INFO 287-02
INFO 287-16
Seminar in Information Science
Fall 2020 Syllabus

Dr. Souvick Ghosh
Office Location: Virtual/Online
Office Hours: Virtually (by appointment) via telephone or online

Syllabus Links
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iSchool eBookstore

Canvas Information: Courses will be available beginning August 19 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

This course offers a practical introduction to the field of Statistical Data Science and focuses on solving real-life data problems through data-driven visualization and statistics. It provides fundamental knowledge in several programming languages and tools, and basic methods for quantitative analytics.

Course Requirements


Lectures, discussions, assignments, and rubrics will be posted to the Canvas course management system. Links to additional materials will be provided in Canvas as well.

Summary of assignments and points earned:

  • Blog Entries/Discussion Forums - 3 blog entries, 5 points each = 15 points [CLOs 3,4]
  • Assignments - 3 assignments, 20 points each = 60 points [CLOs 2,3,4]
  • Final Project - 25 points [CLOs 2,3,4]
The total number of points for this class is 100.
Assignments Due

Students are expected to check the course site several times each week. Unless otherwise noted, each module begins on Wednesday and ends on Tuesday. Assignments will be due by midnight (Pacific Time) on the due date. Contact the instructor prior to due date in the case of serious illness or emergency.

Course Calendar

A detailed Course Calendar will be available in Canvas on the first day of the semester. The table below provides a summary of course topics and assignment due dates. It is subject to minor changes, that will be announced with fair notice.


Dates Module Topic  Module Activity/Assignment


September 9-15

Introduction to the course

Setting up tools and resources

Introduction to UNIX

Blog Entry 1

Due Date: Sept. 15*


September 16-22

Using UNIX to process text

Introduction to Fundamentals of Statistics

Assignment 1

Due Date: Sept. 22


September 23-29

Statistics for Data Science

  • Correlation Analysis
  • Regression Analysis
  • Finding fit of models
  • Evaluating models

Blog Entry 2

Due Date: Sept. 29*


September 30-October 6

Fundamentals of R

  • Data frames and other data structures
  • Data visualization using R

Final Project

  • Topic Identification.
  • Research Question Proposal
Due Date: Oct. 6


October 7-13

Statistics using R

  • Descriptive Statistics
  • Correlation and Regression

Assignment 2

Due Date: Oct. 13


October 14-20

Fundamentals of Python

  • Data types and functions
  • Control Structures and algorithms
  • Data visualization using Python

Blog Entry 3

Due Date: Oct. 20*


October 21-27

Statistics using Python

  • Descriptive statistics
  • Correlation and Regression

Assignment 3

Due Date: Oct. 27


October 28 - November 3

Data Problem with Twitter

  • Collecting data using Twitter API
  • Cleaning and visualizing data
  • Analyzing data to develop insights

Final Project

  • Report
  • Presentation
Due Date: Nov. 3

*NOTE: For weeks with required discussion board postings, students should provide their initial post by Saturday at midnight (Pacific Time), to leave ample time for follow-up discussion. Please participate actively in the required discussions.


  • Course grades are determined by the accumulation of 100 possible points, distributed as outlined above in the course calendar.
  • This class follows the standard iSchool Grading Scale. 
  • Assignments 1,2 and 3 (20 possible points for each assignment): At least 5 points if the student submits the assignment on time and explains the data analysis process correctly. 10 more points will be awarded if the approach is correct and the answer. 5 points will be awarded if appropriate visualizations are included.
  • Blog Entries 1, 2, and 3 (5 possible points each): At least 3 points if the student posts a blog entry in time and discuss the question asked thoughtfully. The other 2 points will be awarded if the student engages in discussion and replies to the posts of at least two other students. 
  • Final Project (25 possible points): At least 5 points if the student takes part in the discussion regarding the topic for the final project. The remaining 20 points will be awarded for the final project submission. To get a perfect score, students should explain the data collection process, the research questions, the analysis done, and the evaluation techniques used. 
  • Late assignments will not be accepted after 5 days past the due date. Late assignments submitted after the assignment deadline will receive a 10% point reduction for each day up to 5 days based on the total point value of the assignment. For example, a 25 point assignment would have a daily 2.5 point reduction; a 15 point assignment would have a daily 1.5 point reduction; a 5 point assignment would have a daily 0.5 point reduction. No points will be awarded after 5 days late.
  • Discussion board postings will not be accepted for credit after the week's discussion has ended.
  • All course materials must be completed by the last day of the class.

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

INFO 200, students will be expected to know HTML/CSS (as taught in INFO 240) or obtained via work experience. 

Course Learning Outcomes

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

  1. Write simple programs and commands in Python, R, and UNIX.
  2. Apply various programming languages to collect, clean, process, and analyze data.
  3. Develop an understanding of basic statistical methods to explore and visualize data.
  4. Identify data-driven analytics problems and use statistical methods and visualization techniques to solve those problems.

Core Competencies (Program Learning Outcomes)

INFO 287 supports the following core competencies:

  1. G Demonstrate understanding of basic principles and standards involved in organizing information such as classification and controlled vocabulary systems, cataloging systems, metadata schemas or other systems for making information accessible to a particular clientele.
  2. H Demonstrate proficiency in identifying, using, and evaluating current and emerging information and communication technologies.


Recommended Textbooks:

  • Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R. Thousand Oaks, CA: Sage Publications, Inc. Available through Amazon: 1446200469arrow gif indicating link outside sjsu domain
  • Severance, C. (2016). Python for everyone: Exploring data in Python 3. Ann Arbor, MI: Create Space Independent Publishing Platform. Available through Amazon: 1530051126arrow 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;
    For core courses in the MLIS program (not MARA or Informatics) — 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.

Students are advised that it is their responsibility to maintain a 3.0 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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