Data Wrangling and Preparation with Programming

Part of the ModularMaster in Data Science (Healthcare) programme

Data Wrangling and Preparation course provides a comprehensive understanding of the critical stages involved in preparing data for analysis, from sourcing and collection to transformation and cleaning. By mastering the techniques and tools involved in data wrangling, professionals can address data inconsistencies, missing values, and outliers, ensuring data quality and integrity. This process enables them to transform raw, unstructured data into a clean and usable format, making it ready for exploration and analysis. Effective data wrangling and preparation not only save time and effort during the analysis phase but also lay the groundwork for accurate and reliable insights. It allows data professionals to unlock the true potential of datasets, extract meaningful information, and make informed decisions that drive business success.

This course, spanning a duration of five days, is specifically designed to equip participants with comprehensive knowledge and hands-on experience in manipulating and preparing healthcare data for analysis. Over the course of the first four days, participants will learn various techniques to transform and clean healthcare data into a suitable format, addressing data quality issues, and employing effective data cleaning techniques. Participants will acquire expertise in data manipulation, including handling missing values, detecting outliers, and ensuring data consistency within the healthcare domain. They will learn practical methods to cleanse and preprocess healthcare data, ensuring it is well-prepared for further analysis. Participants will be actively involved in a healthcare-related project throughout the module. The final day, which is split into two half-days on separate weeks, will be dedicated to project consultation and project presentation. By the end of the course, participants will have a solid understanding of data wrangling principles and techniques, enabling them to adeptly tackle complex data challenges and effectively prepare datasets for analysis within the healthcare field.

Plan your learning path

This course can be taken as a module on its own or as part of the Graduate Certificate in Data Analytics (Healthcare) or ModularMaster in Data Science (Healthcare).

Who Should Attend

Catering to healthcare professionals and individuals aspiring to join the healthcare industry, this course is specifically designed to develop essential skills in data preparation and wrangling. It is highly recommended for clinicians, administrators, and managers who require practical, hands-on data wrangling skills in their day-to-day work, as well as individuals aspiring to become data analysts or data scientists with proficiency in data preparation within the healthcare sector.

Prerequisites

Participants should preferably have passed mathematics at least ‘O’ Level or equivalent.
Participants should be conversant with basic IT skills such as software installation, file management and web navigation.
Participants are encouraged to complete the Foundation of Data Science before enrolling in this course.
Participants are required to pass a pre-course assessment to ensure participants have the requisite knowledge of Python programming. This assessment can be waived if participants have completed both Fundamentals in Python (Basic) and Fundamentals in Python (Intermediate).
Participants are required to bring their laptops.

Programme Outline

Learning Objectives and Structure

Perform the basic ML model component for the role of a junior data scientist
Understand the types of data and databases in the business context
Appreciate the use of data dictionary and harness the potential of metadata for data science
Acquire organizational dataset from data lakes and other democratized data sources for data enrichment purposes
Structure data into an appropriate form for data analysis
Manipulate data structures to support data-wrangling phase
Perform data wrangling on the acquired dataset
Address data quality issues with appropriate data cleansing technique
Iterate the data mining process progressively with the provision of data wrangling and exploratory analysis tools
Understand healthcare case studies shared by SingHealth faculty members to gain insights into real-world scenarios.
Utilise curated public healthcare datasets to perform hands-on activities and assignments, fostering practical experience and understanding of the subject matter.

Day 1

Overview of Data Science Pipeline
What is Data Wrangling and Data Preparation?
Data Acquisition
Understand how data scientist prepares the dataset for data modelling
What is data discovery?
Types of data
Types of databases
Data Dictionary and Metadata
Data Models

Day 2

Data Mining and CRISP-DM
Common Computing Infrastructure
Interactive Data Exploratory Analysis (IDEA)
Basics of Descriptive Statistics

Day 3

Breakdown of Data Preparation Phases
Dataset Structuring: Data Frame Handling
Data Cleaning

Day 4

Data Enrichment and alternative sources
Data Enrichment: Data Aggregation
Data Enrichment: Data Standardisation

Day 5 - Consultation / Project presentation

Project Consultation

Each group of participants will present the progress of their projects and have the opportunity to ask questions and clarify any doubts pertaining to their projects.

Project Presentation

Each group of participants will showcase their work and respond to questions during a Q&A session.

Course Fees and Funding

Full course fee inclusive of prevailing GST	You pay S$4,905.00
SkillsFuture Course Fee subsidy (70%) For Singapore Citizens < 40 years old For Permanent Residents	You pay S$1,471.50
Mid-Career Enhanced Subsidy (90%) For Singapore Citizens ≥ 40 years old	You pay S$571.50
Enhanced Training Support for SMEs (90%) For SME - Sponsored employees	You pay S$571.50

The above module fee payable is inclusive of 9% GST.

Register your interest

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What are you interested in?: Foundation of Data Science Data Storytelling with Visualisation Relational Database Management Systems Data Wrangling and Preparation with Programming Data Validation and Statistical Analysis with Programming Data Science Modelling with Programming All of the above (for the full (Healthcare) ModularMaster in Data Science)
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Instructor

Thia Wei Soon
Instructor, SUTD Academy

Wei Soon has more than ten years of experience working in the manufacturing and IT sectors. He worked as a data scientist using data analytics and machine learning to deliver actionable insights and drive strategic marketing initiatives. In recent years, as a technology consultant, he successfully helped clients to streamline enterprise operations and achieved cost saving through the adoption of robotic process automation.

Wei Soon has a Master of IT in Business Artificial Intelligence from Singapore Management of University and a B.Eng in Mechanical Engineering from Nanyang Technological University. He is proficient with tools such as Tableau, Jupyter, RStudio, MS Visual Studio, Automation Anywhere, UiPath, and programming languages such as Python, R, C#, HTML5, and JavaScript.

Narayan Venkataraman
Assistant Director, Data Management & Informatics, Changi General Hospital

Narayan (Nari) is a Data Science and Biomedical professional with more than 22 years of experience in healthcare with diverse portfolio spanning data science, health informatics, data governance, medical technology, clinical quality and operational analytics, patient safety and risk management.

He is currently the Assistant Director, Data Management & Informatics at Changi General Hospital, Singapore. Recipient of the Singapore Commendation Medal 2022 for Covid19, he is a member of the CGH Covid19 Taskforce and many strategic committees at CGH and SingHealth (SHS). He has completed many medical projects across the Asia-Pacific region representing Singapore MOH and MFA. He is also an honorary biomed consultant for Smiles Asia and has volunteered for many surgical missions in Asia and Oceania. His current academic interests cover robotic process automation, AI/Machine Learning, data visualisation, risk analytics and enterprise data literacy.

Gao Yan
Senior Principal Analyst - Machine Learning, Health Services Research, Changi General Hospital

Presently holding the designation of Senior Principal Analyst, Changi General Hospital (CGH), Gao Yan used to work in research, education and finance sectors.

Trained in machine learning and computer vision, Gao Yan’s research interests include medical image processing, natural language processing, predictive modelling and data visualization. She obtained both her B.Eng and PhD in Information Systems from Nanyang Technological University (NTU).

Policies and Financing Options

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Course may be cancelled due to insufficient participants. SUTD Academy will not be responsible or liable in any way for any claims, damages, losses, expenses, costs or liabilities whatsoever (including, without limitation, any direct or indirect damages for loss of profits, business interruption or loss of information) resulting or arising directly or indirectly from any course cancellation.
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Types of Funding

Funding under Mid-Career Enhanced Subsidy ("MCES")

MCES is an enhanced Subsidy to encourage mid-career individuals to upskill and reskill, thereby helping them to remain competitive and resilient in the job market. With this, all Singaporeans aged 40 and above will receive higher subsidies of up to 90% course fee subsidy for SSG-funded certifiable courses.
Individuals/employers are not required to submit an application for the MCES. Those pursuing SSG-funded programmes will be charged the appropriate subsidised fees by SUTD Academy if they are eligible MCES. Individuals/employers will only need to pay the nett fee (full course fee after SSG's grant).

For more info, please visit SkillsFuture website at https://www.skillsfuture.gov.sg/enhancedsubsidy

Funding under Enhanced Training Support for SMEs ("ETSS")

ETSS is an enhanced funding to enable SMEs to send their employees for training.
SMEs will enjoy subsidies of up to 90% of the course fees when they sponsor their employees for SSG-funded certifiable courses.
In addition to higher course fee funding, SMEs can also claim absentee payroll funding of 80% of basic hourly salary at a higher cap of $7.50 per hour. SMEs may apply for the absentee payroll via the SkillsConnect system.
To qualify, SMEs must meet all of the following criteria:
- Organisation must be registered or incorporated in Singapore
- Employment size of not more than 200 or with annual sales turnover of not more than $100 million
- Trainees must be hired in accordance with the Employment Act and fully sponsored by their employers for the course
- Trainees must be Singapore Citizens or Singapore Permanent Residents

For more info, please visit SSG website at https://www.ssg.gov.sg/programmes-and-initiatives/funding/enhanced-training-support-for-smes1.html

Funding under Union Training Assistance Programme ("UTAP")

UTAP is a training benefit for NTUC members to defray their cost of training. This benefit is to encourage more union members to go for skills upgrading.

NTUC members enjoy 50% unfunded course fee support for up to $250 each year when you sign up for courses supported under UTAP (Union Training Assistance Programme).

For more info, please visit https://e2i.com.sg/individuals/ntuc-education-and-training-fund/.