Syllabus ( MATH 411 )
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Basic information
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| Course title: |
Introduction to Data Analysis |
| Course code: |
MATH 411 |
| Lecturer: |
Assoc. Prof. Dr. Selçuk TOPAL
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| ECTS credits: |
5 |
| GTU credits: |
3 () |
| Year, Semester: |
4, Spring |
| Level of course: |
First Cycle (Undergraduate) |
| Type of course: |
Elective
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| Language of instruction: |
English
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| Mode of delivery: |
Face to face
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| Pre- and co-requisites: |
Math 113 or Math 116 |
| Professional practice: |
No |
| Purpose of the course: |
Collecting, organizing, summarizing and analyzing the data for any research. |
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Learning outcomes
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Upon successful completion of this course, students will be able to:
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Understand the importance of the data for any research.
Contribution to Program Outcomes
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Communicating between mathematics and other disciplines, and building mathematical models for interdisciplinary problems.
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Describing, formulating, and analyzing real-life problems using mathematical and statistical techniques.
Method of assessment
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Written exam
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Organize and summarize the data.
Contribution to Program Outcomes
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Communicating between mathematics and other disciplines, and building mathematical models for interdisciplinary problems.
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Describing, formulating, and analyzing real-life problems using mathematical and statistical techniques.
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Adapt to a fast-changing technological environment, improving their knowledge and abilities constantly.
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Exhibiting professional and ethical responsibility.
Method of assessment
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Written exam
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Analyse the data and estimate or forecast from the knowledge of the data.
Contribution to Program Outcomes
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Communicating between mathematics and other disciplines, and building mathematical models for interdisciplinary problems.
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Describing, formulating, and analyzing real-life problems using mathematical and statistical techniques.
Method of assessment
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Written exam
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Contents
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| Week 1: |
Introduction to Data Analysis. Population, sample, measure. |
| Week 2: |
Organizing data. |
| Week 3: |
Graphs. Frequency tables. |
| Week 4: |
Descriptive statistics for location. |
| Week 5: |
Descriptive statistics for scale. |
| Week 6: |
Relation analysis for qualitative data. Midterm Exam 1. |
| Week 7: |
Relation analysis for quantitative data. |
| Week 8: |
Normal Distribution and Its Applications. |
| Week 9: |
Estimation and Hypothesis Testing. |
| Week 10: |
Simple Linear Regression Analysis. |
| Week 11: |
Multiple Regression Analysis. |
| Week 12: |
ANOVA models. Midterm Exam 2. |
| Week 13: |
Time Series. |
| Week 14: |
SPSS applications. |
| Week 15*: |
- |
| Week 16*: |
Final Exam |
| Textbooks and materials: |
Andrew Gelman, Jennifer Hill, 2006, Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press |
| Recommended readings: |
Gordon S. Linoff, 2012, Data Analysis Using SQL and Excel, Wiley.
Jeff Leek, 2005, The Elements of Data Analytic Style, Kindle Edition. |
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* Between 15th and 16th weeks is there a free week for students to prepare for final exam.
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Assessment
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| Method of assessment |
Week number |
Weight (%) |
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| Mid-terms: |
6,12 |
60 |
| Other in-term studies: |
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0 |
| Project: |
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0 |
| Homework: |
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0 |
| Quiz: |
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0 |
| Final exam: |
16 |
40 |
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Total weight: |
(%) |
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Workload
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| Activity |
Duration (Hours per week) |
Total number of weeks |
Total hours in term |
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| Courses (Face-to-face teaching): |
3 |
14 |
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| Own studies outside class: |
3 |
14 |
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| Practice, Recitation: |
0 |
0 |
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| Homework: |
0 |
0 |
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| Term project: |
0 |
0 |
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| Term project presentation: |
0 |
0 |
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| Quiz: |
0 |
0 |
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| Own study for mid-term exam: |
10 |
2 |
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| Mid-term: |
4 |
2 |
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| Personal studies for final exam: |
10 |
1 |
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| Final exam: |
2 |
1 |
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Total workload: |
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Total ECTS credits: |
* |
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* ECTS credit is calculated by dividing total workload by 25.
(1 ECTS = 25 work hours)
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