DVC and Git For Data Science

Course Curriculum

Chapter 1: Module 01 – Introduction

Lecture 1: Introduction

Lecture 2: Course Guide

Lecture 3: Setting Up and Installing Packages & Course Materials

Lecture 4: What is Version Control?

Lecture 5: Importance of Version Control

Lecture 6: Data Version Control – The What and Why?

Lecture 7: Version Control Tools

Lecture 8: Project Structuring Using Cookiecutter

Chapter 2: Module 02 – Git Essentials For Data Science

Lecture 1: What is Git?

Lecture 2: Git Workflow – Theory

Lecture 3: Configuring Git

Lecture 4: Github Platform

Lecture 5: Configuring SSH For GitHub

Lecture 6: Git Essentials – Creating a Repo

Lecture 7: Git Workflow – Practical

Lecture 8: Git Essentials – Commit & Best Practices

Lecture 9: Git Essentials – Undoing Uncommitted Changes

Lecture 10: Git Essentials – Exploring Git Commit on Github

Lecture 11: Git Essentials – Git Logs

Lecture 12: Git Essentials – Branching For ML Model and Data Science

Lecture 13: Git – Tricks & Tips

Lecture 14: GitHub – Advanced Search

Lecture 15: GitHub-Dorks

Chapter 3: Module 03 – Building A CLI for Version Control From Scratch

Lecture 1: Intro and Designing of CLI for Version Control

Lecture 2: Building Version Control CLI – Status Functionality

Lecture 3: Building Version Control CLI – Push Functionality

Lecture 4: Building Version Control CLI – Remove,Restore,Clone

Chapter 4: Module 03 – DVC Essentials

Lecture 1: What is DVC Tool?

Lecture 2: DVC Features and Benefits

Lecture 3: DVC – The 3 Areas of DVC

Lecture 4: Advantages of Data Version Control

Lecture 5: DVC vs Git Commands

Lecture 6: DVC Essentials – Workflow

Lecture 7: DVC Essentials – Pushing Your Data to GDrive

Lecture 8: DVC Essentials – Pushing Data to Local Storage

Chapter 5: Module 04 – DAGsHub

Lecture 1: DAGsHub Platform Walkthrough

Lecture 2: DAGsHub – Creating a Repo

Lecture 3: DAGsHub – Searching on the Platform

Lecture 4: DAGsHub – Adding topics

Lecture 5: DAGsHub – Label Studio

Chapter 6: Module 04 – End to End Data Science Project with DVC and Git

Lecture 1: Intro & Setting up Workspace

Lecture 2: Data Versioning and Pushing Data to Dagshub

Lecture 3: Data Preparation & Model Building

Lecture 4: Git Branching for ML Models

Lecture 5: Model Storage on DagsHub with Git & DVC

Lecture 6: Saving New ML Models to a New Branch

Lecture 7: ML Experiment Tracking with DagsHub

Chapter 7: DVC Pipelines – Makefiles for Data Science Project

Lecture 1: Introduction & Manually Running ML Pipelines

Lecture 2: DVC Pipelines – DVC Run Interactive Experiment

Lecture 3: DVC Pipelines – DVC Run

Lecture 4: DVC Pipelines – DVC Metrics

Lecture 5: DVC Pipelines – DVC Repro

Lecture 6: DVC Pipelines – Pushing Data and Code to DagsHub

Lecture 7: DVC Pipelines – Fixing Error with Push and Pull