Course Introduction
Git and GitHub for Data Analysts Training Course provides practical skills for managing analytical projects with professional version-control and collaboration practices. The course focuses on how analysts can use Git to track changes to Python scripts, Jupyter notebooks, SQL files, dashboards, documentation, configuration files, and other analytical assets while using GitHub to host repositories, collaborate with colleagues, review changes, and maintain organized project histories. Git records project history through commits and supports parallel development through branches, making it suitable for structured analytical workflows.
The training is designed around the real challenges faced by data analysts when analytical projects evolve over time. Participants learn how to initialize repositories, clone projects, stage changes, create meaningful commits, inspect differences, restore previous versions, create branches, merge work, and synchronize local projects with remote repositories. Rather than treating Git as a software-development-only technology, the course demonstrates how version control can improve transparency, reproducibility, auditability, and collaboration across business analytics, research, reporting, and data science environments.
A major focus is the effective management of data-analysis projects containing notebooks, scripts, datasets, visualizations, SQL queries, reports, and supporting documentation. Participants learn how to structure repositories logically, create useful README files, establish .gitignore rules, distinguish source files from generated outputs, and avoid common repository-management problems. GitHub repositories can contain project files together with their revision history and provide tools for discussing and managing collaborative work.
The course also develops collaborative GitHub practices that are particularly valuable for analysts working in teams. Participants learn how to use branches for isolated analytical changes, push work to remote repositories, create pull requests, review proposed changes, respond to feedback, and merge approved work. GitHub's documented workflow supports creating a branch, committing changes, opening a pull request, reviewing proposed changes, and merging them into another branch.
Special attention is given to notebooks and data-specific challenges such as large files, generated outputs, sensitive information, credentials, environment dependencies, and changing source datasets. Participants explore practical approaches for making analytical repositories easier to reproduce and maintain, including dependency documentation, project metadata, data references, repository conventions, and clear commit histories. The training also introduces GitHub Codespaces as an optional cloud-based development environment where analysts can work with repositories, Git commands, terminals, and source-control functionality without depending entirely on a local development setup.
By the end of the course, participants will be able to manage complete analytical projects using Git and GitHub with greater consistency and confidence. They will understand local and remote repositories, commits, branches, merges, pull requests, conflicts, repository organization, collaboration practices, and reproducibility techniques. The final practical project brings these capabilities together by requiring participants to place a realistic data-analysis workflow under version control, collaborate through branches and pull requests, resolve changes, document the project, and prepare the repository for professional use.
Duration
5 days
Who Should Attend
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Data analysts who want to adopt professional version-control practices for analytical projects.
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Business analysts working with spreadsheets, SQL queries, scripts, dashboards, and analytical reports.
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Data scientists who need stronger Git and GitHub skills for collaborative analytics workflows.
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Python analysts managing scripts, Jupyter notebooks, analytical models, and data-processing workflows.
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SQL analysts who need to version-control queries, database scripts, documentation, and analytical assets.
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Research analysts developing reproducible computational projects and research analysis repositories.
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BI professionals collaborating on analytical code, reporting logic, data transformations, and documentation.
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Data engineers and analytics engineers who collaborate closely with analysts on shared data projects.
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Machine learning practitioners managing exploratory analysis, feature engineering, experiments, and model-related files.
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Team leaders responsible for establishing collaborative version-control standards for analytics teams.
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Professionals transitioning from manual file sharing toward structured Git-based analytical workflows.
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Technical project members who need practical GitHub skills for repository collaboration and analytical project management.
Course Objectives
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Understand Git version control concepts and apply them effectively to analytical projects containing notebooks, scripts, SQL, documentation, and supporting files.
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Create and manage Git repositories while developing practical habits for tracking analytical changes, maintaining history, and organizing project assets.
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Use staging, commits, status checks, logs, diffs, and restoration techniques to maintain clear and useful histories of analytical work.
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Create, switch, synchronize, merge, and manage branches so analysts can develop independent changes without disrupting stable project versions.
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Connect local Git repositories with GitHub repositories and confidently synchronize analytical work between local environments and remote repositories.
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Apply GitHub pull requests, reviews, discussions, and collaborative workflows to improve analytical quality and team-based project development.
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Organize data-analysis repositories using effective README files, directory structures,
.gitignoreconfigurations, documentation, and dependency-management practices. -
Identify and resolve common Git problems including merge conflicts, divergent branches, accidental changes, incorrect commits, and synchronization errors.
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Apply version-control practices that improve reproducibility, transparency, auditability, collaboration, and long-term maintainability of analytical projects.
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Complete an end-to-end Git and GitHub workflow for a realistic data-analysis project, including branching, commits, pull requests, review, merging, and documentation.
Comprehensive Course Outline
Module 1: Git and GitHub Fundamentals for Data Analysts
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Understanding version control, distributed repositories, commits, branches, working directories, staging areas, and how these concepts apply to analytical projects.
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Exploring the relationship between Git and GitHub and understanding how local version control connects with cloud-hosted repositories and collaborative workflows.
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Identifying analytical assets that benefit from version control, including Python scripts, SQL queries, notebooks, documentation, configuration files, and project metadata.
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Installing and configuring Git while establishing user identity, default settings, editors, credential methods, and practical conventions for professional analytical work.
Module 2: Git Repository and Analytical Project Management
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Creating new Git repositories and understanding the relationship between the working directory, staging area, local repository, and remote repository.
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Cloning existing analytical repositories and navigating repository structures while identifying tracked, untracked, modified, and ignored files.
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Using fundamental commands such as
git status,git add,git commit,git log, andgit diffto monitor and record analytical project changes. -
Developing logical repository structures for notebooks, scripts, SQL, documentation, configuration, source-data references, outputs, and supporting analytical resources.
Module 3: Commits, History, and Change Management
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Creating focused, meaningful commits that describe analytical changes clearly and provide useful historical checkpoints for future review or troubleshooting.
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Inspecting commit histories, comparing revisions, examining individual changes, and identifying when and why particular analytical project modifications were introduced.
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Correcting common mistakes through appropriate techniques for amending commits, restoring files, reverting changes, and recovering useful project states.
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Developing commit-message conventions that help analysts communicate changes to colleagues and make analytical project histories easier to understand.
Module 4: Branching and Merging for Analytics Workflows
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Understanding Git branches as independent lines of development and applying branching strategies to analytical enhancements, experiments, fixes, and reporting changes.
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Creating, switching, renaming, updating, and deleting branches while maintaining a clean and understandable analytical project workflow.
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Merging branches safely and understanding fast-forward merges, merge commits, conflicts, and the importance of synchronizing work before integration.
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Applying practical branch-management conventions for teams working simultaneously on notebooks, analysis scripts, SQL transformations, and reporting assets.
Module 5: GitHub Repositories and Remote Collaboration
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Creating GitHub repositories and connecting them to local analytical projects for centralized collaboration, backup, sharing, and project history management.
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Using
git push,git pull,git fetch, and related synchronization workflows to keep local analytical projects aligned with their remote repositories. -
Understanding repository visibility, access permissions, collaboration settings, project documentation, and practical considerations for professional analytical repositories.
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Exploring GitHub's browser-based capabilities and desktop workflows alongside command-line Git for different analyst experience levels and project requirements.
Module 6: Pull Requests, Code Review, and Team Analytics
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Creating pull requests to propose analytical changes and using descriptive titles, summaries, context, and supporting information to facilitate effective review.
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Reviewing proposed changes in notebooks, scripts, SQL, documentation, and configuration files while identifying analytical, technical, and documentation issues.
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Responding to review comments, updating branches, resolving requested changes, and maintaining a clear conversation around analytical modifications.
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Applying collaborative GitHub workflows that encourage peer review, controlled integration, shared ownership, and transparent decision-making for analytical projects.
Module 7: Git for Jupyter Notebooks, Data, and Reproducible Analysis
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Managing Jupyter notebooks under Git while addressing challenges associated with cell outputs, metadata, execution order, generated content, and large notebook files.
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Designing repository structures that separate reusable analytical code, notebooks, configuration, documentation, source-data references, and generated outputs.
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Creating
.gitignorerules to prevent temporary files, local environments, credentials, generated artifacts, caches, and other inappropriate content from entering repositories. -
Improving analytical reproducibility by documenting data sources, package dependencies, execution requirements, assumptions, and instructions needed to recreate project workflows.
Module 8: Merge Conflicts, Troubleshooting, and Git Recovery
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Understanding why merge conflicts occur and developing systematic methods for identifying conflicting files, examining changes, resolving differences, and completing merges.
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Troubleshooting common synchronization problems involving remote changes, rejected pushes, divergent branches, uncommitted work, and incorrect branch selection.
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Using Git history and recovery techniques to investigate accidental changes, misplaced commits, deleted files, and other common analytical project mistakes.
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Developing safe problem-solving habits that protect analytical work while maintaining repository integrity and preserving useful project history.
Module 9: Professional GitHub Practices for Data Analysts
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Creating professional analytical repositories with effective README files, project descriptions, documentation, folder structures, contribution guidance, and reproducibility instructions.
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Managing sensitive information responsibly by identifying credentials, confidential datasets, personal information, and other content that should not be committed to repositories.
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Establishing repository conventions for branches, commits, pull requests, reviews, issues, documentation, analytical outputs, and project maintenance.
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Exploring GitHub Codespaces as a browser-based development environment that provides integrated source control, terminals, editors, and repository workflows for supported projects.
Module 10: End-to-End Git and GitHub Analytics Project
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Creating a complete analytical repository containing source files, notebooks, SQL queries, documentation, configuration information, and reproducibility instructions.
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Implementing a realistic feature-development workflow using branches, focused commits, remote synchronization, pull requests, peer review, and controlled merging.
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Resolving intentional merge conflicts and troubleshooting synchronization issues while preserving analytical changes and maintaining a clean project history.
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Presenting the completed GitHub repository and demonstrating how version control improves collaboration, transparency, reproducibility, auditability, and long-term analytical project management.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
