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Designing and Building Data Products

Development Academy of the Philippines
Enrollment is Closed

Course Overview

Data is a vital component of an organization's operational plans, management decisions, and other relevant business matters. In order to use data effectively, it is important to understand its nature and technicalities. This course provides an overview of how to transform data into products or services. This course explores the intersection of data science and analytics, data products, and rapid prototyping. At the end of the course, the participants will be able to ideate and design their own data products and transform a business intelligence report or data science model into a web service.

Recommended Course Prerequisites:

What You Will Learn

Upon completion of this course, the learners are expected to:

  • gain background on the nature of data products and understand how to transform data into products or services;
  • be familiarized with data interactions and understand the basics of designing and building data products; and
  • understand development workflows and processes for data science.


The code repository for this course is available at the given Github repo. The learner can follow through with the examples in this course by cloning the repo to Google Colab or GCP Deep Learning VM.

Note to SPARTA scholars: Upon enrollment, you will have 6 months to finish a SPARTA course. Failure to complete the course in 6 months and/or inactivity for 3 months will result in course access revocation.

Course Instructor

Course Staff Image #1

Ronrick Da-ano

Subject Matter Expert

Course Content

Week 1: Introduction to Cloud Computing

7 Videos | 2 Activities

7 Videos

  • Welcome to the course!
  • Applied Data Science
  • Cloud Environments
  • Google Cloud Platform
  • Google Cloud SDK and Web CLI
  • JupyterLab Notebooks
  • Google Colaboratory

2 Activities

  • Recall Activity
  • Quiz

Week 2: Google Cloud Machine Learning Engine

4 Videos | 2 Activities

4 Videos

  • Google Cloud Dataflow
  • The Cloud MLE Train/Deploy Process
  • Training on Cloud MLE
  • Scikit-learn on Cloud MLE

2 Activities

  • Ponder and Prove
  • Peer-Graded Assignment

Week 3: Google Auto ML

2 Videos | 2 Activities

2 Videos

  • Cloud Vision
  • Cloud NLP

2 Activities

  • Recall Activity
  • Quiz

Week 4: Modeling on GCP

7 Videos | 2 Activities

7 Videos

  • The Modeling Architecture on GCP
  • Explore Data Analysis
  • Spot Checking ML Algorithms
  • Dataflow and Tensorflow Transform to Large-Scale Data Processing
  • Training on Cloud MLE
  • Deployed Trained Model
  • Batch Prediction

2 Activities

  • Recall Activity
  • Peer-Graded Assignment

Week 5: Microservice Architecture

6 Videos | 2 Activities

6 Videos

  • Containers and Google Kubernetes Engines
  • Kubernetes
  • Kubeflow
  • Kubeflow Pipelines
  • Overview of a Simple End-to-End Solution Pipeline
  • Key Takeaways

2 Activities

  • Recall Activity
  • Capstone
  1. Course Number

  2. Classes Start

  3. Classes End

  4. Estimated Effort

    1-2 hours/week (10 hours)
  5. Price