{"id":23706973,"url":"https://github.com/l-gre/data_analytics_for_finance","last_synced_at":"2026-04-15T14:34:31.409Z","repository":{"id":269482794,"uuid":"907550769","full_name":"L-Gre/Data_Analytics_for_Finance","owner":"L-Gre","description":"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.","archived":false,"fork":false,"pushed_at":"2025-01-12T23:49:10.000Z","size":1054,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-20T06:19:03.516Z","etag":null,"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"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/L-Gre.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-12-23T21:08:49.000Z","updated_at":"2025-01-12T23:49:13.000Z","dependencies_parsed_at":"2024-12-23T22:25:14.392Z","dependency_job_id":"25a8d8cf-39b0-4ecd-b4b7-2d662f2c6f8d","html_url":"https://github.com/L-Gre/Data_Analytics_for_Finance","commit_stats":null,"previous_names":["l-gre/data_analytics_for_finance"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/L-Gre%2FData_Analytics_for_Finance","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/L-Gre%2FData_Analytics_for_Finance/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/L-Gre%2FData_Analytics_for_Finance/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/L-Gre%2FData_Analytics_for_Finance/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/L-Gre","download_url":"https://codeload.github.com/L-Gre/Data_Analytics_for_Finance/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":239786252,"owners_count":19696772,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["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"],"created_at":"2024-12-30T16:01:52.273Z","updated_at":"2026-02-03T04:30:19.928Z","avatar_url":"https://github.com/L-Gre.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Data Analytics for Finance - Master Programme\n\nThis 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.\n\n## Course Modules\n\n### 1. Data Manipulation\n- Cleaning and transforming datasets\n- Working with Python libraries like `pandas` and `NumPy`\n- SQL queries for data extraction and transformation\n\n### 2. Statistical Analysis\n- Key concepts: distributions, hypothesis testing, regression analysis\n- Practical implementation of statistical methods\n- Application to real-world datasets\n\n### 3. Visualisation\n- Creating clear and effective data visualisations\n- Tools: `matplotlib`, `seaborn`, and Tableau\n- Emphasis on storytelling with data\n\n### 4. Tools and Automation\n- Automating data workflows with Python scripting\n- Advanced Excel (including macros)\n- Efficient data handling with SQL\n\n### 5. Applied Case Studies\n- Real-world examples from finance, marketing, and operations\n- Simulating common industry scenarios\n- End-to-end analysis projects to reinforce concepts\n\n---\n\nThis course assumes a basic understanding of programming and is aimed at learners looking to build proficiency in data analytics through practical, hands-on learning.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fl-gre%2Fdata_analytics_for_finance","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fl-gre%2Fdata_analytics_for_finance","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fl-gre%2Fdata_analytics_for_finance/lists"}