Setia Budi completed his academic exercise in Computer Science at the University of Tasmania, Australia. Australia Awards Scholarships and Sense-T Elite Scholarships enabled him to get his Master and PhD qualifications. His primary research interests include optimisation problem, environmental monitoring, data science, educational data mining, and computer vision.
2018
2018
2018
2017
2016
2015
2018
To be presented in November 2018
2013
2013
Prescribed textbook: Elementary Statistics: Picturing the World, 6th Edition
Prescribed textbook: Grokking Algorithm
Prescribed textbook: Data Structures and Algorithms in Python
2013
2011
2005-present
2013-2018
2013-2017
2018
IEEE Member ID: 94845913
2018
This course introduces student to sampling and exploring data, as well as basic probability theory and Bayes’ rule. Student will examine various types of sampling methods, and discuss how such methods can impact the scope of inference. A variety of exploratory data analysis techniques will be covered, including numeric summary statistics and basic data visualization. Student will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The concepts and techniques in this course will serve as building blocks for the inference and modeling courses in the Specialization.
on going course
2018
This course introduces the core data structures of the Python programming language. The course will move past the basics of procedural programming and explore how we can use the Python built-in data structures such as lists, dictionaries, and tuples to perform increasingly complex data analysis. This course will cover Chapters 6-10 of the textbook “Python for Everybody”. This course covers Python 3.
[certificate]2018
This course aims to teach everyone the basics of programming computers using Python. The course covers the basics of how one constructs a program from a series of simple instructions in Python. The course has no pre-requisites and avoids all but the simplest mathematics. Anyone with moderate computer experience should be able to master the materials in this course. This course will cover Chapters 1-5 of the textbook “Python for Everybody”. Once a student completes this course, they will be ready to take more advanced programming courses. This course covers Python 3.
[certificate]2017
This is a focused course designed to rapidly get you up to speed on doing data science in real life. The goal was to make this as convenient as possible for student without sacrificing any essential content.
After completing this course student will know how to:
- Describe the “perfect” data science experience
- Identify strengths and weaknesses in experimental designs
- Describe possible pitfalls when pulling / assembling data and learn solutions for managing data pulls.
- Challenge statistical modeling assumptions and drive feedback to data analysts
- Describe common pitfalls in communicating data analyses
- Get a glimpse into a day in the life of a data analysis manager.
2017
This is a focused course designed to rapidly get you up to speed on doing data science in real life. The goal was to make this as convenient as possible for student without sacrificing any essential content.
After completing this course student will know how to:
- Describe the basic data analysis iteration
- Identify different types of questions and translate them to specific datasets
- Describe different types of data pulls
- Explore datasets to determine if data are appropriate for a given question
- Direct model building efforts in common data analyses
- Interpret the results from common data analyses
- Integrate statistical findings to form coherent data analysis presentations
2017
This is a focused course designed to rapidly get you up to speed on doing data science in real life. The goal was to make this as convenient as possible for student without sacrificing any essential content.
After completing this course student will know how to:
- The different roles in the data science team including data scientist and data engineer
- How the data science team relates to other teams in an organization
- What are the expected qualifications of different data science team members
- Relevant questions for interviewing data scientists
- How to manage the onboarding process for the team
- How to guide data science teams to success
- How to encourage and empower data science teams
2017
This is a focused course designed to rapidly get you up to speed on doing data science in real life. The goal was to make this as convenient as possible for student without sacrificing any essential content.
After completing this course student will know how to:
- How to describe the role data science plays in various contexts
- How statistics, machine learning, and software engineering play a role in data science
- How to describe the structure of a data science project
- Know the key terms and tools used by data scientists
- How to identify a successful and an unsuccessful data science project
- The role of a data science manager