Institute of Science and Technology · Computer Science and Engineering (%30 English) · Doctorate
Course Objective
The course aims to introduce graduate students to the field of data analysis and visualization. The main objective of the course is to equip you with the tools that will enable you to be an independent data analyst by covering state-of-the-art in data modeling, analysis and visualization techniques.
Course Content
The course includes both theoretical and practical topics for students. Python programming language is used to program different modeling and visualization techniques. At the end of the course, students will be able to visualize data effectively by performing quick data analysis via designing visualization system for large datasets. Data transformations, time series analysis, exploratory querying, exploratory spatial data analysis and statistical graphics will be covered mainly.
Required Resources
Data Analysis and Visualization Using Python by Dr. Ossama Embarak (2018)
ISBN-13 (pbk): 978-1-4842-4108-0 ISBN-13 (electronic): 978-1-4842-4109-7
https://doi.org/10.1007/978-1-4842-4109-7
Recommended Resources
Practical Data Science Cookbook: Data Pre-processing, Analysis and Visualization using R and Python. Second Edition by Prabhanjan Tattar, Tony Ojeda (2017)
Rules
- User of mobile phones are strictly prohibited. In classroom or lab, if any student is caught using mobile phones, they may receive a warning first, and later may not be allowed to sit in class or lab.
- Attendance: Attending lectures and lab is mandatory. You will NOT be allowed to enter in class after 10 minutes. You are encouraged to be in class on time. If your attendance is less than 70%, there will be no compromise and you will be withdrawn from course an DZ grade will be assigned.
- Late submission NOT allowed: If you fail to submit your assignment/project on the specified time, you will NOT be able to get any marks.
- Plagiarism Unacceptable: You are not allowed to directly copy and paste any code available online for your project or homework, without understanding them. If you understood and used the online material, then you must provide reference to that website or resource from where you have copied it. Otherwise, it will be considered plagiarism and no marks will be given to you.
Course Learning Outcomes
- Understand basics of data analysis and visualization.
- Know human perceive information and how computers display information.
- Perform data analysis and modeling for large datasets.
- Apply design principles for a variety of statistical graphics and visualizations including scatterplots, line charts, and histograms.
Core Area Distribution
Teaching Methods
Assessment & Evaluation
ECTS / Workload
| Activity | Quantity | Duration (h) | Total Workload |
|---|---|---|---|
| Course Duration (Including Exam Week) | 16 | 3 | 48 |
| Out of Class Study Period | 16 | 4 | 64 |
| Midterm | 1 | 2 | 2 |
| Quiz | 3 | 1 | 3 |
| Assignment | 3 | 2 | 6 |
| Practice | 0 | 0 | 0 |
| Final | 1 | 2 | 2 |
Course Schedule
| Week | Subject | Preparation |
|---|---|---|
| 1 | Introduction to course, Purpose of visualization, data types of elements within datasets’ data, Basics of Python programming and libraries | Lecture |
| 2 | Intro to Numpy. Arrays (Creation, Indexing, Transposition, Universal functions, Processing and Input/Output). | slides |
| 3 | Intro to Pandas, Series and Data Frame, Data Cleaning and Manipulation Techniques. (Index Objects, Reindex, Drop Entry, Selecting Entries, Data Alignment, Rank and Sort, Summary Statistics, Missing Data, Index Hierarchy) | |
| 4 | File I/O Processing and Loading Data (Reading and Writing Text Files, JSON, HTML, Microsoft Excel files). | |
| 5 | Data Exploration and Operations (Merge, Merge on Index, Concatenation, Combining Data Frames, Reshaping, Pivoting, Duplicates in Data Frames, Mapping, Replacing, Rename Index, Binning, Outliers, and Permutation). | |
| 6 | Data Exploration and Operations (Group by on DataFrames Dictionary and Series, Aggregation, Splitting Applying and Combining, Cross Tabulation) | |
| 7 | Data Visualization and Statistical Graphics (Line Plot, Bar Plot, Pie Chart, Histogram Plot, Kernel Density Estimate Plots, Combining Plot Styles, Box and Violin Plots, Regression Plot, Heatmaps and Clustered Matrices). | |
| 8 | Midterm | Ara Sinav |
| 9 | Multivariate Analysis and Machine Learning (Supervised / Unsupervised Learning, Data Visualization and ML Connection, Linear Regression, and Logistic Regression) | |
| 10 | Multivariate Analysis and Machine Learning (Multi Class Classification, Support Vector Machine, and Naïve Bayes) | |
| 11 | Multivariate Analysis and Machine Learning (Decision Trees and Random Forest, Natural Language Processing) | |
| 12 | Data Analysis and Visualization of General Statistical Theorems (Discrete Uniform Distribution and Continuous Uniform Distribution) | |
| 13 | Data Analysis and Visualization of General Statistical Theorems (Binomial Distribution, and Poisson Distribution) | |
| 14 | Data Analysis and Visualization of General Statistical Theorems (Normal Distribution, and Sampling Techniques) | |
| 15 | Data Analysis and Visualization of General Statistical Theorems (T-Distribution, and Hypothesis Testing and Confidence Intervals) | |
| 16 | Final Exam |