Course Title Introduction to Data Science
Module Title
Module Code M1011 Course Code: INSC
CP/ECTS 3
Study Hour Lecture: 48 Laboratory: 48 Tutorial: 0 Home Study: 93
Instructor Name: Simret Mehari
Information
Office Phone: Email: smehari661@gmail.com
Office Location: BLDG#: 031
Consultation Hour:
Course Information Academic Year: 2022
Semester: I
Course Schedule:
Classroom:
Prerequisite: None
Mode of Delivery: Parallel
Today, vast amounts of data from a wide variety of sources are available. It is not
Course Description clear, how to use those data and how to extract useful information from it. This
problem is faced in a tremendous range of scholarly, government, business, medical,
and scientific applications. The purpose of this course is to introduce students to the
rapidly growing field of Data Science, and to equip them with methodological, tool
based, and analytical skills to extract knowledge from data. Being a data scientist
requires an integrated skill set including data collection and integration, data analysis,
descriptive and predictive modeling, data visualization, knowledge evaluation and
effective communication skills with non-expert users. This course will provide
students with an overview of the different components of the Data Science pipeline
and with practical skills to implement it.
After successfully completing this course, students will be able to:
Learning Outcomes
• Define Data Science
• Understand the brief history of Data Science
• Explain data and data set
• Understand Data Science ecosystem
• Define machine learning
• Explain Data Science tasks
• Understand privacy and ethics of Data Science
• Predict Data Science future trends and principles of success
Course Content
Topics Duration References
(Week)
Chapter 1: WHAT IS DATA SCIENCE? 1 -2
• Data Science definition
• A Brief history of Data Science
• The emergence and evolution of Data Science
• Where is Data Science used?
• Why now Data Science?
• Myths about Data Science
Chapter 2. DATA AND DATA SET 3-4
• Data and its type
• Data set
• Perspectives on Data
• Standard stages/steps/process in a Data Science project
Chapter 3: DATA SCIENCE ECOSYSTEM 5-6
• Data Science Infrastructure
• Big data and its challenges
• Hadoop ecosystem
• Data integration and its challenges
Chapter 4: MACHINE LEARNING 7-8
• Machine-learning algorithms and models
✓ Supervised versus Unsupervised Learning
✓ Prediction Models
✓ Linear Regression
✓ Neural Networks,
✓ Deep learning, and
✓ Decision-tree models
• Bias in Data Science
Chapter 5: STANDARD DATA SCIENCE TASKS 9-12
• Clustering (or segmentation)
• Anomaly (or outlier) detection
• Association-rule mining
• Prediction (including the sub problems of classification and
regression)
Chapter 6: PRIVACY AND ETHICS 13-16
• Commercial Interests versus Individual Privacy
• Ethical Implications of Data Science
• Computational Approaches to Preserving Privacy
• Legal Frameworks for Regulating Data Use and Protecting
Privacy
• Towards an Ethical Data Science
Chapter 7: FUTURE TRENDS AND PRINCIPLES
OF SUCCESS
✓ Future promising areas of Data Science
✓ Principle of success or failure for Data Science project
Teaching Strategy Lectures, lab, Video Demos, Class Room Discussions
Assessment Criteria Assessment Forms % of credit allotted
Lecture (100%)
• Mid Exam: 20
• Assignment:10%
• Project: 20%
• Final Exam: 50%
Role of The role of the instructor is to deliver lectures, give quizzes, assign and guide individual
Instructor(s) assignments, and assess performance of learners.
Role of Students The role of the students is to attend lectures, participate in class discussions, do individual
assignments, present individual assignment in class, and carry out written exams.
Required software R studio, Tableau, Python
and/or hardware
Reference Text books
John D. Kelleher and Brendan Tierney, DATA SCIENCE, (2018)Massachusetts
Institute of Technology, the MIT Press Essential Knowledge Series,
Cambridge, Massachusetts
Cao, Longbing (2018) Data Science thinking; the Next Scientific, Technological and
Economic Revolution
Hamid R. Arabnia and et..al(2020)Principles of Data Science; introduces various
techniques, methods, and algorithms adopted by Data Science experts Yeol
Song and Yongjun Zhu(2018) , Big data and data science: what should we
teach?
