1. Handbook
  2. Subjects
  3. Data Analysis for Finance

Data Analysis for Finance (FNCE90083)

Graduate courseworkPoints: 12.5On Campus (Parkville)

You’re viewing the 2025 Handbook:
Or view archived Handbooks

Overview

Availability
Semester 1
FeesLook up fees

This subject introduces students to principles and applications of data analysis for finance. Concepts covered include data collection, processing and management, relevant theory in statistics, econometrics and machine learning, programming in relevant languages and data presentation. Specific topics include data sourcing, processing and cleaning, summarizing and visualizing data; multiple regression, time-series models, panel data techniques and causal inference; machine learning and classification methods, model selection and assessing model performance, unsupervised learning and textual analysis.  Students will become proficient in relevant programming languages such as Python or R.

Intended learning outcomes

On successful completion of this subject students should be able to:

  • Develop proficiency with relevant software and programming languages for complex data analysis tasks with finance applications
  • Demonstrate the ability to source, process and clean complex data from a variety of sources
  • Understand the methods and principles of summarizing and visualizing data
  • Understand the relevant theoretical principles behind data analysis, including statistics, econometrics and machine learning
  • Develop skills in presenting data analysis for a variety of audiences in written form

Generic skills

  • Oral and written communication
  • Problem solving
  • Application of theory to practice
  • Team work
  • Critical thinking
  • Evaluation of data
  • Using Computer Programs
  • Statistical Reasoning

Last updated: 8 November 2024