Kobe Institute of Computing, Syllabus 2023

1. Course Code2262
2. Course Title
G52e: Data Science Exercises
3. Teacher
HAMIDULLAH, Sokout
4. TermSpring 2
5. Course Requirements (Courses / Knowledge for this course) and Important Information
Data Science Course (2261)

6. Course Overview and Objectives
The major goal of Data Science is to solve companies (institutions, government, private
sectors, .. etc) problems using available data. Data Science entails everything that has
something to do with data such as: Collecting the data, Cleaning up the data, Visualizing
data, Analyzing it, and Creating predictive models based on the data. Playing with data will
pave the way to gain insights that could help us with good decision making.
In this course we will start from the very basics (pre-processing data), we will build up yourskills and soon you will be able to solve advanced statistics tasks using machine learningalgorithms in Python such as CLUSTERING, REGRESSION, CLASSIFICATION. As well as
you learn with integration and visualization with Python.

7. Course Outline
1 Course Orientation and Data Analysis with Python
2 Data Visualization with Python
3 Data Visualization with Python
4 Data Visualization with Python Exercises
5 Data Preprocessing
6 Data Preprocessing Exercises with Machine Learning (Regression)
7 Machine Learning Algorithmes (Classification)
8 Classification Exercises
9 Machine Learning Algorithms (Clustering)
10 Clustering Exercises
11 Model Selection
12 Exercises
13 Integration Concept
14 Integration Exercises (In-Class Practice)
15 Students Presentation
16 Students Presentation
8. Textbooks (Required Books for this course)

9. Reference Books (optional books for further study)(1) Machine Learning with Python Cookbook, ISBN: 9781491989388.
(2) Advanced Data Analytics using Python, ISBN: 978-1-4842-3449-5.
(3) Available resources on Internet10. Course Goals (Attainment Targets)(1) Understanding and applying Visulization with Python(2) Able to preprocess data using Python(3) Gain in-depth familiarity with various Machine Learning algorithms (supervised learningalgorithms and unsupervised learning algorithms)(4) Able to Implement machine learning algorithms to real-world problems, and rigorously
evaluate their performance using different methods.
(5) Understand the concpet of Integration Programming and implement in real-world
problems.
(6)
(7)
(8)
11. Correspondence relationship between Educational goals and Course goals
Educational goals of the school
Course Goals
High level ICT Basic academic skills
Specialized knowledge and literacy(1) (2) (3) (4) (5)skillsAbility to continually improve own strengths(1) (2) (3) (4) (5)Problem setting
Ability to discover andHypothesis planning
Human skill resolve the problem inHypothesis testing(Tankyusociety
Practiceskill）
Fundamental
Ability to step forward(3) (4) (5)Ability to think through(3) (4) (5)Competencies for
Ability to work in a team(3) (4) (5)Working Persons
Professional ethics
12. Evaluation
Goals
Evaluation method & point allocation
Examination
Quiz
Reports Presentation Deliverables
Other(1)
〇〇〇
(2)
〇〇〇
(3)
〇〇
(4)
〇〇
(5)
〇
(6)
(7)
(8)
Allocation
25255013. Evaluation Criteria
Examination
Quiz

Every week multiple choice quizzes are used to evaluate the understandingof students and motivate them for further learning.

Reports
Presentation

In the final presentation, students will be asked to work on a real-worldproblem using Machine Learning algorithm. The evaluation will be based onparticipation in the group presentation, the structure of presentation, therelavance of the argument, time managment and relation to the coursecontents.

Deliverables

Individual and group assignment will be assigned for the students with focus
on learning goals (1,3,4, and 5). The evaluation will be based on how thestudents understand the exercises and participation.

Other14. Active Learning
Hourly percentage of active learning within the whole class time80%
1 Active learning such as problem solving assignment using the knowledge All the timeand skills acquired in class.
2 Active learning such as group works and discussions.

All the time

3 Outcome presentations and feedbacks.

Sometimes

4 Students actively make decisions on how the class should be conducted.

Not at all

15. Notes
Please bring your computers in the class.

16. Course plan(Notice) This plan is tentative and might be changed at the time of delivery

Lessen 1: (Course Orientation)

Lecture + Exercises

1. Course Orientation and Introduction to Data Science Exercises
2. Data Analysis with Python Part III

Lessen 2: (Data Visualization with Python )Lecture + Exercises

1. Data Analysis Exercise
2. Data Visualization with Python

Lessen 3: (Data Visualization with Python)

Lecture + Exercises

1. Data Visualization with Python
a. Bar chartsb. Histogramc. Regression
Lessen 4: (Data Visualization with Python)

Lecture + Exercises

1. Data Visualization with Python
a. Word Cloudb. Waffle Chartc. Exercise
Lessen 5: (Data Preprocessing)
Data Preprocessing with Python
1. Importing Libraries
2. Uploading Data set
3. Dealing with missing data
4. Encoding categorical data
5. Splitting dataset (Traing & Testing)
6. Feature ScallingLecture + Exercises

Lessen 6: (Data Preprocessing Exercises)

Lecture + Exercises

1. Machine Learning Algorithmes (Regression)
2. Exercises

Lessen 7: (Machine Learning Algorithmes)

Lecture + Exercises

Classification

Lessen 8: (Classification Exercises)

Lecture + Exercises

Exercises

Lessen 9: (Machine Learning Algorithmes)

Lecture + Exercises

Clustering

Lessen 10: (Clustering Exercises)

Lecture + Exercises

Exercises

Lessen 11: (Model Selection)

Lecture + Exercises

1. Model Selection Concepts
2. Model Selection Exercises

Lessen 12: (Exercises)

Lecture + Exercises

Exercises

Lessen 13: (Integration Exercises)

Exercises

1. Integration Concept
2. Integration Exercise

Lessen 14: Integration Exercises (In-Class Practice)

Exercises

Integration Exercises

Lessen 15: (Student Presentation)

Presentation(90 min)Students Presentation

Lessen 16 : Students Presentation
Students Presentation

Presentation (90 min)