Our Students
Bootcamp Programs
Short Courses
Portfolio Courses
Bootcamp Programs

Launch your career in Data and AI through our bootcamp programs

  • Industry-leading curriculum
  • Real portfolio/industry projects
  • Career support program
  • Both Full-time & Part-time options.
Data Science & Big Data
Data Engineering

Become a data analyst through building hands-on data/business use cases

Become an AI/ML engineer by getting specialized in deep learning, computer vision, NLP, and MLOps

Become a DevOps Engineer by learning AWS, Docker, Kubernetes, IaaS, IaC (Terraform), and CI/CD

Short Courses

Improve your data & AI skills through self-paced and instructor-led courses

  • Industry-leading curriculum
  • Portfolio projects
  • Part-time flexible schedule
Portfolio Courses

Learn to build impressive data/AI portfolio projects that get you hired

  • Portfolio project workshops
  • Work on real industry data & AI project
  • Job readiness assessment
  • Career support & job referrals

Build data strategies and solve ML challenges for real clients

Help real clients build BI dashboard and tell data stories

Build end to end data pipelines in the cloud for real clients


Choose to learn at your comfort home or at one of our campuses

Corporate Partners

We’ve partnered with many companies on corporate upskilling, branding events, talent acquisition, as well as consulting services.

AI/Data Transformations with our customized and proven curriculum

Do you need expert help on data strategies and project implementations? 

Hire Data, AI, and Engineering talents from WeCloudData

Our Students

Meet our amazing alumni working in the Data industry

Read our students’ stories on how WeCloudData have transformed their career


Check out our events and blog posts to learn and connect with like-minded professionals working in the industry

Read blogs and updates from our community and alumni

Explore different Data Science career paths and how to get started

What do Data Engineers Do?

Collecting data: Before starting any work, data engineers need to gather data from the right sources. After adopting some dataset standards, the data engineer stores the upgraded data.

Building Data model: Data engineers use data model to integrate data from various sources. In addition, the data model is used for data analysis or data science purpose, so the data model must meet the requirements from data analysis or data sciences teams.

Processing data: data comes from various sources with various quality and format. Data Engineer are supposed to cleanse, transform and deduplicate these data to meet the data standard of the project.

Building data infrastructure: A Data Engineer is also responsible to build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL, Python and ‘big data’ technologies. We call this a data ‘pipeline’. Data Engineers will also automate the data pipeline in the production stage.

Continuous Optimizing the data delivery process: As a Data Engineer, you need not only to know how to build a data delivery process, you also need to be familiar how to optimize the entire data pipeline through the coding and infrastructure level.

Testing: After the codes have been developed, Data Engineers will run a set of testing processes, like Unit testing, Integrated Testing and so on. The main reason why the codes are tested by Data Engineers internally instead of by testing team is that in many cases only the Data Engineers have skills and knowledge to create testing cases and check testing result.

If you want to know more information, please watch our videos:

Data Engineers’ Responsibility

A Data Engineer’s daily work

Check out other career guides

Our Career Guide provides all the resources you will need to help you get started in navigating data careers. We include free resources, guides, and tools that will help you get started.


WeCloudData is the leading data science and AI academy. Our blended learning courses have helped thousands of learners and many enterprises make successful leaps in their data journeys.

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