Issue Date:11/7/2021 issue:02
ZU/QP10F004
Faculty: Information Technology
Program: Bachelor Department: Data Science and
Artificial Intelligence
Semester:
Academic year:
Course Plan
First: Course Information
Credit Hours: 3 Course Title: Introduction to Data Science
Course No.:
1505332
Lecture Time:
Section No.:
Prerequisite:
1501222
Obligatory Faculty Requirement Elective University Requirement Obligatory University Requirement Faculty Requirement
Course Elective Specialty Requirement Obligatory Specialization requiremen
tType Of Course:
Face-to-Face Learning
Blended Learning(2 Face-to-Face + 1Asynchronous) Online Learning (2 Synchronous+1 Asynchronous) Type of Learning:
Second: Instructor’s Information
Academic Rank:
Name:
E-mail:
Ext. Number:
Office Number:
Sunday Monday Tuesday Wednesday Thursday Office Hours:
Third: Course Description
This course introduces the ideas and techniques of data science and allowing students to easily develop a firm understanding of the subject. It covers topics such as data types and data pre- processing, data analysis and data Analytics, data collection, experimentation, and evaluation.
Students are required to use either python or R in their work.
Issue Date:11/7/2021 issue:02
ZU/QP10F004
Fourth: Learning Source
A HANDS-ON INTRODUCTION TO DATA SCIENCE
Main Reference:
Publication Year: 2020 Issue No.: first
Author: CHIRAG SHAH
Moodle Additional Sources
&Websites:
Classroom Laboratory Workshop MS Teams Moodle Teaching Type:
Fifth: Learning Outcomes
Connection To Program ILOs Code
Course Intended Learning Outcomes (CILOs) Course
Code
Knowledge Understand different types of data *PK5
**K1
Understand data pre-processing mechanisms
K2
Understand techniques of data modeling and analytics PK5 K3
Understand the data collection process PK5 K4
Understand basic concepts of data visualization PK5 K5
Skills The ability to collect data efficiently PS3
***S1
The ability to pre-process data efficiently PS3 S2
The ability to classify data into different types PS3 S3
The ability to build data models PS3 S4
The ability to analyze data models PS3 S5
Competencies Communication and collaboration PC5
****C1
Teamwork PC5 C2
Critical Thinking and Creativity PC3 C3
Leadership PC4 C4
Critical thinking PC2 C5
* P: Program, **K: knowledge, ***S: skills, ****C: competencies.
Issue Date:11/7/2021 issue:02
ZU/QP10F004
Sixth: Course Structure
Lecture Date
Intended Teaching
Outcomes(ILOs) Topics
ProceduresTeaching ***Teaching
Methods ***References
Week 1
C1,C2,C3,C4,C5 Introduction Face to face Lecture, quizzes
and assignment P. 3-32 C1,C2,C3,C4,C5 Introduction Face to face Lecture, quizzes
and assignment P. 3-32
K1, S1 Data science in
application domains Face to face Lecture, quizzes
and assignment P. 3-32
Week 2
K1, S1 Data science as
interdisciplinary field Face to face Lecture, quizzes
and assignment P. 3-32 K1, S1 Information vs. Data Face to face Lecture, quizzes
and assignment P. 3-32 K1, S1 Information vs. Data Face to face Lecture, quizzes
and assignment P. 3-32
Week 3
K1, S1 Skills and tools for Data
Science Face to face Lecture, quizzes
and assignment P. 3-32 K1, S1 Skills and tools for Data
Science Face to face Lecture, quizzes
and assignment P. 3-32 C1, C2, C3, C4,C5 Data Face to face Lecture, quizzes
and assignment P. 37-65
Week 4
K1, S1 Data Types Face to face Lecture, quizzes
and assignment P. 37-65 K4, S1
Data Collections Face to face Lecture, quizzes
and assignment P. 37-65 K2, S2 Data Pre-processing Face to face Lecture, quizzes
and assignment P. 37-65
Week 5
K2, S2 Data Pre-processing Face to face Lecture, quizzes
and assignment P. 37-65 K2, S2 Data Pre-processing Face to face Lecture, quizzes
and assignment P. 37-65 K2, S2 Data Pre-processing Face to face Lecture, quizzes
and assignment P. 37-65
Week 6
C1 ,C2 ,C3, C4,C5 Data Analysis and Data
Analytics Face to face Lecture, quizzes
and assignment P. 66-95 K3, S3 Descriptive Analysis Face to face Lecture, quizzes
and assignment P. 66-95 K3, S3 Diagnostic Analytics and
Predictive Analytics Face to face Lecture, quizzes
and assignment P. 66-95
Week 7
K3, S3
Prescriptive Analytics and Exploratory
Analysis
Face to face
Lecture, quizzes
and assignment P. 66-95
K3, S3
Prescriptive Analytics and Exploratory
Analysis
Face to face
Lecture, quizzes
and assignment P. 66-95
Week 8
K3, S3 Diagnostic Analytics and
Predictive Analytics Face to face Lecture, quizzes
and assignment P. 66-95 K3, S3 Diagnostic Analytics and
Predictive Analytics Face to face Lecture, quizzes
and assignment P. 66-95
Week 9
K3, S3 Diagnostic Analytics and
Predictive Analytics Face to face Lecture, quizzes
and assignment P. 66-95 K3, S3 Mechanistic Analysis Face to face Lecture, quizzes
and assignment P. 66-95 K3, S3 Mechanistic Analysis
Problems Face to face Lecture, quizzes
and assignment P. 66-95
Week 10
K3, S3 Hands-On with Solving
Data Problems Face to face Lecture, quizzes
and assignment P. 66-95 K3, S3 Hands-On with Solving
Data Problems Face to face Lecture, quizzes
and assignment P. 66-95
Issue Date:11/7/2021 issue:02
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C1 ,C2 ,C3, C4,C5
Data Collection, Experimentation, and
Evaluation
Face to face
Lecture, quizzes
and assignment P. 354-375
Week 11
C1 ,C2 ,C3, C4,C5
Data Collection, Experimentation, and
Evaluation
Face to face
Lecture, quizzes
and assignment P. 354-375
C1 ,C2 ,C3, C4,C5
Data Collection, Experimentation, and
Evaluation
Face to face
Lecture, quizzes
and assignment P. 354-375
Week 12
K4,K5, S3,S4,S5 Data Collection, Experimentation, and
Evaluation
Face to face
Lecture, quizzes
and assignment P. 354-375 K4,K5, S3,S4,S5 Data Collection,
Experimentation, and Evaluation
Face to face
Lecture, quizzes
and assignment P. 354-375
Week 13
K4,K5, S3,S4,S5 Data Collection, Experimentation, and
Evaluation
Face to face
Lecture, quizzes
and assignment P. 354-375 K4,K5, S3,S4,S5 Data Collection,
Experimentation, and Evaluation
Face to face
Lecture, quizzes
and assignment P. 354-375 C1, C2, C3, C4, C5 Revision and Projects Face to face Discussion
Week 14
C1, C2, C3, C4, C5 Revision and Projects Face to face Discussion C1, C2, C3, C4, C5 Revision and Projects Face to face Discussion C1, C2, C3, C4, C5 Revision and Projects Face to face Discussion
* Learning procedures: (Face-to-Face, synchronous, asynchronous). * * Teaching methods: (Lecture, video…..). ** * Reference: (Pages of the book, recorded lecture, video….).
Seventh: Assessment methods
Methods Online Learning Blended Learning
Face-To-Face Learning
Measurable Course (ILOs)
First Exam 0 0 0
Second Exam 0 0 0
Mid-term Exam 0 0 35
C1 ,C2 ,C3, C4,C5, K1,K3,K4, K5,S1,S3
Participation 0 0 15
Asynchronous
Meetings 0 0 0
Final Exam 0 0 50
C1 ,C2 ,C3, C4,C5, K1,K3,K4,S1,S3,S4,S5
Eighth: Course Policies
All course policies are applied on all teaching patterns (online, blended, and face- to-face Learning) as follows:
a. Punctuality.
b. Participation and interaction.
c. Attendance and exams.
Academic integrity: (cheating and plagiarism are prohibited).
