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ADVANCED TECHNIQUE IN DATA ANALYSIS
COURSE SYLLABUS
ADVANCED TECHNIQUE IN DATA ANALYSIS
I. Course Information:
Program Title: Master of Marketing Studies and Actions
Course Title: Advance technique in data analysis
Course Code:
Number of Credits:
II. Lecturer’s Information:
III. Course Description
This course provides students with fundamental knowledge on advance technique in data analysis. In specific, the course includes the following basic contents: data classification techniques (focusing on current emerging decision tree and neural network methods), data clustering techniques (HAC and k-means methods), and methods evaluate the results of the classification model, clustering .... In addition, the course also presents some applications of these techniques in marketing and forecasting of businesses and organizations in general.
IV. Course Objectives
Upon completion of this subject, students should be able to:
1. Mastering and proficient application of data classification techniques
2. Master and masterful application of data clustering techniques
3. Use the R tool to build data analysis models
4. Evaluate the effectiveness of models built on a given data set
5. Solve, compare and evaluate data analysis techniques for a problem with a real data set
6. Proficient in applications such as analyzing user views, clustering customers, predicting loyal customers, customers will leave in the future, …
V. Main Topics
1. Introduction to data classification techniques
a. Decision tree
b. Neural networks
2. Introduction to data clustering techniques
a. K-means
b. HAC
3. The methods to evaluate the effectiveness of predictive models
4. Practice building and evaluating models with R
5. Applications in general marketing problem
VI. Textbooks and Recommended readings
[1] Shmueli, G., Bruce, P. C., Yahav, I., Patel, N. R., & Lichtendahl Jr, K. C. (2017). Data mining for business analytics: concepts, techniques, and applications in R. John Wiley & Sons.
[2] Wu, X., Kumar, V., Quinlan, J. R., Ghosh, J., Yang, Q., Motoda, H., … & Zhou, Z. H. (2008). Top 10 algorithms in data mining. Knowledge and information systems, 14(1), 1-37.
VII. Teaching Methods
| No | Activities | Purpose, methods |
| 1 | Introduction to data classification techniquesa. Decision treeb. Neural networks | Lecture note |
| 2 | Introduction to data clustering techniquesa. K-meansb. HAC | Lecture note |
| 3 | The methods to evaluate the effectiveness of predictive models | Lecture note |
| 4 | Practice building and evaluating models with R | Lecture note |
| 5 | Applications in general marketing problem | Lecture note |
| Final exam |
VII. Assessment Methods
| No | Assessment | Grade | Note |
| 1. | Attendance | Students who present less than 70% of the course hours in this course, will not be eligible to take the final exam and must repeat this course. | |
| 2. | Group assignment | 20% | Group assignment include 3-4 students, presenting results in class and submitting group homework reports |
| 3. | Final exam | 80% | Group project |
| Total | 100% |
IX. Other Requirements
Plagiarism is prohibited under any circumstances. Students who commit plagiarism are subjected to failure of the class and possible dismissal from the university.
Follow the procedures during the exams
Follow the policies by International School, Vietnam National University, Hanoiand Nantes University, France
Nguyen The Hoan-
