DT Academy

Our Courses

We offer specialist courses on Google Cloud data, AI and machine learning – the core areas where we have unparalleled experience and expertise to share. All courses blend hands-on training with theoretical learning and can be customised to your requirements with industry or business specific use cases.

Our Courses

Your courses, your way

We believe Cloud skills should be accessible to all. That’s why we offer flexible ways to learn with blended delivery methods and customised content for maximum impact.

Official Google Cloud Courses
Official Google Cloud Courses
Our team of Authorised Google Cloud trainers can deliver every official course across a range of topics including Analytics, Looker, Infrastructure, Machine Learning, Data Engineering, Networking and more. You can view some example courses below.
Tailored & Custom Courses
Tailored & Custom Courses
We offer tailored and custom courses to suit your exact business requirements. This includes everything from dialling up or down aspects of standard curriculums, to building and delivering an entirely new course from scratch.

Select learning path

Data Engineering
Data Engineering
BI Development & Analytics
BI Development & Analytics
DevOps Engineering
DevOps Engineering
Machine Learning Engineering
Machine Learning Engineering

Data Engineering

Data Engineering is software engineering applied to the movement and processing of Data. A data engineer’s role is to build applications which connect systems, collect data and process that data into a useful format for others in the business. Often this data is ‘big’ and requires distributed frameworks or pipelines to move and process it. A data engineer is a key cog in a data platform as they look after the acquisition of data for analytics, and connecting the output of ML models to operational systems.

As a Data Engineer I work with building systems that can process large amounts of data in a secure and reliable way. What I like most about the role is that I get to solve challenging problems in collaboration with other people, and that there are always new and better ways of doing things as the technologies within data and AI are evolving very quickly. Johanna, Senior Data Engineer

BI Development & Analytics

Business intelligence (BI) is a set of technologies and practices that transform business information into actionable insights which eliminate inefficiencies and drive change.

 

A BI Analyst is responsible for designing, building and maintaining analytics solutions, with a primary focus on business unit-specific data marts, self-service data models, and building front-end analytical visualisations and dashboards. Whereas the BI Developer is responsible for designing, building, and maintaining end-to-end business intelligence solutions, with a primary focus on enterprise-wide data warehouse design, data models, and storage optimisation.

As a BI Developer, I help companies build, end to end BI solutions from developing data warehouses to building reports while helping companies understand valuable insights. I really enjoy helping companies reach their potential through data. I'd recommend this career to anyone wanting to build impactful solutions that make a difference. David, Senior BI Developer

DevOps Engineering

DevOps is all about the unification and automation of processes, to balance needs throughout the software development life cycle. DevOps engineers are instrumental in combining code, enabling deployment, application maintenance, and application management. Successful DevOps requires not just an understanding of development life cycles but also DevOps philosophy, practices and tools.

As a DevOps engineer, I bridge the gap between the work produced by our data/development teams and the Cloud to make the code runnable by us, the client and in some cases the public. A key part of the job is the creation of pipelines to allow code to be built, tested and deployed automatically, ensuring that quality is baked into the final product. I particularly enjoy the automation aspect of the work; watching code and infrastructure build itself is always satisfying! Alex, Senior DevOps Engineer

Machine Learning Engineering

Combining software engineering and data science, this branch of AI involves the creation of programmes and algorithms to enable machines to act without being directed, and to learn and improve from data. An ML engineer designs and builds machine learning solutions that are reliable, fair and maintainable.

As a Machine Learning Engineer, I use scientific principles, tools, and techniques of machine learning and traditional software engineering to design and build complex computing systems. I really enjoy moving the data science lifecycle into a production system which heavily uses automation, orchestration and simplicity to generate value for the consumers. This role fits well for problem-solvers, engineers and machine learning enthusiasts who care about scale, robustness and impact. Felix, ML Engineer

Available courses

Data Engineering on Google Cloud

This course provides a hands-on introduction to designing and building data processing systems on Google Cloud Platform. You will learn how to design data processing systems, build end-to-end data pipelines, analyse data, and carry out machine learning. The course covers structured, unstructured, and streaming data.

 

Pre-requisites: 
Google Cloud Foundations or equivalent experience; basic proficiency with common query language such as SQL; experience with data modelling, extract, transform, load activities; experience with developing applications using a common programming language such as Python; familiarity with ML and/or statistics.

 

Course objectives:
+  Design and build data processing systems on Google Cloud
+  Process batch & streaming data by implementing auto-scaling data pipelines on Dataflow
+  Derive business insights from extremely large datasets using BigQuery
+  Train, evaluate, and predict using machine learning models using Tensorflow & Vertex AI
+  Leverage unstructured data using Spark and ML APIs on Cloud Dataproc
+  Enable instant insights from streaming data

  • Level: Beginner, Intermediate
  • Length: 4 days
Apply for this course

Intro to BI Development & Analytics

Learn how to design, build, visualise and maintain end-to-end business intelligence solutions to harness real time insights. This course is perfect for existing Data Analysts, Programmers and Data Warehouse Analysts looking to upskill and apply their experience to Google Cloud Platform. 

 

Pre-requisites: 
Proficient in SQL

 

In this course you will learn:
+  fundamentals of Google Cloud platform
+  agile and scrum methodology
+  how to build a BI stack
+  data modelling, data transformation and pipeline building
+  how to understand cloud data architecture
+  how to build a data warehouse

  • Level: Beginner, Intermediate
  • Length: 5 days
Apply for this course

DevOps Engineering

This course teaches you the skills needed to start using DevOps tools to manage and automate application development and deployment. You will learn how to build continuous integration and deployment pipelines using tools like Git, Docker, Kubernetes, Terraform, Cloud Build, Cloud Run and more.

 

Pre-requisites:
GCP Foundations or equivalent experience; basic understanding of Linux operating system and commands; some training and experience with Cloud services; programming experience in languages like Java, .NET, JavaScript, etc; scripting experience relevant for Cloud/DevOps with languages like Python or bash shell.

 

Course objectives:
+  Manage applications using DevOps automation and tools
+  Architect applications using microservices
+  Manage source code and versions using Git
+  Incorporating Cloud security in DevOps
+  Deploy microservices using Docker containers
+  Orchestrate container deployment using Kubernetes (incl. via Cloud Run)
+  Automate deployment resources using Infrastructure as Code tools
+  Build CI/CD pipelines using Cloud Build
+  Ensure service quality using Site Reliability Engineering techniques

  • Level: Beginner, Intermediate
  • Length: 4 days
Apply for this course

Machine Learning Engineering

Learn how to write, train and optimise Machine Learning models and how to strategically apply ML as a business solution. This course is perfect for existing Data Scientists, Data Engineers and Programmers looking to upskill and learn how to apply ML models in Google Cloud Platform. 

 

Pre-requisites:
Foundational understanding of machine learning concepts; Proficient in a common query language (e.g. SQL); Experience programming in Python

 

In this course you will learn:
+  fundamentals of Google Cloud platform
+  agile and scrum methodology
+  advanced machine learning techniques and algorithms
+  how hyperparameters impact models in optimisation 
+  how to package and deploy your models to a production environment
+  hands-on experience of deploying trained models and evaluating performance

  • Level: Beginner, Intermediate
  • Length: 2-3 weeks
Apply for this course

Register your interest now

Our courses are designed to help learners reach their potential and to help businesses achieve their goals.

Register your interest now
It was an amazing and interactive course, I enjoyed every part of it. It's been the single-most important thing for my future career in data. Yadvendra Bhadauria, Academy Graduate 2021

Contact us

Not sure which course is right for you or your business? Get in touch with us and our team will help you decide on your next steps.