Data Literacy
BT1101 Introduction to Business Analytics
This course provides students with an introduction to the fundamental concepts and tools needed to understand the emerging role of business analytics and data science applications in business and non-profit organisations. Students will learn how to apply basic business analytics and data science/analytics tools (such as R) to large real-life datasets in different contexts, and how to effectively use and interpret analytic models and results for making better and more well-informed business decisions. This course will provide both the organisational and technical aspects of business analytics and serves to provide students with a broad overview of how and why business analytics can be implemented in organisations, the various approaches and techniques that could be adopted for different organisational objectives and issues.
DSA1101 Introduction to Data Science
The abundance of data being harvested from various sectors of today’s society increases the demand for skilled data science practitioners. This course introduces foundational data science concepts to prepare students for tackling real-world data analytic challenges. Major topics include basic concepts in probability and statistics, data manipulation, supervised and unsupervised learning, model validation and big data analysis, alongside special topics discussed in guest lectures delivered by practicing data scientists from government and industry. Throughout the course, students will learn fundamental R programming skills to implement and apply the data science methods in motivating real-world case studies from diverse fields.
DSE1101 Introductory Data Science for Economics
The vast amount of data generated by economic activities such as consumption, production and investment increases the demand for data science practitioners with a knowledge of economic analysis. This course introduces foundational data science concepts to prepare students for tackling data analytic challenges in economics and related fields such as finance, public policy and society. Topics include basic concepts in probability and statistics, data manipulation, supervised and unsupervised learning, model validation and big data analysis.
Students will learn fundamental R programming skills to implement and apply the data science methods in motivating real-world case studies from economics and related fields.
The assessment of the course will be based on quizzes, individual assignments, individual projects, a midterm examination and a final examination. For more information, see the course's LumiNUS page (for enrolled students), or NUSMods.
Introduction to Data Science and Economics (XDP)
GEA1000 Quantitative Reasoning with Data
This course aims to equip undergraduate students with essential data literacy skills to analyse data and make decisions under uncertainty. It covers the basic principles and practice for collecting data and extracting useful insights, illustrated in a variety of application domains. For example, when two issues are correlated (e.g., smoking and cancer), how can we tell whether the relationship is causal (e.g., smoking causes cancer)? How can we deal with categorical data? Numerical data? What about uncertainty and complex relationships? These and many other questions will be addressed using data software and computational tools, with real-world data sets.
ST1131 Introduction to Statistics and Statistical Computing
This course introduces students to basic concepts and methods of statistics that will enable them to perform appropriate data analyses to uncover meaningful insights. The statistical software R is taught alongside the material to introduce statistical computing. Students will learn to load raw data, make numerical and graphical summaries of data, and conduct various estimation and testing procedures. Topics include programming in R, descriptive statistics, concepts of probability, random variables and probability distributions, sampling distribution, statistical estimation, hypothesis testing, linear regression, and applications to real-world problems.
Notes:
- Students who have declared a primary major in Data Science and Economics before the stipulated deadline for each Academic Year (AY) will be pre-allocated DSE1101 which can fulfil the Data Literacy requirement. DSE1101 can only be read by students in the Data Science and Economics programme.
- Students who have declared a primary major in Data Science and Analytics or Statistics will be pre-allocated their respective gateway courses which can fulfil the Data Literacy requirement.
- The following students will be pre-allocated GEA1000:
- Students who did not declare a primary major in Data Science and Economics before the stipulated deadline for each AY
- Students who did not declare a primary major in Data Science and Analytics or Statistics
- Students who did not declare a second major/minor in Data Analytics/Statistics.
However, if students would like to read ST1131/DSA1101/BT1101 to fulfil the Data Literacy requirements, they can drop GEA1000 and select ST1131/DSA1101/BT1101 instead. Please appeal via the ModReg system under "Drop Lec/Tut allocated by admin". More details can be found here.
- Students reading the primary Major/Second Major/Minor in Statistics are advised to read ST1131 instead of GEA1000 to fulfil the Data Literacy requirement.
- Students reading the primary Major in Data Science and Analytics or Second Major in Data Analytics are advised to read DSA1101 instead of GEA1000 to fulfil the Data Literacy requirement.
- Students reading the Data Science and Economics Cross-Disciplinary Programme are advised to read DSE1101 instead of GEA1000 to fulfil the Data Literacy requirement.
- IE1111R Industrial and Systems Engineering Principles and Practice I can only be read by students in the Industrial and Systems Engineering programme under the College of Design and Engineering to fulfil the Data Literacy pillar.
