Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
9 Hour(s) 20 Minute(s)
Language
English
Taught by
Jesse E. Agbe
Certificate
- Available
- *The delivery and distribution of the certificate are subject to the policies and arrangements of the course provider.
Rating
Course Overview
Master the Basics of Git and Data Version Control (DVC) for Beginners
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 Content
- 7 section(s)
- 54 lecture(s)
- Section 1 Module 01 - Introduction
- Section 2 Module 02 - Git Essentials For Data Science
- Section 3 Module 03 - Building A CLI for Version Control From Scratch
- Section 4 Module 03 - DVC Essentials
- Section 5 Module 04 - DAGsHub
- Section 6 Module 04 - End to End Data Science Project with DVC and Git
- Section 7 DVC Pipelines - Makefiles for Data Science Project
What You’ll 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
Skills covered in this course
Reviews
-
DDanish Alam
He did not show the instructions for Windows only for linux, as if he assumed everyone knows Linux.. and its very very hard to comprehend the accent
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MMoumini KABORE
Nice path to understand versioning of code and data in data science. Congratulations to the trainer.
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EElias Herrero Jaraba
Horrible pronunciation!
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AAxel Miguel Vargas
This course is a very superficial view of the git and dvc tools, although the git part is at least a little acceptable, in general it leaves a lot to be done, the last section is very poorly designed without sharing the materials, it is more advisable to read the dvc documentation directly, and as for the editing, sometimes there was external noise or the pronunciation was not well understood, I hope the professor improves for his next courses
