Page 93 - Handbook Bachelor Degree of Science Academic Session 20212022

Faculty of Science Handbook, Academic Session 2021/2022

SIT3024 STATISTICAL CONSULTANCY AND DATA
References: ANALYSIS
1. Deitel, P.J. & Dietal, H. (2019). Introduction to Python
for Computer Science and Data Science: Learning to
Program with AI, Big Data and the Cloud. UK: Pearson Introduction to consultancy activities and consulting
Education. methods. Related problems and issues. Exposure to the use
2. Müller, A.C. & Guido, S. (2016). Introduction to Machine of primary and secondary data from various sources.
Application of suitable statistical methods such as
Learning with Python: A Guide for Data Scientists. multivariate analysis, regression and time series in the
Sebastopol, CA: O'Reilly Media.
3. Raschka, S. & Mirjalili, V. (2019). Python Machine analysis of real data. Producing report and presenting the
Learning: Machine Learning and Deep Learning with findings that suit the needs of the client.
Python, scikit-learn, and TensorFlow 2 (3rd ed.). Assessment:
Birmingham, UK: Packt Publishing.
Continuous Assessment: 100%
References:
SIT3021 INDUSTRIAL TRAINING
1. Boen, J.R. & Zahn, D.A. (1982). The Human Side of
Statistical Consulting. Belmont, CA: Lifetime Learning
Candidates are required to spend a minimum of 16 weeks
working with selected companies in selected areas of Publications.
industry. 2. Hand, D.J. & Everitt, B.S. (1987). The Statistical
Consultant in Action. Cambridge: Cambridge
University Press.
Assessment: 3. Tufte, E.R. (2001). The Visual Display of Quantitative
Continuous Assessment: 100% nd
Information. (2 ed.). Connecticut, USA: Graphics
Press.
References:
Universiti Malaya Guidebook untuk Latihan Industri 4. Wickham, H. & Grolemund, G. (2017). R for Data
Science: Import, Tidy, Transform, Visualize, and
Model Data. Sebastopol, CA: O'Reilly Media.
SIT3022 PROBABILITY THEORY
SIT3025 STATISTICAL SCIENCE PROJECT
Probability measure and space, sigma field. Lebesgue
integration. Random variables, measurability, Subject to supervising lecturer.
independence. Distribution functions. Inequalities,
characteristic functions. Various modes of convergence of
sequences of random variables. Classical limit theorems. Assessment:
Examples of applications. Continuous Assessment: 100%
References:
Assessment:
Continuous Assessment: 50% Refer to supervising lecturer.
Final Examination: 50%
References:
1. Billingsley, P. (1995). Probability and Measure (3rd ed.).
New York: John Wiley.
2. Durrett, R. (2019). Probability: Theory and Examples
(5th ed.). Cambridge: Cambridge University Press.
3. Karr, A. F. (1993). Probability. New York: Springer-
Verlag.
4. Rosenthal, J.S. (2006). A First Look at Rigourous
nd
Probability Theory (2 ed.). Singapore: World Scientific
Publishing Company.

SIT3023 STATISTICAL LABORATORY
Use of functions and commands in statistical packages for
exploratory data analysis, modelling and statistical
inferences. Coding and programming using statistical
software to solve statistical problems.

Assessment:
Continuous Assessment: 50%
Final Examination: 50%
References:
1. Crawley, M. (2019). Statistics: An Introduction using R
(2nd ed.). Chichester, UK: John Wiley & Sons.
2. Matloff, N. (2011). The Art of R Programming: A Tour
of Statistical Software Design. San Francisco, CA: No
Starch Press.
3. Deitel, P.J. & Dietal, H. (2019). Introduction to Python
for Computer Science and Data Science: Learning to
Program with AI, Big Data and the Cloud. UK: Pearson
Education.

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