SDST2604 Introduction to R/Python programming and elementary data analysis (6 credits) Academic Year 2025
Offering Department SCDS (Department of Statistics and Actuarial Science) Quota ---
Course Co-ordinator Prof M Zhang, SCDS (Department of Statistics and Actuarial Science) < mzhang18@hku.hk >
Teachers Involved (Prof M Zhang,Statistics & Actuarial Science)
Course Objectives This course is designed to provide a first-level introduction to Python programming for statistics. This course focuses on learning the basic programming skills in Python with examples and applications in elementary statistical analysis. The programming skills involved can be applied to input and output of data sets, work with different data types, manipulation and transformation of data, random sampling, descriptive data analysis, and production of professional summary reports with high-quality graphs.
Course Contents & Topics 1. Python basics: first steps; language essentials.
2. The Python environment and libraries such as pandas, numpy, scipy.stats, matplotlib, seaborn, etc.
3. Probability and distributions: random sampling; probability calculations and combinatorics; discrete distributions; continuous distributions.
4. Descriptive statistics and graphics: summary statistics for a single group; graphical display of distributions; summary statistics by groups; graphics for grouped data; graphical display of tables.
5. Simple linear regression: residuals and fitted values; prediction and confidence bands; correlation.
Course Learning Outcomes
On successful completion of this course, students should be able to:

CLO 1 access online help and documents for Python
CLO 2 use Python to input data, perform data transformation and merging, output data
CLO 3 summarize data in tables and graphs for descriptive data analysis
CLO 4 work with numeric, character, and other unstructured data types
CLO 5 be able to write functions, loops and control flows
CLO 6 perform data management in Python
CLO 7 perform Monte Carlo simulations to validate statistical concepts
Pre-requisites
(and Co-requisites and
Impermissible combinations)		 Pass or already enrolled in SDST1600 or MATH1821 or (MATH1851 and MATH1853).
Only for students admitted in 2025 and thereafter.
Course Status with Related Major/Minor/Professional Core 2025 Minor in Risk Management ( Disciplinary Elective )
2025 Minor in Statistics ( Disciplinary Elective )
Course to PLO Mapping
Offer in 2025 - 2026 Y        1st sem    Examination No Exam     
Offer in 2026 - 2027 Y
Course Grade A+ to F
Grade Descriptors
A Demonstrate thorough mastery at an advanced level of extensive knowledge and skills required for attaining all the course learning outcomes. Show strong analytical and critical abilities and logical thinking, with evidence of original thought, and ability to apply knowledge to a wide range of complex, familiar and unfamiliar situations. Apply highly effective organizational and presentational skills.
B Demonstrate substantial command of a broad range of knowledge and skills required for attaining at least most of the course learning outcomes. Show evidence of analytical and critical abilities and logical thinking, and ability to apply knowledge to familiar and some unfamiliar situations. Apply effective organizational and presentational skills.
C Demonstrate general but incomplete command of knowledge and skills required for attaining most of the course learning outcomes. Show evidence of some analytical and critical abilities and logical thinking, and ability to apply knowledge to most familiar situations. Apply moderately effective organizational and presentational skills.
D Demonstrate partial but limited command of knowledge and skills required for attaining some of the course learning outcomes. Show evidence of some coherent and logical thinking, but with limited analytical and critical abilities. Show limited ability to apply knowledge to solve problems. Apply limited or barely effective organizational and presentational skills.
Fail Demonstrate little or no evidence of command of knowledge and skills required for attaining the course learning outcomes. Lack of analytical and critical abilities, logical and coherent thinking. Show very little or no ability to apply knowledge to solve problems. Organization and presentational skills are minimally effective or ineffective.
Communication-intensive Course N
Course Type Lecture-based course
Course Teaching
& Learning Activities
Activities Details No. of Hours
Lectures 36.0
Tutorials 12.0
Reading / Self study 100.0
Assessment Methods
and Weighting
Methods Details Weighting in final
course grade (%)		 Assessment Methods
to CLO Mapping
Assignments Coursework (assignments, tutorials, and class test(s)) 50.0 1,2,3,4,5,6,7
Project reports 50.0 1,2,3,4,5,6,7
Required/recommended reading
and online materials		 - Fabio Neli. Python Data Analytics With Pandas, NumPy, and Matplotlib, Second Edition. Apress Publisher. 2018.
ISBN (electronic) 978-1-4842-3913-1
ISBN (pbk):978-1-4842-3912-4
- There are also many on-line learning materials that fit well the contents of this course (details provided by the course instructor).
Course Website http://moodle.hku.hk
Additional Course Information NIL