S2, P4
11 10/12 A3, D2, S3  
  10/15   Excel CC 2 (opt.)
12 10/17 P5 HW5
13 10/19 S4, D3 Peer review
14 10/24 Meeting 8:15–10:00am or 12:45–2:30pm
Midterm project
15 10/26 S5  
16 10/31 S6, A5 HW6
17 11/2 A6 Peer review
18 11/7 Meeting 8:15–10:00am or 12:45–2:30pm
Ethics report
19 11/9 P6  
20 11/14 A7 HW7
21 11/16 cont. Peer review
22 11/21 D4  
  11/23 No class (Thanksgiving break)  
23 11/28 A8 HW8
24 11/30 A9  
25 12/5 Group presentations Final project
26 12/7 Group presentations
(Final exam distributed)
Market report
  12/12 No class (Final exam due) Final exam



Module Topic Resources
P1 Introduction to Python TP 1–3, 8, 10–12
WTP 1–7
CfE Getting Started 1, Coding 3, Getting Started 2.1–2.8
PESDA 2, 4, 10
PDA 2.3, 3.1
P2 Control flow and data structures TP 5–7
WTP 8–14
CfE Getting Started 2.9–2.16
PDA 3.2
P3 Data manipulation PDSH 3
PESDA 8–9, 16
CfE Data 1, Data 2
PDA 5, 7–8, 10-12
P4 Visualization PDSH 4.14
Seaborn Tutorial API overview and Plotting functions
P5 Regression and statistics PESDA 21
P6 Numerical Python PDSH 2
PESDA 3, 11, 19

Statistics and Inference

Module Topic Resources
S1 Introduction to data IMS 1–3
S2 Exploratory data analysis (EDA) IMS 4–6
S3 Regression modeling IMS 7–10
S4 Foundations of inference IMS 11–15
S5 Statistical inference IMS 16–23
S6 Inferential modeling IMS 24–27

Financial Data

Module Topic Resources
D1 pandas-datareader datareader documentation
D2 Bloomberg TBA
D3 WRDS WRDS Data documentation, Classroom, Research
WRDS Python Data Access Library
D4 Alternative data TBA

Microsoft Excel

Excel will be used throughout the course, with coverage not divided into explicit modules.

Financial Applications

Module Topic Resources
A1 Monte Carlo simulation TBA
A2 Fixed income TBA
A3 Equity returns TBA
A4 Professional ethics TBA
A5 Foreign exchange TBA
A6 Factor models TBA
A7 Capital budgeting TBA
A8 Derivatives TBA
A9 Equity portfolios TBA