Data Analytics for Finance

Hands-on data analytics for finance using Python, covering forecasting, prediction, reporting, and analysis through progressively complex financial applications.

no offering record for this termCredits unavailable
Recorded instructors · Spring 2027 No instructors listed

Summary

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Recent recorded grades — Spring 2022: 3.63 GPA, 69.6% A/AB (n=23 letter grades).

What you’ll be able to do

  • Understand the fundamentals of programming and how those fundamentals are implemented in Python

  • Download, organize, and manipulate data to prepare them for Finance applications

  • Visualize and organize Financial Data for the purpose of creating effective presentations

  • Apply these programming skills in the context of standard financial applications such as asset pricing model estimation, Monte Carlo simulations for capital budgeting purposes, portfolio optimization, and financial analysis and forecasting

Source: UW Guide

Grade history

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average GPA
letter grades
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Prerequisites

Course map

ECON/​FINANCE 300

FINANCE 310

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Professors

Spring 2027

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Calendar & sections

Spring 2027

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Student experience

the class

Recent recorded grades — Spring 2022: 3.63 GPA, 69.6% A/AB (n=23 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Advanced analytics methods for forecasting and prediction
  • Python programming for finance
  • Asset pricing, Monte Carlo simulation, portfolio optimization, and financial analysis

Skills

  • Programming fundamentals in Python
  • Data preparation for finance applications
  • Financial data visualization and presentation
  • Asset pricing, Monte Carlo simulation, portfolio optimization, and forecasting

Grades

Latest available · Spring 2022— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

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AB
B
BC
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Not enough comparable courses for Spring 2027 in UW–Madison.

Sources & history

UW–Madison

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Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Model & dataset provenance
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  "task_version": "17",
  "output_id": "475946a01492cb7a095b02e78686f3080c1b3a195b0ec077cdef962511f4e8f4",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}