The objective of this course is to help students develop competences on statistical techniques needed for data analysis, and various data mining techniques and algorithms used in practical problems that require processing big data for decision making purpose.

The students on the completion of this course would be able to
• Apply various inferential statistical analysis techniques to describe data sets and withdraw useful conclusions from the data sets (e.g., confidence interval, hypothesis testing)
• Apply data visualization techniques and key data mining techniques (e.g., classification analysis, associate rule learning, anomaly/outlier detection, clustering analysis, regression analysis) in dealing with big data sets
• Implement the analytic algorithms for practical data sets
• Perform large scale analytic projects in various industrial sectors

None

Module 1: Fundamental Data Analysis
I. Basic Statistical Concepts
1. Descriptive Statistics
2. Statistical Inferences
3. Data Measurement
4. Measures of Central Tendency and Dispersion
5. Common Statistical Graphs
6. Determination of Outliers
II. Statistical Inferences
1. Point Estimation and Required Properties of Point Estimators
2. Interval Estimations for Mean, Proportion and Variance of Population
3. Sample Size Determination
III. Hypothesis Testing
1. Hypothesis Testing for Mean, Proportion and Variance of Population –
Single Sample Test
2. Hypothesis Testing for Mean, Proportion and Variance of Population –
Two Samples Test
3. Type I and Type II Errors – Power of the Test
4. Observed Significance Level
Module 2: Data Visualization
IV. Data Visualization
1. Introduction to Data Visualization
2. Basic Charts for Numerical Data and Categorical Data
3. Distribution Plots
4. Multivariate Charts: Combo Chart, Combination Chart, Stacked Column
Chart
V. Data Dashboard
1. What is a Data Dashboard?
2. Applications and Benefits of Data Dashboard
3. Design and Construct a Data Dashboard
Module 3: Key Data Mining Techniques
VI. Regression Analysis
1. Linear Regression and Least Square Method
2. Residual Analysis
3. Multiple Regression
4. Goodness of Fit Tests
VII. Data Classification
1. k-Nearest Neighbor Algorithm for Estimation and Prediction
2. Distance Functions: Euclidian, Manhattan, Minkowski, Min-Max Normalization, Z-Score Standardization
3. Logistics Regression
4. Bayesian Networks
5. Model Evaluation Measures for Classification Task
VIII. Data Clustering
1. Hierarchical Clustering Method
2. k-Means Clustering
3. Measuring Cluster Goodness: The Silhouette Method and The Pseudo-F Statistic
IX. Association Rules
1. Affinity Analysis
2. The a Priori Algorithm – Generating Frequent Itemsets
3. The a Priori Algorithm – Generating Association Rules
4. Measure the Usefulness of Associate Rules

None

No designated textbook, but class notes and handouts will be provided

1. Larose, D.T. and Larose, C.D., Data Mining and Predictive Analytics, 2nd edition, Wiley, 2015
2. Shmueli, G., Bruce, P.C., Yahav, I., Patel, N.R. and Lichtendahl Jr., K.C., Data Mining for Business Analytics – Concepts, Techniques, and Application in R, 1st edition, Wiley, 2018
3. Ankam, V., Big Data Analytics, Packt, 2016
4. Walkowiak, S., Big Data Analytics with R, 1st edition, Packt, 2016
5. Grolemund, G., Hands-on Programming with R, 1st edition, O’Reilly, 2014
6. Wickham, H. and Grolemund, G., R for Data Science, 1st edition, O’Reilly, 2017
7. Wexler, S., Shaffer, J. and Cotgreave, A., The Big Book of Dashboards: Visualizing Your Data Using Real-World Business Scenarios, 1st edition, Wiley, 2017
8. O’Cornor, E., Microsoft Power BI Dashboards Step by Step, 1st edition, Practice Files, 2019

1. International Journal of Data Science and Analytics, Springer.
2. International Journal of Experimental Design and Process Optimisation, Inderscience.
3. Journal of the Operational Research Society, Palgrave Macmillan.
4. Management Science, Informs.

Lectures: 45 hours
Tutorials/Group Discussions: 60 hours
Self-study: 45 hours
Group project: 30 hours

The teaching is done via lectures by the instructor. Tutorial sessions are conducted on the use of tools in each subject. The learning methods include group discussion, individual/group assignment and group project/case study.

The final grade will be computed according to the following
weight distribution: Mid-semester examination 20%, assignments and group projects 50%, final examination 30%. In final grading,
An “A” would be awarded if a student shows a deep understanding of the knowledge learned through home assignments, project works, and exam results.
A “B” would be awarded if a student shows an overall understanding of all topics.
A “C” would be given if a student meets below average expectation in understanding and application of basic knowledge.
A “D” would be given if a student does not meet expectations in both understanding and application of the given knowledge.