Course Title | Data Analysis with Computer | ||
Course Code | MH3511 | ||
Offered | Study Year 3, Semester 2 | ||
Course Coordinator | Yeo Kwee Poo (Asst Prof) | kweepoo@ntu.edu.sg | 6513-7456 |
Pre-requisites | MH2500 | ||
AU | 3 | ||
Contact hours | Laboratories: 26, Lectures: 26 | ||
Approved for delivery from | AY 2019/20 semester 2 | ||
Last revised | 10 Dec 2019, 09:04 |
In today's business, data analysis plays an important role in making decisions more scientific and helping the business achieve effective operation. By closely examining data we can find patterns to perceive information, and the information can be used to enhance knowledge. This course provides basic concepts for data analysis with the usage of the R programming language. You will learn the skills of plotting, summarising, making inferences, and presenting various types of data.
Upon successfully completing this course, you should be able to:
Basic of R programming • Basic R syntax • Write mathematical expression in R language • Variable, vector, matrix and dataframe, and their operations in R • Importing dataset into R, subsetting dataset • Basic loops in R
Describing Data • Basic parameters such as mean, median, standard deviation, variance, inter-quartile range • Boxplot, histogram, stem-leaf plot • Normality checks, qq-plot, outlier, transformation
Statistical Inference • Sample, sampling distribution • Central Limit Theorem • Confidence Interval • Statistical hypothesis testing, Type I and Type II errors, p-value
Categorical Data • Proportion estimate, testing of proportion parameter • Goodness-of-fit test • Two-way contingency table • Paired 2-way contingency table
Multiple Samples • Two independent samples, inference on mean difference • Two dependent samples • Multiple (>2) independent samples, ANOVA test • Multiple (>2) dependent samples
Nonparametric Tests • Quantile test • Wilcoxon rank-sum test • Kruskal-Wallies Test • Sign test, Wilcoxon signed-rank test • Friedman test
Correlation and Regression • Correlation coefficient, its confidence interval and statistical test • Simple linear regression model • Inference on the parameters of linear model • Prediction inference • Model checking
Component | Course ILOs tested | SPMS-MAS Graduate Attributes tested | Weighting | Team / Individual | Assessment Rubrics |
---|---|---|---|---|---|
Continuous Assessment | |||||
Laboratories | |||||
Written Report | 1, 2, 3, 4, 5, 6, 7, 8, 9 | 1. a, b, c, d 2. a, b, d 3. a, b 4. a 5. a | 20 | individual | See Appendix for rubric |
Mid-semester Quiz | |||||
Short Answer Questions 1 | 1, 2, 3, 4 | 1. a, b, c, d 2. a, b, d 3. a | 15 | individual | See Appendix for rubric |
Short Answer Questions 2 | 5, 6, 7 | 1. a, b, c, d 2. a, b, d 3. a | 15 | individual | See Appendix for rubric |
Examination (2 hours) | |||||
Short Answer Questions | 1, 2, 3, 4, 5, 6, 7, 8, 9 | 1. a, b, c 2. b, d 3. a | 50 | individual | See Appendix for rubric |
Total | 100% |
These are the relevant SPMS-MAS Graduate Attributes.
1. Competence
a. Independently process and interpret mathematical theories and methodologies, and apply them to solve problems
b. Formulate mathematical statements precisely using rigorous mathematical language
c. Discover patterns by abstraction from examples
d. Use computer technology to solve problems, and to communicate mathematical ideas
2. Creativity
a. Critically assess the applicability of mathematical tools in the workplace
b. Build on the connection between subfields of mathematics to tackle new problems
d. Critically analyse data from a multitude of sources
3. Communication
a. Present mathematics ideas logically and coherently at the appropriate level for the intended audience
b. Work in teams on complicated projects that require applications of mathematics, and communicate the results verbally and in written form
4. Civic-mindedness
a. Develop and communicate mathematical ideas and concepts relevant in everyday life for the benefits of society
5. Character
a. Act in socially responsible and ethical ways in line with the societal expectations of a mathematics professional, particularly in relation to analysis of data, computer security, numerical computations and algorithms
For midterm Quizzes: Feedback on common mistakes and the level of difficulty of the problems will be given.
For Project Work: You will receive individual written and/or verbal feedback about their project.
For Final Exam: the Examiner's Report will be provided.
Laboratories (26 hours) | This will help to develop your problem solving and computing skills, reinforce the understanding of the concepts and notions. |
Lectures (26 hours) | This is intended to help you understand the motivation and definitions of the concepts and notions, approaches to solving problems in pursuant to learning outcomes. |
1 Hothorn & Everitt: A Handbook of Statistical Analysis Using R, 3rd Edition, CRC Press 2014. ISBN-10: 1482204584, ISBN-13: 978-1482204582
2 Michael J. Crawley: Statistics, An Introduction using R, Wiley 2005. ISBN-10: 0470022981, ISBN-13: 978-0470022986
(1) General
You are expected to complete all lab assignments, take the quizzes, and complete the project. You are expected to take responsibility to follow up with course notes, assignments and course related announcements if you are absent.
(2) Absenteeism
Absence from quizzes and examination without a valid reason will affect your overall course grade. Valid reasons include falling sick supported by a medical certificate and participation in NTU’s approved activities supported by an excuse letter from the relevant bodies.
(3) Absence Due to Medical or Other Reasons
If you are sick and not able to attend the midterm, you have to submit the original Medical Certificate (or another relevant document) to the administration to obtain official leave. In this case, the missed assessment component will not be counted towards the final grade. There are no make-up midterm.
Good academic work depends on honesty and ethical behaviour. The quality of your work as a student relies on adhering to the principles of academic integrity and to the NTU Honour Code, a set of values shared by the whole university community. Truth, Trust and Justice are at the core of NTU’s shared values.
As a student, it is important that you recognize your responsibilities in understanding and applying the principles of academic integrity in all the work you do at NTU. Not knowing what is involved in maintaining academic integrity does not excuse academic dishonesty. You need to actively equip yourself with strategies to avoid all forms of academic dishonesty, including plagiarism, academic fraud, collusion and cheating. If you are uncertain of the definitions of any of these terms, you should go to the Academic Integrity website for more information. Consult your instructor(s) if you need any clarification about the requirements of academic integrity in the course.
Instructor | Office Location | Phone | |
---|---|---|---|
Yeo Kwee Poo (Asst Prof) | SPMS-MAS-04-16 | 6513-7456 | kweepoo@ntu.edu.sg |
Week | Topic | Course ILO | Readings/ Activities |
---|---|---|---|
1 | Basic of R programming | 1 | Lecture notes |
2 | Basic of R programming | 1 | Lecture notes / Lab Assignment |
3 | Describing Data | 2, 3, 4 | Lecture notes / Lab Assignment |
4 | Statistical Inference | 5, 6, 7 | Lecture notes / Lab Assignment |
5 | Statistical Inference | 5, 6, 7 | Lecture notes / Lab Assignment |
6 | Categorical Data | 5, 6, 7 | Lecture notes / Lab Assignment |
7 | Categorical Data | 5, 6, 7 | Lecture notes / Lab Assignment |
8 | Multiple Samples | 5, 6, 7 | Lecture notes / Lab Assignment |
9 | Multiple Samples | 5, 6, 7 | Lecture notes / Lab Assignment |
10 | Nonparametric Tests | 5, 6, 8 | Lecture notes / Lab Assignment |
11 | Nonparametric Tests | 5, 6, 8 | Lecture notes / Lab Assignment |
12 | Correlation and Regression | 5, 6, 9 | Lecture notes / Lab Assignment |
13 | Correlation and Regression | 5, 6, 9 | Lecture notes / Lab Assignment |
Criteria | Standards | ||
Fail standard | Pass standard | High standard | |
Methods of approach | · Using methods that are irrelevant or do not apply to the given problem. · Invoking theorems whose conditions are not satisfied. | · Using relevant methods that help solve the problem. · Invoking theorems whose conditions are satisfied. | Finding methods and utilizing theorems that are both relevant and effective |
Validity of reasoning | Reasoning is logically invalid. | Reasoning is logically valid. | Reasoning is logically valid and effective. |
Clarity of argument | Reasoning is poorly explained or not explained at all. | Reasoning is clear but may contain some gaps. | Reasoning is clear, precise with no or insignificant gaps. |
Criteria | Standards | ||
Fail standard | Pass standard | High standard | |
Methods of approach | · Using methods that are irrelevant or do not apply to the given problem. · Invoking theorems whose conditions are not satisfied. | · Using relevant methods that help solve the problem. · Invoking theorems whose conditions are satisfied. | Finding methods and utilizing theorems that are both relevant and effective |
Validity of reasoning | Reasoning is logically invalid. | Reasoning is logically valid. | Reasoning is logically valid and effective. |
Clarity of argument | Reasoning is poorly explained or not explained at all. | Reasoning is clear but may contain some gaps. | Reasoning is clear, precise with no or insignificant gaps. |
Criteria | Standards | ||
Fail standard | Pass standard | High standard | |
Methods of approach | · Using methods that are irrelevant or do not apply to the given problem. · Invoking theorems whose conditions are not satisfied. | · Using relevant methods that help solve the problem. · Invoking theorems whose conditions are satisfied. | Finding methods and utilizing theorems that are both relevant and effective |
Validity of reasoning | Reasoning is logically invalid. | Reasoning is logically valid. | Reasoning is logically valid and effective. |
Clarity of argument | Reasoning is poorly explained or not explained at all. | Reasoning is clear but may contain some gaps. | Reasoning is clear, precise with no or insignificant gaps. |
Criteria | Standards | ||
Fail standard | Pass standard | High standard | |
Methods of approach | · Using methods that are irrelevant or do not apply to the given problem. · Invoking theorems whose conditions are not satisfied. | · Using relevant methods that help solve the problem. · Invoking theorems whose conditions are satisfied. | Finding methods and utilizing theorems that are both relevant and effective |
Validity of reasoning | Reasoning is logically invalid. | Reasoning is logically valid. | Reasoning is logically valid and effective. |
Clarity of argument | Reasoning is poorly explained or not explained at all. | Reasoning is clear but may contain some gaps. | Reasoning is clear, precise with no or insignificant gaps. |