Kobe Institute of Computing, Syllabus 2023

1. Course Code2261
2. Course Title
G51e: Data Science
3. Teacher
HAMIDULLAH, Sokout
4. TermSpring 1
5. Course Requirements (Courses / Knowledge for this course) and Important Information
Basic Knowledge of Statistics with Excel and Computer Programming (But NOT Essential)
6. Course Overview and Objectives
Data Science has a big impact on the business landscape, which is constantly changing intoday's world. As the quantity of human connection with digital footprint increases daily, anunimaginable large mass of fine-grained data is generated on a regular basis. Global
internet users have climbed to 4.95 billion at the start of 2022, with internet penetration now
standing at 62.5 percent of the world’s total population. The data generated behind could be
useful to analyze existing issues, reveal previously hidden opportunities and predict future
insights. Companies will compete for hundreds of thousands of new workers needed tonavigate the digital world. Already, experts estimate that millions of jobs in DATA SCIENCE
might remain vacant for the lack of readily available talent.
This course is about the world of data science. In this course we will start with anintroduction where we will discuss the world of data science. I explain why data science isimportant and how it helps businesses stay competitive in this day and age. Then continues
by introducing important mathematical and statistical concepts that are essential to do data
science. Once we have laid out a solid foundation, we will be ready to learn Database for
Data Science. Starting from the very basics, we will build up your skills and soon you will beable to play with analysis of data with Python.

7. Course Outline
1 Course orientation and Introduction to Data Science
2 Major terminologies in Data Science
3 Data Science Tools and Methodology
4 Fundamentals of Data and Data Science
5 Descriptive Statistics
6 Exercises
7 Inferential Statistics
8 Exercises
9 Hypothesis testing
10 P-value
11 Database and SQL for Data Science
12 Database and SQL for Data Science Exercises
13 Data Analysis with Python
14 Data Analysis with Python Exercises
15 Final Presentation
16 Final Presentation
8. Textbooks (Required Books for this course)

9. Reference Books (optional books for further study)(1) Learning from Data: An Introduction to statistical reasoning, third edition. InternationalStandard Book Number-13: 978-0-8058-4921-9.
(2) Data Science from scratch, ISBN-978-1-4919-0142-7.
(3) Available resources on Internet
10. Course Goals (Attainment Targets)(1) Become familiar with foundations of Data and Data Science.
(2) Understanding the basic of statistics for Data Science.
(3) Be able to summarize a data set using descriptive statistics and Inferential statistics.
(4) Understand the Data Science Methodology (i) from problme to appraoch, (ii) working
with the data, (iii) Deriving the answer.
(5) Familiar to work with Databases.
(6) Be able to define data-intensive problems in data science and understand theirunderlying statistical and computational principles.
(7)(8)
11. Correspondence relationship between Educational goals and Course goals
Educational goals of the school
Course Goals
High level ICT Basic academic skills(1) (2)
Specialized knowledge and literacy(1) (2) (3) (4)skillsAbility to continually improve own strengths(4) (5)
Problem setting(6)
Ability to discover andHypothesis planning(2) (3) (6)Human skill resolve the problem inHypothesis testing(2) (3) (6)(TankyusocietyPractice(6)skill）
Fundamental
Ability to step forward
Ability to think through(3)
Competencies for
Abilitytoworkinateam
Working Persons
Professional ethics
12. Evaluation
Goals
Evaluation method & point allocation
Examination
Quiz
Reports Presentation Deliverables
Other(1)
〇〇
(2)
〇〇
(3)
〇〇
(4)
〇
(5)
〇〇〇
(6)
〇〇〇
(7)
(8)
Allocation
30304013. Evaluation Criteria
Examination
Quiz
Every week multiple choice quizzes are used to evaluate the understandingof students and motivate them for further learning.
Reports
Presentation

Deliverables

OtherIn the final presentation, students will be asked to choose and summarize ascientific research journal paper or thier own ideas related to the course
main objectives and contents. The evaluation will be based on participationin the group presentation, the structure of presentation, the relavance of theargument, time managment and relation to the course contents.
Individuale and group assignment will be assigned for the students withfocus on learning goals (2,3, and 5). The evaluation will be based on how
the students understand the exercises and participation.

14. Active Learning
Hourly percentage of active learning within the whole class time
1 Active learning such as problem solving assignment using theknowledge and skills acquired in class.

60%
All the time

2 Active learning such as group works and discussions.

Sometimes

3 Outcome presentations and feedbacks.

Sometimes

4 Students actively make decisions on how the class should be conducted. Not at all
15. Notes
This course is like a jouney by attending it you will benefit and might enjoy. Please bringyour own computer in the class.
16. Course plan(Notice) This plan is tentative and might be changed at the time of delivery

Lessen 1: (Course Orientation and Introduction to DataScience)
1. Introduction, Evaluation, Scope of the Course.
2. What is Data Science.
3. Imerging Technologies shaping the modern data

LectureLessen 2: ( Major Terminologies in Data Science )Lecture1. What is difference:a. Big Datab. Data Miningc. Machine Learning
d. Deep Learning
e. Neural Netowrks
Lessen 3: (Data Science Tools and Methodology)

Lecture1. Tools for Data Science
2. Data Science Methodology
Lessen 4: (Fundametals of Data Science )Lecture1. Dataa. Traditionalb. Big
2. Data Sciencea. Business Intelligenceb. Traditional Methods
c. Machine Learning
Lessen 5: Statisitcs for Data Science
1. Population vs Sample
2. Types of Data

Lecture + Exercises

Lessen 6: Descriptive Statistics Exercises

Lecture + Exercises

Exercises
Lessen 7: Inferential Statistics

Lecture + Exercises

Exercises
Lessen 8:

Lecture + Exercises

1. P-Value
2. Exercises
Lessen 9: (Hypothesis Testing)

Lecture + Exercises

1. Hypothesis Testinga. Null Hypoythesisb. Alternative Hypothesis
c. Error in Hypothesis
Lessen 10: (P-value)

Lecture + Exercises

Exercises
Lessen 11: (Database and SQL for Data Science)(90 min)

1. Database Concepts
2. SQL Statements
3. SQL Joins
Lessen 12: Database and SQL for Data Science Exercises)

Lecture + Exercises

Exercises
Lessen 13: (Data Analysis with Python, Part I, II)

Lecture + Exercises

1. Data Analysis with Python
a. Pandas
b. Numpy
Lessen 14: (Data Analysis with Python Exercises)

Lecture + Exercises

Exercises
Lessen 15: (Presentation by Students)

Presentation (90 min)Group Presentation by students
Lessen 16: (Presentation by Students)
Group Presentation by students

Presentation (90 min)