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BusinessBusiness Analytics & IntelligenceData Warehouse

Analytics Engineering Bootcamp

Become an Analytics Engineer expert with just ONE course. Learn Data Modelling, dbt, Google Bigquery & many more!
Rating: 4.6 out of 54.6 (234 ratings)
2,190 students
Created by Rahul Prasad, David Badovinac
Last updated 4/2022
English
English [Auto]

What you'll 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

Requirements

  • No prior experience is required, however, if you have some basic understanding of SQL it'll help you understand this course better

Description

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 & 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


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

Who this course is for:

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

Instructors

Rahul Prasad
Lead BI Analyst
Rahul Prasad
  • 4.6 Instructor Rating
  • 234 Reviews
  • 2,190 Students
  • 1 Course

Rahul has 10+ years of experience working in BI & Data warehouse field. His expertise & specialisation is in building Analytics & BI solutions. Throughout this he has created various applications using Python to build web applications and to automate data solutions or using API to create embedded reports to enable custom visualisations.

Rahul has experience various BI solutions such as Looker, PowerBI or Tableau. He is also a Google certified Professional Data Engineer and Professional Data Architect.

Rahul has a passion for trainings and enjoy conducting workshops to companies.


David Badovinac
Analytics Engineer
David Badovinac
  • 4.6 Instructor Rating
  • 234 Reviews
  • 2,190 Students
  • 1 Course

Experienced, effective Analytics engineer with a wider background in  Business Intelligence. Having worked at top European high-growth startups and consultancies.
Helping companies improve, implement their BI stack from Data warehouse development to reporting and presentational layer.

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