1. Course Code2261
2. Course Title
G51e: Data Science
3. Teacher
HAMIDULLAH, Sokout
4. TermSpring 1
5. Course Requirements (Courses / Knowledge prerequisite for this course)
Basic Knowledge of Statistics 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, an
unimaginable 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
definatley useful to analyze existing issues, reveal previously hidden opportunities andpredict future insights. Companies will compete for hundreds of thousands of new workers
needed to navigate the digital world. Already, experts estimate that millions of jobs in DATASCIENCE 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 an
introduction 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 datascience. Once we have laid out a solid foundation, we will be ready to learn Python andDatabase for Data Science. Starting from the very basics, we will build up your skills andsoon you will be able 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 Statistics for Data Science
6 Distribution
7 Hypothesis testing
8 P-value
9 Python for Data Science
10 Python for Data Science Exercises
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. International
Standard 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.
Understand the Data Science Methodology (i) from problme to appraoch, (ii) working(4)with the data, (iii) Deriving the answer.
(5) Familiar with coding with Python for Data Science, as well as Databases.
Be able to define data-intensive problems in data science and understand their(6)
underlying statistical and computational principles.
11. Correspondence relationship between Educational goals and Course goals
High level ICTskills

Human skill(Tankyu skill）

Educational goals of the school
Basic academic skills
Specialized knowledge and literacy
Ability to continually improve own strengths

Course Goals(1) (2)(1) (2) (3) (4)(4) (5)

Human skill(Tankyu skill）

Ability to discover andresolve the problem insociety

Fundamental
Competencies for
Working Persons
Professional ethics
12. Evaluation
Goals
Examination

Problem setting
Hypothesis planning
Hypothesis testing
Practice
Ability to step forward
Ability to think through
Ability to work in a team(6)(2) (3) (6)(2) (3) (6)(6)(3)

Evaluation method & point allocation
Quiz
Reports PresentationDeliverables
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Other
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Allocation
254035
13. Evaluation Criteria
Examination
Quiz
Every week multiple choice quizzes are used to evaluate theunderstanding of students and motivate them for further learning.
Reports
Presentation
In the final presentation, students will be asked to choose and summarize
a scientific research journal paper or thier own ideas related to the course
main objectives and contents. The evaluation will be based onparticipation in the group presentation, the structure of presentation, therelavance of the argument, time managment and relation to the course
contents.
Deliverables
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.
Other
14. Active LearningHourly percentage of active learning within the whole class time60%
1 Active learning such as problem solving assignment using the knowledge
and skills acquired in class.

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 bring yourown 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 Data Science)(90 min)

1. Introduction, Evaluation, Scope of the Course.
2. What is Data Science.
3. Imerging Technologies shaping the modern data
Lessen 2: ( Major Terminologies in Data Science )1. What is difference:a. Big Datab. Data Miningc. Machine Learning
d. Deep Learning
e. Neural Netowrks(90 min)Lessen 3: (Data Science Tools and Methodology)(90 min)

1. Tools for Data Science
2. Data Science Methodology

Lessen 4: (Fundametals of Data Science )(90 min)

1. Dataa. Traditionalb. Big
2. Data Sciencea. Business Intelligenceb. Traditional Methods
c. Machine Learning
Lessen 5: (Statisitcs for Data Science)(90 min)

1. Population vs Sample
2. Types of Data
3. Exercises
Lessen 6: (Distribution)(90 min)1. Distribution
2. Confidence Intervals
3. Exercises
Lessen 7: (Hypothesis Testing)(90 min)

1. Hypothesis Testinga. Null Hypoythesisb. Alternative Hypothesis
c. Error in Hypothesis
Lessen 8: (P-value)(90 min)

1. P-Value
2. Exercises
3. Basics of Python for Data Science
Lessen 9: (Python for Data Science)(90 min)1. Python Basics
2. Python Statements
3. Python Loops
4. Python Functions
5. Python Object Oriented Programming
Lessen 10: (Python for Data Science Exercises)(90 min)

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)(90 min)

Exercises
Lessen 13: (Data Analysis with Python, Part I, II)(90 min)

1. Data Analysis with Pythona. Pandas
b. Numpy
Lessen 14: (Data Analysis with Python Exercises)(90 min)

Exercises
Lessen 15: (Presentation by Students)
Group Presentation by students
Lessen 16: (Presentation by Students)
Group Presentation by students(90 min)(90 min)