Analytics Engineering Bootcamp

Analytics Engineering Bootcamp, available at $84.99, has an average rating of 4.62, with 150 lectures, 6 quizzes, based on 1367 reviews, and has 10920 subscribers.

You will learn about Learn all the skill sets that is required to become an Analytics Engineer In-depth understanding of data modelling techniques Ability to participate in architectural decision making and be able to create one Data modelling techniques using DBT Learn hands-on skills required to build a Data Warehouse from scratch Boost your resume with most in-demand Analytics Engineer skills Design & Implement a data warehouse Create Data Warehouse Architecture Design Conceptual, Logical & Physical Models Learn various modelling methodologies (Inmon, Kimball, Data Vault, OBT) Apply principles of dimensional data modeling in a hands-on Learn all the concepts and terms such as the OLTP, OLAP, Facts, Dimensions, Star Schema, Snowflake Schema This course is ideal for individuals who are Anyone who is interested in becoming an Analytics Engineer or Anyone who want's to understand more about data modelling and data transformation It is particularly useful for Anyone who is interested in becoming an Analytics Engineer or Anyone who want's to understand more about data modelling and data transformation.

Enroll now: Analytics Engineering Bootcamp

Summary

Title: Analytics Engineering Bootcamp

Price: $84.99

Average Rating: 4.62

Number of Lectures: 150

Number of Quizzes: 6

Number of Published Lectures: 150

Number of Published Quizzes: 6

Number of Curriculum Items: 156

Number of Published Curriculum Objects: 156

Original Price: £199.99

Quality Status: approved

Status: Live

What You Will Learn

Learn all the skill sets that is required to become an Analytics Engineer
In-depth understanding of data modelling techniques
Ability to participate in architectural decision making and be able to create one
Data modelling techniques using DBT
Learn hands-on skills required to build a Data Warehouse from scratch
Boost your resume with most in-demand Analytics Engineer skills
Design & Implement a data warehouse
Create Data Warehouse Architecture
Design Conceptual, Logical & Physical Models
Learn various modelling methodologies (Inmon, Kimball, Data Vault, OBT)
Apply principles of dimensional data modeling in a hands-on
Learn all the concepts and terms such as the OLTP, OLAP, Facts, Dimensions, Star Schema, Snowflake Schema

Who Should Attend

Anyone who is interested in becoming an Analytics Engineer
Anyone who want's to understand more about data modelling and data transformation

Target Audiences

Anyone who is interested in becoming an Analytics Engineer
Anyone who want's to understand more about data modelling and data transformation

Welcome to the Analytics Engineering Bootcamp course. the only course you need to become an amazing Analytics Engineer.

This complete Analytics Engineering Bootcamp will take you step-by-step through engaging and fun lectures and teach you everything you need to know on how to succeed as an Analytics Engineer. Throughout this course you’ll get an in depth insight into all the various tools, technologies and modelling concepts.

Students will learn how to design and implement a Data Warehouse solution using DBT (Data build tool) & BigQuery.

Each section contains scenario based quiz questions that help solidify key learning objectives for each concept & theory..

By the end of the course, you’ll learn and get really good understanding of:

Differences between database and a data warehouse
Concepts between OLTP & OLAP systems
Normalisation & De-Normalisation methods
Data Modelling methodologies such as (Inmon, Kimball, Data Vault & OBT)
Difference between ETL & ELT
Data modelling techniques especially using dbt
Hands-on experience building dimensional data warehouse

RECENT UPDATES:

Mar2023 – Updated Glossary and added more contents

Mar2024 – New: dbt Power User accelerated development lectures (Including usage of Data Pilot, Generative AI driven workflow assistant)

Who this course is for:

Data Analyst, BI Analysts or Data Warehouse developers who are looking to become Analytics Engineers or looking to improve existing skills
For data professionals who wants to get a refresher on all the concepts and terms surrounding OLTP & OLAP systems
Students or recent graduates who are looking to get a job as an Analytics Engineer
Anyone who is interested in Analytics Engineer Career Path

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Course Overview

Lecture 3: How to get the best out of this course

Lecture 4: Resources

Chapter 2: What is a database?

Lecture 1: Database Introduction

Lecture 2: Database definition

Lecture 3: SQL Example

Lecture 4: Database Management System (DBMS)

Lecture 5: Sheets vs Database

Lecture 6: OLTP

Lecture 7: OLTP ACID

Lecture 8: OLAP

Lecture 9: OLTP vs OLAP Summary

Lecture 10: NoSQL Introduction

Lecture 11: Key Value Store

Lecture 12: Document Store

Lecture 13: Wide Columns

Lecture 14: Graph Database

Lecture 15: Search Engines

Lecture 16: SQL vs NoSQL

Lecture 17: On-Prem vs Cloud

Chapter 3: What is a data warehouse?

Lecture 1: Data Warehouse Introduction

Lecture 2: Data Warehouse Definition

Lecture 3: Data Warehouse Benefits

Lecture 4: Data Warehouse Architecture

Lecture 5: Data Source

Lecture 6: Data Lake

Lecture 7: Data Warehouse Layer

Lecture 8: Business Intelligence Introduction

Lecture 9: Business Intelligence Tools

Lecture 10: ETL – ELT Introduction

Lecture 11: ETL

Lecture 12: ELT

Lecture 13: ETL vs ELT

Chapter 4: Data Modelling & ERD Notations

Lecture 1: Data Modelling & Entity Relationship Diagram (ERD) Introduction

Lecture 2: Data Modelling Overview

Lecture 3: ERD Overview

Lecture 4: Entity Attributes Relationships

Lecture 5: Steps to Create an ERD

Lecture 6: Build ERD using Chen's Notation Style

Lecture 7: Build ERD using Information Engineering Notation Style

Lecture 8: Data Modelling Concepts

Lecture 9: Different Type of Keys

Lecture 10: Recommended Tools for Creating ERD

Chapter 5: Normalisation & Denormalisation

Lecture 1: What is Normalisation?

Lecture 2: 1st Normal Form

Lecture 3: 2nd Normal Form

Lecture 4: 3rd Normal Form

Lecture 5: Pros & Cons of Normalised Model

Lecture 6: What is De-Normalisation?

Lecture 7: De-Normalisation Techniques

Lecture 8: Pros & Cons of De-Normalised Model

Chapter 6: Data Warehouse Design Methodologies

Lecture 1: Data Warehouse Design Methodologies Introduction

Lecture 2: Inmon Methodology

Lecture 3: Corporate Information Factory (CIF) Architecture Explained

Lecture 4: Inmon Architecture

Lecture 5: Pros & Cons of Inmon Methodology

Lecture 6: Kimball Methodology

Lecture 7: Processes of Kimball Methodology

Lecture 8: Kimball Architecture

Lecture 9: Pros & Cons of Kimball Methodology

Lecture 10: Inmon vs Kimball

Lecture 11: Hybrid Architecture

Lecture 12: Data Vault Methodology Introduction

Lecture 13: Data Vault Components

Lecture 14: Data Vault Architecture & Example

Lecture 15: Pros & Cons of Data Vault

Lecture 16: Inmon vs Kimball vs Data Vault

Lecture 17: One Big Table (OBT) / Wide Table

Lecture 18: Pros & Cons of OBT

Lecture 19: Data Modelling Then, Now & Next

Chapter 7: Dimensional Modelling

Lecture 1: Dimensional Modelling Introduction

Lecture 2: What is Dimensional Modelling?

Lecture 3: Data Warehouse LifeCycle Overview

Lecture 4: Program/Project Planning

Lecture 5: Requirement Gathering

Lecture 6: Concept & Steps of Dimensional Modelling

Lecture 7: Select Business Process & Declare the Grain

Lecture 8: Dimensions (Types)

Lecture 9: Conformed Dimensions

Lecture 10: Junk Dimensions

Lecture 11: Degenerate Dimensions

Lecture 12: Role Playing Dimensions

Lecture 13: Slowly Changing Dimensions (SCD) – Intro

Lecture 14: Type 0 – SCD (Slowly Changing Dimensions)

Lecture 15: Type 1 – SCD (Slowly Changing Dimensions)

Lecture 16: Type 2 – SCD (Slowly Changing Dimensions)

Lecture 17: Type 3 – SCD (Slowly Changing Dimensions)

Instructors

Rahul Prasad
Head of Analytics
David Badovinac
Analytics Engineer

Rating Distribution

1 stars: 9 votes
2 stars: 22 votes
3 stars: 119 votes
4 stars: 477 votes
5 stars: 740 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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