https://github.com/l-gre/data_analytics_for_finance
Comprehensive course materials for the Data Analytics for Finance - Master Programme, covering data manipulation, statistical analysis, visualisation, automation, and real-world case studies using industry-standard tools.
https://github.com/l-gre/data_analytics_for_finance
automation data-cleaning data-manipulation data-visualization excel hypothesis-testing industry-applications matplotlib numpy pandas python real-world-case-studies regression-analysis seaborn sql statistical-analysis tableau workflow-automation
Last synced: 3 months ago
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Comprehensive course materials for the Data Analytics for Finance - Master Programme, covering data manipulation, statistical analysis, visualisation, automation, and real-world case studies using industry-standard tools.
- Host: GitHub
- URL: https://github.com/l-gre/data_analytics_for_finance
- Owner: L-Gre
- Created: 2024-12-23T21:08:49.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-01-12T23:49:10.000Z (5 months ago)
- Last Synced: 2025-02-20T06:19:03.516Z (3 months ago)
- Topics: automation, data-cleaning, data-manipulation, data-visualization, excel, hypothesis-testing, industry-applications, matplotlib, numpy, pandas, python, real-world-case-studies, regression-analysis, seaborn, sql, statistical-analysis, tableau, workflow-automation
- Language: Jupyter Notebook
- Homepage:
- Size: 1.01 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Data Analytics for Finance - Master Programme
This repository contains the course materials for the **Data Analytics for Finance - Master Programme**. The programme is designed to provide a structured approach to learning data analytics with a focus on practical finance skills and industry-standard tools.
## Course Modules
### 1. Data Manipulation
- Cleaning and transforming datasets
- Working with Python libraries like `pandas` and `NumPy`
- SQL queries for data extraction and transformation
### 2. Statistical Analysis
- Key concepts: distributions, hypothesis testing, regression analysis
- Practical implementation of statistical methods
- Application to real-world datasets
### 3. Visualisation
- Creating clear and effective data visualisations
- Tools: `matplotlib`, `seaborn`, and Tableau
- Emphasis on storytelling with data
### 4. Tools and Automation
- Automating data workflows with Python scripting
- Advanced Excel (including macros)
- Efficient data handling with SQL
### 5. Applied Case Studies
- Real-world examples from finance, marketing, and operations
- Simulating common industry scenarios
- End-to-end analysis projects to reinforce concepts
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This course assumes a basic understanding of programming and is aimed at learners looking to build proficiency in data analytics through practical, hands-on learning.