1. Department of Information Technology & Security
College of CS and IT
Jazan University
Introduction to Data Science
(ITEC - 313)
Department Required Course
2. Course Information
Course
Title
Introduction to Data
Science
Course Code ITEC 313
Pre Req. Nil Level 7
Contact
Hrs.
2 hrs Theory + 2 Hrs. Lab Cr. Hrs. 3
Course Objective :
This course will develop the students’ ability to:
• Describe Data Science and the skill sets needed to be a data scientist.
• Understand the Data Science Process and how its components interact.
• Carry out basic statistical modeling and analysis.
• Explain the significance of exploratory data analysis (EDA) in data science.
• Apply basic tools (plots, graphs, summary statistics) to carry out EDA.
• Use APIs and other tools to scrap the Web and collect data.
• Apply EDA and the Data Science process in a case study.
3. Course Description
Data Science is the study of the generalizable extraction of knowledge from
data. Being a data scientist requires an integrated skill set spanning
mathematics, statistics, databases and other branches of computer science
along with a good understanding of the craft of problem formulation to
engineer effective solutions. This course will introduce students to this rapidly
growing field and equip them with some of its basic principles and tools as
well as its general mindset. Students will learn concepts, techniques and tools
they need to deal with various facets of data science practice, including data
collection and integration, exploratory data analysis, predictive modeling,
descriptive modeling, data product creation, evaluation, and effective
communication. The focus in the treatment of these topics will be on breadth,
rather than depth, and emphasis will be placed on integration and synthesis of
concepts and their application to solving problems. To make the learning
contextual, real datasets from a variety of disciplines will be used.
4. Course Learning Outcomes
CLOs
Aligned
PLOs
1 Knowledge and Understanding:
1.1 Define the basic concepts and terminologies of data science process K1
1.2 Explain the critical methods and techniques commonly used in data
science K1
2 Skills
2.1 Demonstrate proficiency with the methods and techniques for
obtaining, organizing, exploring and analyzing data S1
2.2 Analyze basic data analysis and statistical modeling tools to carry out
Exploratory Data Analysis (EDA) S3
3 Values
3.1 Evaluate and determine appropriate solutions for a problem using
Application Program Interface (API) and EDA
V3
5. CLOs with Assessment Methods
Code Course Learning Outcomes
Teaching
Strategies
Assessment
Methods
1.0 Knowledge and Understanding
1.1
Define the basic concepts and terminologies of
data science process
Visual & Verbal
[Lectures /
Presentations]
Mid Term,
Assignment, Final
Exam
1.2
Explain the critical methods and techniques
commonly used in data science
Visual & Verbal
[Lectures /
Presentations]
Mid Term,
Assignment, Final
Exam
2.0 Skills
2.1
Demonstrate proficiency with the methods and
techniques for obtaining, organizing, exploring
and analyzing data
Visual & Verbal
[Lectures /
Presentations]
Mid Term,
Assignment, Final
Exam
2.2
Analyze basic data analysis and statistical
modeling tools to carry out Exploratory Data
Analysis (EDA)
Visual & Verbal
[Lectures / Lab
Practical]
Assignment, Lab
Exam
3.0 Values
3.1
Evaluate and determine appropriate solutions
for a problem using Application Program
Interface (API) and EDA
Self-study
Visual & Verbal
[Lectures / Lab
Practical]
Quiz, Lab Exam
8. Electronic Materials
https://www.datacamp.com/courses/
https://lms.jazanu.edu.sa/webapps/login
Other Learning
Materials
Text books:
Mathematics for Machine Learning: https://mml-
book.github.io/
An introduction to Data Science by Jeffrey Stanton
The Elements of Data Analytic Style by Jeff Leek
Exploratory Data Analysis with R, by Roger Peng
OpenIntro Statistics, by Diez, Barr, and Centinkaya-Rundel
R Programming for Data Science, by Roger Peng
Data Resources:
UC Irvine Machine Learning Repository
https://archive.ics.uci.edu/ml/index.php
Variety of consumer datasets
https://www.kaggle.com/datasets
World Bank https://data.worldbank.org/data-catalog/
US Government Data https://www.data.gov/
Learning Resources
9. Chapters to be Covered
Chapter 1: Introduction to Data Science
Chapter 2: Data
Chapter 3: Data Analysis
Chapter 4: Data Analytics
Chapter 5: Machine Learning
Self-Study
Chapter 6: Data Collection, Experimentation,
and Evaluation
10. Course Roadmap
Chapter Topics Weeks &
Hours
Chapter 1 Introduction to Data Science: Importance of Data Science,
Where Do We See Data Science? How Does Data Science
Relate to Other Fields? Skills for Data Science, A Data Science
Profile, Data Scientist’s tasks, Tools for Data Science, Issues
of Ethics, Bias, and Privacy in Data Science
Week 1
+
Week 2
4 Hours
Chapter 2 Data: Data Types, Structured Data, Unstructured Data,
Challenges with, Unstructured Data, Data Collections, Open
Data, Social Media Data, Multimodal Data, Data Storage and
Presentation, Data Pre-processing, Data Cleaning, Data
Integration, Data Transformation, Data Reduction, Data
Discretization
Week 3
+
Week 4
4 Hours
Chapter 3 Data Analysis: Data Analysis and Data Analytics, Descriptive
Analysis, Variables, Frequency Distribution, Measures of
Centrality, Dispersion of a Distribution
Week 5
+
Week 6
4 Hours
11. Course Roadmap
Chapter Topics Weeks &
Hours
Chapter 4 Diagnostic Analytics, Predictive Analytics,
Prescriptive Analytics, Exploratory Analysis,
Mechanistic Analysis
Week 1
+
Week 2
4 Hours
Chapter 5 Machine Learning: What Is Machine Learning?
Machine Learning Examples, Machine Learning
Applications, Machine Learning Algorithms,
Regression, Error Function or Cost Function,
Gradient Descent, Choosing ML Algorithm, Choosing
the right estimator
Week 3
+
Week 4
4 Hours
Chapter 6
(Self-Study)
Data Collection, Experimentation and Evaluation Week 5
+
Week 6
4 Hours
12. Lab Outline
CHAPTER ONE
STATISTICS
1.1 Type of data
1.1.1 Continuous
1.1.2 Discrete
1.1.3 Categorical
1.1.3.1 Ordinal
1.1.3.2 Nominal
1.1.4 Binary
1.2 Location Estimates
1.2.1 mean(average)
1.2.2 trimmed mean
1.2.3 weighted mean
1.2.4 median
1.2.5 mode
CHAPTER TWO
COVARIANCE AND CORRELATIONS
2.1 Covariance
2.1.1 Positive Covariance
2.1.2 Zero Covariance
2.2 Correlation Coefficient
2.2.1 Pearson Correlation
2.2.1.1 Positive Correlation
2.2.1.2 Negative
Correlation
CHAPTER THREE
PLOT 3D
5.1 Surface
5.2 wireframe
5.3 Line in 3D plane
5.4 Scatter in 3D plane
5.5 Sphere
5.6 Paraboloid
1.3 Variability Estimates
1.3.1 deviation
1.3.2 mean absolute deviation
(MAD)
1.3.3 variance
1.3.4 degree of freedom(dof)
1.3.5 standard deviation
1.3.6 range (max - min)
1.3.7 percentile
1.3.8 Quartiles
1.3.9 Inter Quartile Range (IQR)
1.3.10 skewness
1.3.10.1 unskewed
1.3.10.2 left skewed
1.3.10.3 right skewed
1.3.11 Kurtosis
1.4 Multi Variate Analysis
1.4.1 hexbin
1.4.2 boxplot
1.4.3 violinplot
Note: Working Platform : Google Colab
13. Grading Scheme (100%)
Assessments Coverage Allocation Marks
Assignment-1 Chapter 1 to 4 Week 2/Week 3 20
Midterm Exam Chapter 1, 2 Week 5/Week 6 15
Quiz (Online or
Offline)
Self-Study Chapter Week 8/Week 9 05
Final Lab Exam Lab Works in Practical
Manual
Week 9/Week 10 20
Final Exam Chapter 1 to 5 Week 11/Week 12 40
Total 100
14. Course Material
Introduction to Data Science (ITEC-313)
1. Course Description
2. Course Specification
3. Updated PPT
4. Self-Study Chapter
5. Lab Manual
6. Text Book
7. Road Map – 2022-23-3
8. Course Introduction PPT
15. Course Coordinator
Personal Profile
Dr. Nadim Rana
Lecturer, Department of IT and Security
Office No: 09
Email: nadimrana@jazanu.edu.sa
Mobile: 0543776607
Student Consultation Hours:
Monday: 10-12
Wednesday:10-12
16. Thank You
Please discuss the Course Outline,
Assessment methods, Grade distribution, Lab
assessment, etc. with students in the class.
