DVC and Git For Data Science

DVC and Git For Data Science, available at $54.99, has an average rating of 3.75, with 55 lectures, based on 27 reviews, and has 276 subscribers.

You will learn about Learn Version Control and Why We Need it? Understand the Need for Data Version Control Git and Github For Data Science Project Master DVC For Data Science Project Explore DAGsHub Build Your Own Custom Version Control Tool (Git) From Scratch This course is ideal for individuals who are Anyone interested in Learning Git and DVC or Data Scientist curious about Data Version Control or Students It is particularly useful for Anyone interested in Learning Git and DVC or Data Scientist curious about Data Version Control or Students.

Enroll now: DVC and Git For Data Science

Summary

Title: DVC and Git For Data Science

Price: $54.99

Average Rating: 3.75

Number of Lectures: 55

Number of Published Lectures: 54

Number of Curriculum Items: 55

Number of Published Curriculum Objects: 54

Original Price: $54.99

Quality Status: approved

Status: Live

What You Will Learn

Learn Version Control and Why We Need it?

Understand the Need for Data Version Control

Git and Github For Data Science Project

Master DVC For Data Science Project

Explore DAGsHub

Build Your Own Custom Version Control Tool (Git) From Scratch

Who Should Attend

Anyone interested in Learning Git and DVC

Data Scientist curious about Data Version Control

Students

Target Audiences

Anyone interested in Learning Git and DVC

Data Scientist curious about Data Version Control

Students

Our modern world runs on software and data, with Git – a version control tool we track and manage the different changes and versions of our software. Git is very useful in every programmer’s work. It is a must-have tool for working in any software-related field, that includes data science to machine learning.

What about the data and the ML models we build? How do we track and manage them?

How do data scientist, machine learning engineers and AI developers track and manage the data and models they spend hours and days building?

In this course we will explore Git and DVC – two essential version control tools that every data scientist, ML engineer and AI developer needs when working on their data science project.

This is a very new field hence there are not a lot of materials on using git and dvc for data science projects. The goal of this exciting and unscripted course is to introduce you to Git and DVC for data science.

We will also explore Data Version control, how to track your models and your datasets using DVC and Git.

By the end of the course you will have a comprehensive overview of the fundamentals of Git and DVC and how to use these tools in  managing and tracking your ML models and dataset for the entire machine learning project life cycle.

This course is unscripted,fun and exciting but at the same time we will dive deep into DVC and Git For Data Science.

Specifically you will learn

Git Essentials

How Git works

Git Branching for Data Science Project

Build our own custom Version Control Tools from scratch

Data Version Control – The What,Why and How

DVC Essentials

How to track and version your ML Models

DVC pipelines

How to use DAGsHub and GitHub

Label Studio

Best practices in using Git and DVC

Machine Learning Experiment Tracking

etc

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

Instructors

Jesse E. Agbe
Developer

Rating Distribution

1 stars: 3 votes

2 stars: 2 votes

3 stars: 2 votes

4 stars: 6 votes

5 stars: 14 votes

Frequently Asked Questions

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