Course Code: ANL503
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Synopsis

ANL503 Data Wrangling equips students with advanced data acquisition and manipulation techniques for real-world analytics. Using MySQL, Python, and R, students learn to acquire data from relational databases and common file formats in a scalable, reproducible manner, then transform raw, messy data into analytics-ready formats. The course concludes with an introduction to visualisation in R to support exploratory analysis and communication of findings.
Level: 5
Credit Units: 5
Presentation Pattern: EVERY REGULAR SEMESTER

Topics

  • Introduction to the MySQL RDBMS and SQL as a glue language for analytics.
  • Essential concepts in probability, statistics, and data quality relevant to data wrangling.
  • Introduction to SQL and data manipulation with the SELECT statement (filtering, sorting, simple aggregation).
  • Combining data from multiple sources with union and joins.
  • Understanding and applying regular expressions.
  • Introduction to R for data analysis.
  • Data manipulation in R (importing, reshaping, joining datasets).
  • Essential principles of data visualisation and exploratory plotting in R.
  • Introduction to Python programming for data tasks.
  • Practical Python for data acquisition from files (CSV, Excel) and basic mangling and reporting.
  • Using Python to automate scalable spreadsheet and flat-file data acquisition.
  • Introduction to end‑to‑end data pipelines combining MySQL, R, and Python for reproducible analytics workflows.

Learning Outcome

  • Assess the appropriateness of relational database designs and data models for different analytics needs and data characteristics.
  • Critique data visualisations for exploratory analysis constructively.
  • Construct SQL queries (including joins and aggregations) to acquire and reshape data from relational databases.
  • Assemble reproducible data flows in MySQL, R, and Python for cleaning, transforming, and integrating heterogeneous tabular data from databases and files.
  • Create Python scripts to automate acquisition and processing of spreadsheet and delimited-text data at scale.
  • Design and implement clear visualisations in R to support data quality checks and exploratory analysis.
Part-time Programmes / CET Courses

Date and Duration

DayDateWeekTime
Thursday26 Feb 2026707:00 pm - 10:00 pm
Thursday5 Mar 2026807:00 pm - 10:00 pm
Thursday12 Mar 2026907:00 pm - 10:00 pm
Thursday19 Mar 20261007:00 pm - 10:00 pm
Thursday26 Mar 20261107:00 pm - 10:00 pm
Thursday2 Apr 20261207:00 pm - 10:00 pm


Target Audience

Executives interested analytics.

 

Relevance of Course to employment/upskilling/reskilling

ANL503 Data Wrangling supports employment, upskilling, and reskilling by equipping learners with practical skills to collect, clean, transform, and integrate real-world data for analysis. As messy, unstructured, and multi-source data are common in the workplace, these competencies are essential for analytics roles across industries and enable professionals to build reliable datasets, improve productivity, and deliver trustworthy insights for decision-making.


Admissions pre-requisite

Refer to graduate CET admissions and also include Admission Eligibility Criteria for Graduate CET Modular Courses (Admission Eligibility Criteria for Graduate CET Modular Courses).

 

Schedule

TimeAgendaDeliverables
Session 1

Introduction to Data Wrangling

  • 19:00 – 20:30 Introduction to the MySQL RDBMS and SQL as a glue language for analytics
  • 20:30 – 20:45 Break
  • 20:45 – 22:00 Essential concepts in probability and statistics
-
Session 2
Introduction to SQL
  • 19:00 – 20:30 Introduction to SQL and data manipulation with the SELECT statement
  • 20:30 – 20:45 Break
  • 20:45 – 22:00 Combining data from multiple sources with union and joins
-
Session 3
Regular Expressions 
  • 19:00 – 20:30 Understanding regular expressions
  • 20:30 – 20:45 Break
  • 20:45 – 22:00 Introduction to R
-
Session 4
Data manipulation in R 
  • 19:00 – 20:30 Data Manipulation with R
  • 20:30 – 20:45 Break
  • 20:45 – 22:00 Essential principles of data visualisation
TMA Due:
Before Session 5
Session 5
Essential Python
  • 19:00 – 20:30 Introduction to Python programming
  • 20:30 – 20:45 Break
  • 20:45 – 22:00 Practical Python for data acquisition, mangling, and reporting
-
Session 6
Acquiring data programmatically 
  • 19:00 – 20:30 Handling web APIs and web scraping
  • 20:30 – 20:45 Break
  • 20:45 – 22:00 Using Python for scalable spreadsheet data acquisition and/or In-class assessment
ECA Due:
2-weeks after Session 6

 

Assessments

Assignments, Online Test, Others, Written Exam, PARTICIPATION

 

Trainer info

A/P Marcus Lee is Vice-Dean of the School of Business at SUSS. Marcus specialises in the areas of service experience design and measurement, data visualisation, and data-intensive analytics. 

Prior to SUSS, Marcus was Director, Customer Engagement and Strategy at LTA, where he led a team to enhance LTA’s ability to be citizen-centric and pro-enterprise, as well as to create collaborative partnerships for greater affinity towards public transport as a way of life.
 
Prior to his role at LTA, Marcus was the founding academic director of the Institute of Service Excellence at SMU, and was the primary person responsible for the design, execution, and evolution of the Customer Satisfaction Index of Singapore (CSISG) from 2007 to 2016.
 
Marcus has a Ph.D. in Marketing from the Joseph L. Rotman School of Management at the University of Toronto, and a B.A.Sc. in Computer Engineering from the Faculty of Applied Science and Engineering at the University of Toronto, Canada.

Dr. Liu Wenting is Head of the Master of Artificial Intelligence for Business programme at SUSS. Her background combines a Ph.D. from NUS and extensive industry experience as a Senior Analytics Manager at P&G and a Director of Revenue Management Solutions. Dr. Liu's pioneering research and grants focus on applying AI and machine learning to critical areas like consumer perceptions and sustainable digital economies, translating advanced analytics into real-world impact.

 

Course Completion requirements

Participants are required to achieve at least 75% attendance and pass any prescribed examinations/assessments or submit any course/project work (if any) under the course requirement.

  • Participants are required to complete all surveys and feedbacks related to the course
  • The course fees are reviewed annually and may be revised. The University reserves the right to adjust the course fees without prior notice.
  • Singapore University of Social Sciences reserves the right to amend and/or revise the above schedule without prior notice

 

Course Fees, payment and refund policy

 International Participants Singapore Citizens (below 40yrs), Permanent Residents Singapore Citizens (40yrs and above) SkillsFuture Mid - Career Enhanced Subsidy Enhanced Training Support for SME (Singaporean and PRs)
Course Fees (A) $3,168.00 $2,640.00 $2,640.00 $2,640.00
SSG Grant (70%) (B)  $1,848.00 $1,848.00 $1,848.00
Nett Course fees (A) - (B) = (C) $3,168.00 $792.00 $792.00 $792.00
9% GST on Nett course fees (D) $285.12 $71.28 $71.28 $71.28
Total nett course fees payable including GST (C) + (D) $3,453.12 $863.28 $863.28 $863.28
Less additional funding if eligible under various schemes (F) $-   $-   $528.00 $528.00
Total nett course fees payable including GST, after additional funding form the various schemes (E) - (f) = (H) $3,453.12 $863.28 $335.28 $335.28

For the various payment mode, please refer here.
For the refund policy, please refer here. 

 

For clarification, please contact the SUSS Academy via the following:
Telephone: +65 6248 0263
Email: [email protected]