Fundamentals of Data Science

Singapore University of Social Sciences

Fundamentals of Data Science (DSM301)

Applications Open: To be confirmed

Applications Close: To be confirmed

Next Available Intake: To be confirmed

Course Types: To be confirmed

Language: English

Duration: 6 months

Fees: To be confirmed

Area of Interest: Science and Technology

Schemes: To be confirmed

Funding: To be confirmed

School/Department: School of Science and Technology

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Synopsis

DSM301 Fundamentals of Data Science provides students with a comprehensive exploration of end-to-end pipelines, spanning from data ingestion to model output. This course is designed to equip students with essential knowledge and skills, guiding them through a transformative journey into the field of data science. From mastering the basics of databases to machine learning interpretability, students will engage with a diverse range of topics essential for a successful career in data science.

Level: 3
Credit Units: 5
Presentation Pattern: EVERY JAN

Topics

Introduction to SQL databases
SQL basics
Advanced SQL queries
Data cleaning and pre-processing
Feature engineering and extraction
Introduction to machine learning
Introduction to machine learning
Supervised machine learning algorithms
Cross-validation techniques
Model evaluation metrics
Why model interpretability matters
SHapley Additive exPlanations (SHAP)
Local Interpretable Model-agnostic Explanations (LIME)

Learning Outcome

Show the basic structure and syntax of SQL queries.
Discuss varies techniques for feature engineering.
Demonstrate the principles and techniques of machine learning cross-validation techniques.
Use complex SQL queries to retrieve and pre-process data for effective data manipulation.
Implement appropriate metrics for model evaluation.
Interpret machine learning models using SHAP or LIME.