ISDA 111-10
Information and Data Science
Fall 2021 Syllabus

Adam Romanik
Office: Online/Virtual
Phone: 443-252-1276
Office Hours: Virtual office hours. Telephone and in-person advising by appointment

Syllabus Links
Program Learning Outcomes (PLOs)
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Canvas Information: Courses will be available beginning August 19, 2021, at 6 am PT unless you are taking an intensive or a one-unit or two-unit class that starts on a different day. In that case, the class will open on the first day that the class meets.

You will be enrolled in the Canvas site automatically.

Course Description

Students from a number of disciplines will be introduced to the rapidly growing fields of information science and data science and equipped with some of their basic principles and tools.


Weekly Participation and Discussions (15 points, supports CLOs 1, 2, 3, 4, 8)
Students are required to actively participate in class and make thoughtful contributions to three class discussions posted on the course site. Students will be evaluated for their involvement in, and intellectual contribution to, the collaborative learning environment.

Hands-on Practices (60 points, supports CLOs 1, 5, 7, 9)
Four individual, hands-on practices will be given throughout the semester to help students review and reinforce what they have learned in class about information and data science.

Case Study (10 points, supports CLOs 2, 3, 8)
Students will discuss social, organizational, and managerial issues of information and data science in practice through various case studies and submit a case essay that answers and critiques the questions from the case study

Semester Project (50 points supports CLOs 1, 2, 3, 4, 5, 6, 7, 8, 9)
Students will work in groups on a semester project that focuses on emerging topics in information and data science. The project consists of three phases (more details TBA on the Canvas site):

  • Milestone I - Initial Thoughts: Students will submit a short paragraph discussing the potential topics and directions of the semester project. Students will also briefly present the motivation of the study and the approach that might be taken.
  • Milestone II - Mini Report: Students will submit a one-page report outlining the current progress of the semester project. The report will include what has been done, what the current status and results are, and what needs to be accomplished.
  • Final Report and Demo: Students will submit a detailed, 10-page report for the project. The report should at least include the following sections: motivation, problem statement, methodology, analysis results, and discussions and conclusion. Students will also prepare a short "demo" to present their work.



Textbook Readings

Assignments and Deadlines





Aug 19-25


Course Overview

·       Introduction to information science


Bawden & Robinson,

Chapters 1-3




Aug 26-Sept 1

Basic Concepts of Information & Organization

·       Information Organization

·       Information Overload


Bawden & Robinson,

Chapters 4-6




Sept 2-8

Information systems and the Internet

·       Information needs

·       Human Information Behavior

·       Information seeking behavior

·       Information economics



Bawden & Robinson,

Chapters 7-9

Assignment #1 Due



Sept 9-15

Networked Economy

·       Social and mobile media

·       Online communities

·       VR/AR

Bawden & Robinson,

Chapters 10-11




Sept 16-22


Information Literacy

·       Literacy in the digital age

·       Fake News and Misinformation

Information quality and evaluation

Bawden & Robinson,

Chapters 12-13




Sept 23-29

Positive and Negative Takes on Information Age

  • Privacy, security, and surveillance

·       ICTs for political and social change

Bawden & Robinson,

Chapters 14-15

Project Milestone I Due



Sept 30-Oct 6

Intro to Data Science

Saltz & Stanton

Chapters 1-2

Assignment #2 Due



Oct 7-13

Statistical Inference Recap

·       Populations and samples

·       Statistical modeling

·       Probability distributions

·       Model fitting

Saltz & Stanton

Chapters 3-7




Oct 14-20

Exploratory Data Analysis (EDA) and the Data Science Process

  • Basic tools (plots, graphs, and summary statistics) of EDA
  • Philosophy of EDA
  • Data Lifecycle
  • The Data Science Process

Saltz & Stanton

Chapters 8-10




Oct 21-27

Fundamental Machine Learning Algorithms

·       Supervised vs. unsupervised learning

  • Linear Regression, k-Nearest Neighbors (k-NN), k-means, and Naïve Bayes

Saltz & Stanton

Chapters 11-13

Project Milestone II Due



Oct 28-Nov 3

Machine Learning Usage and Applications

  • Data Wrangling: APIs and tools for scrapping the Web
  • Recommendation systems

Saltz & Stanton,

Chapters 14-15

Assignment #3 Due



Nov 4-10

Social Network Mining

  • Social networks as graphs
  • Clustering of graphs
  • Direct discovery of communities in graphs, partitioning of graphs, and neighborhood

Saltz & Stanton,

Chapters 16-18




Nov 11-17

Data Visualization

  • Basic principles, ideas, and tools for data visualization
  • Examples of inspiring (industry) projects

Saltz & Stanton,

Chapters 19-20




Nov 18-24

Data Analytics Pipeline

·       Data generating processes

·       How data is transported to be stored

·       How analytics, computing capabilities, production machine learning, and modeling platforms are built

·       Architectural design patterns and practical implementation considerations


Assignment #4 Due



Nov 25-Dec 1

Information Science vs. Data Science

  • Big data and artificial intelligence
  • A look back at information and data science
  • Next-generation information professionals and data scientists


Case Study Due



Dec 2-6

Final Project Presentations


Final Project Paper and Demo Due

This schedule and related dates/readings/assignments is tentative and subject to change with fair notice. Any changes will be announced in due time in class and on the course’s website in the Canvas Learning Management System. The students are obliged to consult the most updated and detailed version of the reading material and syllabus, which will be posted on the course’s website.

Detailed information on assignments, including the research paper grading rubric, will be provided on the course Canvas site.

Course Workload Expectations

Success in this course is based on the expectation that students will spend, for each unit of credit, a minimum of forty-five hours over the length of the course (normally 3 hours per unit per week with 1 of the hours used for lecture) for instruction or preparation/studying or course related activities including but not limited to internships, labs, clinical practica. Other course structures will have equivalent workload expectations as described in the syllabus.

Instructional time may include but is not limited to:
Working on posted modules or lessons prepared by the instructor; discussion forum interactions with the instructor and/or other students; making presentations and getting feedback from the instructor; attending office hours or other synchronous sessions with the instructor.

Student time outside of class:
In any seven-day period, a student is expected to be academically engaged through submitting an academic assignment; taking an exam or an interactive tutorial, or computer-assisted instruction; building websites, blogs, databases, social media presentations; attending a study group;contributing to an academic online discussion; writing papers; reading articles; conducting research; engaging in small group work.

Course Prerequisites

ISDA 111 has no prequisite requirements.

Course Learning Outcomes

Upon successful completion of the course, students will be able to:

  1. Demonstrate knowledge of fundamental concepts, ideas, and methodologies related to information science and systems.
  2. Explain how information & communication technologies (ICTs) and the networked economy shape behaviors, both at an individual and an organizational level.
  3. Describe, analyze, and evaluate information systems in a variety of contexts.
  4. Describe what data science is and understand the skill sets needed to be a data scientist.
  5. Understand and explain the data lifecycle and the interplay between different components in the data science process.
  6. Collect, combine, and visualize data from different sources.
  7. Apply supervised and unsupervised machine learning algorithms for data analysis and predictive modeling.
  8. Understand and explain ethical and privacy issues in data science.
  9. Understand and demonstrate the use of technology and specializations pertaining to the Internet, Web, and databases.

    SLOs & PLOs

    ISDA 111 supports:

  1. Information Science and Data Analytics SLO 2: Identify and apply appropriate data management strategies, carry out relevant analyses, interpret and apply the results to inform understanding and solve specific problems in context; and communicate analysis and visualization results appropriately to a diverse non-technical audience.
  2. Information Science and Data Analytics SLO 3: Demonstrate proficiency in the computing skills needed to support information and data analysis, including prototype building and scripting for working with structured data (data that is clearly defined and easily searchable) and unstructured data (data that is not easily searchable such as email, audio, video, and social media postings).
  1. SLO 2 and SLO 3 supports the following Information and Data Science Program Learning Outcomes (PLOs):

  2. PLO 1: Apply information and data science concepts and methods by thinking critically and creatively to conceptualize and solve real world problems.
  3. PLO 3: Demonstrate an understanding of professional and ethical responsibility in data ownership, security, sensitivity of data, and consequences and privacy concerns of data analysis.


Required Textbooks:

  • Bawden, D., & Robinson, L. (2017). An introduction to information science. Facet publishing. Available through Amazon: 1783303212arrow gif indicating link outside sjsu domain
  • Saltz, J., & Stanton, J. (2018). An introduction to data science. SAGE. Available through Amazon: 150637753Xarrow gif indicating link outside sjsu domain

Grading Scale

The standard SJSU School of Information Grading Scale is utilized for all iSchool courses:

97 to 100 A
94 to 96 A minus
91 to 93 B plus
88 to 90 B
85 to 87 B minus
82 to 84 C plus
79 to 81 C
76 to 78 C minus
73 to 75 D plus
70 to 72 D
67 to 69 D minus
Below 67 F


In order to provide consistent guidelines for assessment for graduate level work in the School, these terms are applied to letter grades:

  • C represents Adequate work; a grade of "C" counts for credit for the course;
  • B represents Good work; a grade of "B" clearly meets the standards for graduate level work or undergraduate (for BS-ISDA);
    For core courses in the MLIS program (not MARA, Informatics, BS-ISDA) — INFO 200, INFO 202, INFO 204 — the iSchool requires that students earn a B in the course. If the grade is less than B (B- or lower) after the first attempt you will be placed on administrative probation. You must repeat the class if you wish to stay in the program. If - on the second attempt - you do not pass the class with a grade of B or better (not B- but B) you will be disqualified.
  • A represents Exceptional work; a grade of "A" will be assigned for outstanding work only.

Graduate Students are advised that it is their responsibility to maintain a 3.0 Grade Point Average (GPA). Undergraduates must maintain a 2.0 Grade Point Average (GPA).

University Policies

Per University Policy S16-9, university-wide policy information relevant to all courses, such as academic integrity, accommodations, etc. will be available on Office of Graduate and Undergraduate Programs' Syllabus Information web page at: Make sure to visit this page, review and be familiar with these university policies and resources.

In order to request an accommodation in a class please contact the Accessible Education Center and register via the MyAEC portal.

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