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Data Engineering

Analytics Engineering Course

US Online

The analytics engineering course teaches you the modern data warehouse skills including snowflake, dbt, airbyte, reverse ETL, and prepares you for an analytics engineer role. The modern data stack has become widely popular in the past few years and analytics engineering is becoming the new frontier of data engineering. In this course you will gain the most in-demand skills by

  • following a world-class curriculum designed by experts
  • completing an end to end analytics engineering project
  • working with popular tools that help you get noticed
Intermediate
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Part-time
Online Live
10 weeks
100 hours
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About the Course

This course can be taken as a part of WeCloudData’s Data Engineering Bootcamp. It teaches you how to leverage the modern data stack to create the enterprise data assets for BI and data science. It focuses on case-based learning and project building.

 

  • What you learn
    • Data Infrastructure (AWS, Docker)
    • ELT and Data Ingestion (dbt, Airbyte)
    • Data Warehouse (Snowflake)
    • DBT and Transformation
    • Reverse ETL
    • Data engineering best practices in the industry
    • How to build end-to-end data warehouse projects

WeCloudData is the perfect place to grow your career

5/5

Laura Vieira, Data Engineer

“Amazing course and support”

The course was really great a little too fast if you are not in a technical field already. You will need to study hard reviewing classes and making the labs and assignments but you always have the support of the TAs (they are awesome) or even the instructors and your own classmates that are always helping each other via Slack. There will be a final project where you will need to present a pipeline that you created (don’t worry they will be helping you!). Then, they will be helping you to find a job. Shaohua is always looking for the best for his students, he wants to make sure that you have the best experience with them.

Ranked #1 Data Training Program

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CURRICULUM

Be ready for the new economy

WeCloudData programs are designed to be project-based. We not only cover essential theories, but also teach how to apply tools and platforms that are in high demand today. Our program curriculum is also highly adaptive to the latest market trends. 

Module 1
Data Infrastructure
This module introduces learners to the data infrastructure side of data engineering. Learners will learn how to work with AWS and use docker to spin up environments to run various open source tools. Modern data engineers should get comfortable with trying out new tools and shouldn't be too comfortable with getting handed over existing infrastructure to work with.
LEARNING OUTCOMES
  • Learn how to work with AWS, especially storage (S3), compute (EC2, Lambda), and databases (RDS)
  • Get familiar with linux command line prompts
  • Learn the basics of docker containers and know how to spin up environments that are required to complete end to end projects
  • Understand the essential role of data infrastructure in modern data stacks and why some tooling decisions are made in practice
KEY SKILLS
  • Linux Commands
  • AWS
  • Cloud Storage
  • EC2
  • Cloud Instance
  • S3
  • RDS
  • Serverless
  • Lambda
  • Docker
  • Containerization
  • Containers
Tools:
AWS
EC2
S3
Docker
Lambda
Linux
Tools:
SQL
RDS
Module 2
Databases
Data engineers don't just work with tools. SQL and databases are still the most important skills data engineers need to have. Tools will evolve over time but the ability to understand data relations, query and manipulate data is the key to the success. In this module, students will learn the basics of relational databases.
LEARNING OUTCOMES
  • Master of the concepts of data modeling
  • Practice advanced SQL skills
  • Understand database performance tunings
KEY SKILLS
  • ERD
  • Data Modeling
  • SQL
  • RDBMS
  • SQL Query Plan
  • SQL Query Tuning
  • Advanced SQL
Module 3
ELT and Data Ingestion
This module teaches students how to ingest data using modern data ingestion tools such as Apache Airbyte. Students will learn the concepts of ELT, its difference from the traditional ETL approach, and get familiar with different types of data sources.
LEARNING OUTCOMES
  • Understand the difference between ELT and ETL
  • Understand different types of data sources including:
    • Marketing data (Mailchimp)
    • Sales data (Hubspot)
    • Google analytics data
    • Product analytics data (Mixpanel, Amplitude)
    • Social media data
    • etc.
  • Learn how to set up connectors and ingest data using Apache Airbyte
KEY SKILLS
  • Data Connectors
  • Data Ingestion
  • Incremental Loading
  • OAuth2
  • Apache Airbyte
  • ELT
  • ETL
Tools:
Airbyte
Docker
Mixpanel
GA
Ads
Hubspot
Tools:
Snowflake
Redshift
Module 4
Data Warehouse
This module teaches learners the core concepts of modern data warehouse. It focuses on important topics such as dimensional modelling, slowly changing dimension, wide tables, and data marts. Learners will get to build a data warehouse in Snowflake.
LEARNING OUTCOMES
  • Understand the concepts of star schema and dimensional modelling
  • Understand different types of SCD (slowly changing dimensions)
  • Know the pros and cons of dimensional models and the alternative ways such as OBT (wide tables)
  • Build data models and work with modern data warehouses such as Snowflake and Redshift
KEY SKILLS
  • Data Warehouse
  • Snowflake
  • Redshift
  • Dimensional Modelling
  • Kimball
  • Wide Tables
  • OBT
  • Slowly Changing Dimension
  • Data Mart
Module 5
DBT and Transformation
This module teaches learners one of the most popular SQL-based orchestration platform: Data Build Tool (dbt). It focuses on dbt models , macros, and the essence of data transformations. Learners will follow the best practices to build the landing, staging, warehouse, and mart layers in the warehouse.
LEARNING OUTCOMES
  • Learn how to structure the data warehouse
  • Learn how to structure your dbt project
  • Follow the best practice to model and transform data using dbt and SQL in warehouse
KEY SKILLS
  • Data Transformation
  • DBT
  • ELT
  • Data Modelling
  • Workflow Orchestration
Tools:
dbt
Snowflake
Tools:
Lambda
Hightouch
Snowflake
Module 6
Reverse ETL
Data warehouse is only valuable when the data can be used effectively by the end users. Reverse ETL is the process of moving data from the modern data warehouse to the operational systems such as Google/Meta Ads, Mailchimp, Hubspot, etc. In this module, we will teach students the common reverse ETL platforms such as Hightouch and how to set up reverse ETL using AWS Lambda serverless functions when reverse ETL tools are not readily available.
LEARNING OUTCOMES
  • Learn how to push data from the warehouse to downstream applications
  • Learn how to create marts or wide tables for reverse ETL
  • Learn how to send data to operational systems using serverless frameworks such as Lambda
KEY SKILLS
  • Reverse ETL
  • Hightouch
  • AWS Lambda
  • Sink Connectors
  • Rudderstack
  • Census
  • Airbyte
FACULTY TEAM

Learn from the best

We’ve brought together a team of highly skilled and experienced instructors to help you learn effectively. Our instructors have a passion for teaching and a wealth of real-world experiences in their respective fields, so you can be confident that you’re learning from the best.

Stan Taov
Data Engineer @ Telus
Stan Taov is a professional data engineer with hands-on experience in building, validating and deploying machine learning models and creating end-to-end data pipelines. He is passionate about all things data. Stan has strong knowledge of converting business problems into analytical use cases and enjoys solving problems and building data-driven architecture. Stan has a passion for big data technologies and machine learning. Stan finished the Applied Data Engineering diploma program from WeCloudData and York University’s machine learning and predictive analytics certification programs. Stan switched his career multiple times, he moved from civil engineering to business and marketing and from the business field, he moved to data engineering. Currently, Stan works for Telus where he foresees and solves any operational issues with the on-prem cluster. He builds ETL pipelines and deploys spark analytical workloads.
Indrani Gorti
Director of Platform and ML engineering, Loblaw Digital
Indrani Gorti is a polyglot with experience in data engineering, data science, and solving business problems using ML and AI. She has worked in major industries like Finance, Telecommunications, Technology, Retail, and Startups. Her work has involved end-to-end projects from data ingestion, data analysis to the deployment of models in various setups using user transaction and interaction logs. Indrani is a Director of Platform and ML engineering at Loblaw Digital. She holds a BS and MS in Computer Science.
Edwin Guo
Big Data Specialist @ CPPIB
Edwin is an expert Data Engineer and Cloud Architect who is specialized in Distributed Computing, Distributed Storage, Microservice, Cloud(AWS), Machine learning and Blockchain technologies. Skilled in Spark, Apache Kafka, Akka, Cassandra and various popular tools in the open-source landscape, he has had the opportunity to lead many challenging big data projects in the past. Coming from a Software Engineer background with a strong passion for the CS space, Edwin Guo is well versed in productive languages such as Scala, Java, Clojure, Python, and Golang. His past experience covers many domains including telecommunications, stock market price dissemination/portfolio management system, customer loyalty programs, and cybersecurity.

Project

Portfolio Experience Building

This is what you will experience

  • Create the modern data warehouse project plan
  • Design the end to end data flow
  • Create the system design diagram
  • Set up data ingestion pipeline
  • Develop the ELT strategy and data models
  • Use dbt and snowflake to orchestrate the transformation pipelines
  • Reverse ETL data into operational systems
  • Code review with project mentors
  • Present the project and publish your portfolio work
SCHEDULE

Upcoming Start Dates

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Analytics Engineering Course
$3,800 USD
  • Case-based learning
  • Portfolio project mentoring
  • Flexible payment plan
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Recommended Short Courses
$3,800 USD
  • Up-skill your DE skills by taking the next-level DE courses
  • Get alumni discount for other DE, AI, MLOps, DevOps courses
  • Short courses to consider after completing this course ⇩
Upgrade to Bootcamp
$6,000 - $12,000 USD
  • Upgrade to the data engineering bootcamp and save $5,000
  • Get extensive 1-1 career mentoring and job support
  • Get the flexibility to create your own bootcamp
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What our graduates are saying

5/5
Albert N.

Graduated 2022 | Overall ⭐⭐⭐⭐⭐

“A solid bootcamp”

I completed the Data Engineering bootcamp at Weclouddata and was fortunate to land a job approximately 1 month after graduating. As many others have mentioned, the most significant reason that makes WCD stand out is its corporate partnerships. These companies, ranging from high profile multinationals to small local startups, provide real development work for the students and in turn makes all the difference on your resume. As long as you work hard in this program, you have a good chance of success.

5/5
Anonymous Graduate, 2022

Graduated 2023 | Overall ⭐⭐⭐⭐⭐

“Data Engineering boot camp – great experience”

Prior to joining the WeCloudData Data Engineering boot camp I already had some Data Engineering experience in previous jobs and a master’s degree in big data and machine learning. This program provided me with the opportunity to enhance my data engineering background and helped me land a Senior Data Engineer role in AI operations.

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FAQ

Frequently asked questions about the bootcamp
This is an intermediate level data engineering course. It is suitable as the second course in your data engineering learning path, after the Python and SQL fundamentals courses.
Yes. Ideally the learners joining this course already have experiences with SQL and some knowledge of Relational Databases.
Students taking this course are either career switchers going through the Data Engineering Bootcamp (they take this as a part of the graduation criteria), or data analysts, developers, and IT professionals who would like to up-skill themselves and stay competitive in the market.
This course prepares you for the skills required for a data engineer role. We provide project mentoring and students will graduate with a solid capstone project. If you want career help, job referrals, and career mentorship, you will need to purchase different packages. Career support comes by default with the bootcamp programs, but not the short courses.
You will be eligible for data engineer, analytics engineer, and data warehouse engineer related positions. Past experience also matters when it comes to the seniority of the offer. You can inquire about WeCloudData’s real project course or career mentoring programs if you want job support.
This course primarily focuses on AWS.
This course requires basic knowledge of SQL programming and some python. If you are a career switcher and don’t know much about coding, we recommend you take WeClouddata’s Data Fundamentals course.
Yes, if you take the Data Engineer Bootcamp, you will get $5,000 scholarship. You can basically take the third course in the data engineering curriculum for free.
Yes. You can fill out the form on this course page to see the tuition and financing options.
Some students have their employers cover the tuition. You can always ask your employers about it. We’re happy to provide the curriculum and enrolment letter.
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View our Analytics Engineering Course course package
View our Analytics Engineering Course course package