Data Wrangling Course | SkillsFuture CET Courses

Singapore University of Social Sciences

Data Wrangling (ANL503)

Applications Open: 01 October 2024

Applications Close: 15 November 2024

Next Available Intake: January 2025

Course Types: Modular Graduate Course, SkillsFuture Series

Language: English

Duration: 6 months

Fees: $2640 View More Details on Fees

Area of Interest: Business Administration

Schemes: Alumni Continuing Education (ACE), Postgraduate Alumni Continuing Education (PACE)

Funding: SkillsFuture

School/Department: School of Business

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Synopsis

ANL503 Data Wrangling aims to equip students with advanced data acquisition and manipulation skills and techniques. Using MySQL, Python, and R, students learn the ins and outs of acquiring data from relational database systems (using SQL), web scraping, and web APIs (using Python) in a scalable and reproducible manner. Students also learn how to transform raw data into formats suitable for deeper analytics (using MySQL, Python, and R). The course rounds up with an introduction to visualisation (using R).

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 and statistics
Introduction to SQL and data manipulation with the SELECT statement
Combining data from multiple sources with union and joins
Understanding regular expressions
Introduction to R
Data manipulation with R
Essential principles of data visualisation
Introduction to Python programming
Practical Python for data acquisition, mangling, and reporting
Handling web APIs and web scraping
Using Python for scalable spreadsheet data acquisition

Learning Outcome

Assess appropriateness of database designs based on characteristics of data and analytics needs
Critique data visualisations constructively
Construct suitable SQL queries to acquire and reshape data
Assemble effective data flows in MySQL, Python, and R as part of a reproducible workflow process
Create Python scripts to automate the acquisition and processing of web and spreadsheet data
Design and implement effective data visualisations in R