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

Let’s get together and enjoy the fun from treasure hunting in massive real-world datasets

Read blogs and updates from our community and alumni

Explore different Data Science career paths and how to get started

What Do Data/BI Analysts Do

A Typical BI Process

A typical BI process includes 4 steps:

  1. Business Analysis: How to make money in a business? Core processes and key decision areas; goal and objectives; strategies, tactics and operations.
  2. Data Analysis: Preparing data for analysis is more than half the battle; data sourcing; data pre-processing, exploratory data analysis (EDA) and summary statistics, data issues and treatments, extract-transform-load (ETL).
  3. Data Modeling: A data model reflects the business model in a digital world; data models are a shortcut to understanding the business model; entity relationship diagram (ERD), using primary key and foreign key to create one-to-many relationships between dimension tables and fact table.
  4. Data Visualization: Storytelling with data, producing interactive reports and dynamic dashboards with a 360-degree view of a business; dimensions and measures (KPIs), rollup and drilldown, slice and dice, sort and rank, conditional formatting and traffic lights; visualize trends and drivers, present conclusions and recommendations with real impact on business (e.g. revenue growth, cost reduction, efficiency gain, quality improvement).

BI Roles and Responsibilities

Career opportunities for BI roles have grown exponentially to meet the increasing demands of digitally transformed industries, businesses, functions, and processes. Organizations are looking to fill 4 major BI roles in a BI team with overlapping responsibilities: BI Analyst (junior) and BI Consultant (senior) who spend more time in step 1 and 4 of the BI process, BI Developer (junior) and BI Engineer (senior) who spend more time in step 2 and 3 of the BI process. When there isn’t a BI team and separation of duties, you will need to do all 4 steps and cover the entire BI process.

BI teams combine business analysis, data analysis, data modeling, and data visualization into one end-to-end BI process to deliver various BI services. For example:

  1. Building pipelines. Define KPIs. Develop Dashboards. Automate Processes.
  2. Determining when to use what charts to visualize measures by dimensions: line (trend of time series), pie (part-to-whole relationship),column or bar (comparison), histogram (distribution), scatterplot (correlation).
  3. Rollup and drilldown, slice and dice, sort and rank, present findings, recommend actions to monetize an opportunity (before window closes) or fix a problem (before it gets bigger).
  4. Presenting a 360-degree view of a business, from different angles (product, marketing, sales, operations, finance) and at different levels (analysts, managers, directors, VPs, C-Suite).

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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