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582: Introduction to Data Science
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UNIVERSITY OF WISCONSIN-MILWAUKEE
School of Information Studies
INFOST (582) – Introduction to Data Science
Section 201 and 202 - Online
Spring 2016
SYLLABUS
Instructor: Margaret Kipp
E-mail:
[email protected] (best contact method)
Fax:
414-229-6699
Office:
NWQB 2574
Office Hours: TBA
CATALOG DESCRIPTION
Introduces basic concepts, background, theoretical, practical and technological aspects of
data science. 3 credits
GENERAL DESCRIPTION
This course provides an introduction to data science. Data science has developed as a set of
methods for analysing massive data sets to extract useful knowledge. A data scientist is a
person who has the skills and knowledge to perform these analyses. This course will cover
topics necessary to develop data-science solutions to problems including data collection, data
cleaning and integration, data analysis, and data presentation.
PREREQUISITES
 Junior Standing. For 500 and 600 level courses it is recommended that an
undergraduate student first consult with the appropriate instructor and/or advisor
concerning the applicability of this specific course.
 Basic computer facility and technology literacy as listed in the SOIS policy are
required: http://www4.uwm.edu/sois/programs/graduate/mlis/complitreq.cfm
 Optional: Some programming experience or basic statistical knowledge (measures of
central tendency) would be an asset for later in the course.
OBJECTIVES/OUTCOMES
Upon completion of the course, students will be able to:
1.
effectively develop researchable questions; (Paper or project)
2.
identify data sources, collect, clean and merge data; (Selecting a data set,
Cleaning data)
3.
manipulate structured or unstructured data sources; (Querying a Database,
Creating Metadata)
4.
identify and apply appropriate statistical methods for analysing data; (Analysing
Data, Project)
5.
critically evaluate tools for working with data; (Project, Cleaning Data)
6.
address multilingual and multicultural issues in data creation and analysis;
(Creating Metadata, Querying a Database, Readings and Discussions)
582: Introduction to Data Science
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7.
identify emerging trends and stay current with issues in data science. (Readings
and Discussions)
ALA COMPETENCIES (for MLIS students)
1. The systems of cataloguing, metadata, indexing, and classification standards and
methods used to organize recorded knowledge and information.
2. Information, communication, assistive, and related technologies as they affect the
resources, service delivery, and uses of libraries and other information agencies.
3. The application of information, communication, assistive, and related technology and
tools consistent with professional ethics and prevailing service norms and applications.
4. The principles and techniques necessary to identify and analyse emerging
technologies and innovations in order to recognize and implement relevant
technological improvements.
METHOD
Lecture/Discussion/Readings/Examples/Exercises – to achieve a satisfactory understanding
of the course material and to fulfil requirements of the assignments, students are expected to
attend the lectures, read and comment on the readings, participate in discussions and inclass exercises, and explore examples and tutorials.
TIME COMMITMENT
This course requires a weekly time commitment. General university guidelines indicate that a
3 credit course requires a minimum 144 hour time commitment over the course of a term.
This time commitment represents a minimum of 9-10 hours of work per week per course. For
an onsite class 3 of these hours represent onsite instruction in a classroom; in an online class
this time would be spent on independent reading, discussions and in-class exercises.
Each week you may be required to read notes and readings from the reading list associated
with that class, participate in discussions, write summaries of readings, complete in-class
exercises, explore examples, or complete assignments and projects. It is your responsibility
to plan your time in order to complete all activities based on the schedule outlined in this
syllabus.
ACCOMMODATIONS
If you need accommodations due to illness, disabilities, scheduling conflicts with religious
observances, or other life events (e.g. military service) contact the instructor as soon as
possible, preferably by the third week of class as per university policy. Official documentation
may be required depending on the nature of the considerations requested per university
policy (http://www4.uwm.edu/secu/docs/faculty/1895R3_Uniform_abus_Policy.pdf).
TEXTBOOK AND READINGS
Shron, Max. 2014. Thinking with Data: How to Turn Information into Insight. O'Reilly Media.
ISBN: 978-1449362935 (Available in Paperback, Kindle, EPUB, MOBI, etc.) [Required]
Readings are listed in the course outline for each class. Readings should be completed
before the class. Other course materials, including this syllabus, are available through D2L
(http://d2l.uwm.edu/).
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Changes may be made to the readings as the term progresses. These are generally marked
with TBD. Changes will be announced in D2L ahead of the classes for which changes will
occur.
COURSE OUTLINE
Class Date Topics
1
Jan
Data Science
27
and Big Data,
Becoming a
Data Scientist
2
Feb
3
3
Feb
10
Developing
Data Based
Questions
Choosing data
sets/sources of
data and
methods of
collecting data
Readings (complete before class)
 Shron. 2014. Thinking with Data, Preface, Chapter 1
(18p);
 Loukides. 2010. What is Data Science? (12p)
http://www.cloudera.com/content/dam/cloudera/Resources
/PDF/What_is_Data_Science_OReilly.pdf;
 Zhu & Xiong. 2015. Defining Data Science. (8p)
http://arxiv.org/abs/1501.05039 [cs.DB];
 Becoming a Data Scientist 8 Jul 2013 by Swami
Chandrasekaran (1p)
http://nirvacana.com/thoughts/becoming-a-data-scientist/;
 Miller. 2013. Data Science: The Numbers of Our Lives,
APRIL 11, 2013, New York Times (4p)
http://nyti.ms/10QarGu;
 Dumbill. 2012. What is big data?: An introduction to the big
data landscape. O'Reilly.com. (9p)
http://radar.oreilly.com/2012/01/what-is-big-data.html
 Shron. 2014. Thinking with Data, Chapters 2-4 (50p);


Readings
◦ Shron. 2014. Thinking with Data, Chapter 5, 6 (25 p);
◦ Mattmann. 2013. Computing: A vision for data science.
Nature 493, p.473–475. doi:10.1038/493473a (UWM
Libary Full Text) ;
◦ Marx. 2013. Biology: The big challenges of big data.
Nature 498, p.255–260. doi:10.1038/498255a (UWM
Library Full Text) ;
◦ Doctorow. 2008. News Feature: Big data: Welcome to
the petacentre. Nature 455, 16-21.
http://www.nature.com/news/2008/080903/full/455016a
.html ;
◦ Wallis, et al. 2013. If We Share Data, Will Anyone Use
Them? Data Sharing and Reuse in the Long Tail of
Science and Technology. PLOS One. DOI:
10.1371/journal.pone.0067332
http://journals.plos.org/plosone/article?
id=10.1371/journal.pone.0067332 ;
Datasets
◦ Open Data Handbook
http://opendatahandbook.org/guide/en/what-is-open-
582: Introduction to Data Science
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4
Feb
17
Privacy, Ethics
and Data




5
Feb
24
Metadata and
the Semantic
Web


6
Mar
2
Databases and
other data
stores

data/;
◦ Open Data Datasets. KDNuggets.
http://www.kdnuggets.com/datasets/index.html;
◦ Marr. 2015. The Free 'Big Data' Sources Everyone
Should Know. DataScienceCentral.com.
http://www.datasciencecentral.com/profiles/blogs/thefree-big-data-sources-everyone-should-know;
O'Leary. 2015. "Big Data and Privacy: Emerging Issues,"
in Intelligent Systems, IEEE 30(6): 92-96. (UWM Library
Full Text);
Perera, et al. 2015. "Big Data Privacy in the Internet of
Things Era," in IT Professional 17(3): 32-39 (UWM Library
Full Text);
Daries, et al. 2014. "Privacy, Anonymity, and Big Data in
the Social Sciences." Communications Of The ACM 57(9):
56-63. (D2L);
Shilton. 2012. Participatory personal data: An emerging
research challenge for the information sciences. Journal of
the American Society for Information Science and
Technology 63(10): 1905-1915. (UWM Library Full Text);
Readings
◦ Elings and Waibel. 2007. Metadata for All: Descriptive
Standards and Metadata Sharing across Libraries,
Archives and Museums. First Monday 12(3).
http://firstmonday.org/htbin/cgiwrap/bin/ojs/index.php/f
m/article/view/1628/1543;
◦ Gilliland. 2008. "Setting the Stage" In Introduction to
Metadata.
http://www.getty.edu/research/publications/electronic_p
ublications/intrometadata/setting.html;
◦ Robu et al. 2006. An introduction to the Semantic Web
for health sciences librarians. JMLA 94(2): 198-205.
http://www.ncbi.nlm.nih.gov/pmc/articles/PM C1435839/
;
◦ Introducing Linked Data and the Semantic Web.
LinkedDataTools.com.
http://www.linkeddatatools.com/semantic-web-basics;
Tutorials
◦ XML Basic (first 10 pages)
http://www.w3schools.com/xml/default.asp;
◦ JSON Tutorial http://www.w3schools.com/json/;
◦ Introduction to RDF (first 3 pages)
http://www.w3schools.com/rdf/rdf_intro.asp;
Readings
◦ Kimani. Introduction to Databases. Technopedia.
https://www.techopedia.com/6/28832/enterprise/databa
582: Introduction to Data Science
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
7
Mar
9
Working with
structured and
semi-structured
data:
Databases and
Metadata


8
9
Mar
16
Mar
23
Spring Break No Class
Unstructured
Data and Log
Files, Working
with
unstructured
data




ses/introduction-to-databases ;
◦ Pokorny. 2015. Database technologies in the world of
big data. In Proceedings of the 16th International
Conference on Computer Systems and Technologies
(CompSysTech '15), Boris Rachev and Angel
Smrikarov (Eds.). ACM, New York, NY, USA, 1-12.
(D2L);
◦ Sadalage. 2014. NoSQL Databases: An Overview.
ThoughtWorks.com.
http://www.thoughtworks.com/insights/blog/nosqldatabases-overview;
Tutorials
◦ Interactive SQL Tutorial http://sqlzoo.net/;
◦ Lynda.com
(http://www4.uwm.edu/sois/resources/it/lynda/)
▪ Relational Database Fundamentals
▪ MySQL Essential Training
Readings
◦ OAI for Beginners - the Open Archives Forum online
tutorial (Sections 1,3) http://www.oaforum.org/tutorial/;
◦ Jackson et al. Dublin Core Metadata Harvested
Through OAI-PMH. Journal of Library Metadata 8:1
(2008) 5-21. http://hdl.handle.net/2142/9091;
Tutorials
◦ Using Open Refine
http://openrefine.org/documentation.html;
◦ Interactive SQL Tutorial http://sqlzoo.net/;
◦ SPARQL https://code.google.com/p/tdwgrdf/wiki/Beginners6SPARQL;
◦ Export Data From Database to CSV File
https://support.spatialkey.com/export-data-fromdatabase-to-csv-file/;
No Readings
Nicholas, et al. 2003. Micro-mining and segmented log file
analysis: a method for enriching the data yield from
Internet log files. Journal of Information Science, 29 (5),
pp. 391–404 . (UWM Library Full Text);
Huntington, et al. 2006. Obtaining subject data from log
files using deep log analysis: case study OhioLINK.
Journal of Information Science 32 no. 4, 299-308. (UWM
Library Full Text);
Blumberg, et al. 2003. The problem with unstructured
data. DM REVIEW - soquelgroup.com.
http://soquelgroup.com/Articles/dmreview_0203_problem.
pdf;
582: Introduction to Data Science
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


10
Mar
30
Cleaning up
data and
integrating data
sets






11
April
6
Describing data



12
April
13
Analysing Data


13
April
20
Visualizing
Data

Polnaszek, et al. 2016. Overcoming the Challenges of
Unstructured Data in Multisite, Electronic Medical Recordbased Abstraction. Medical Care (publish ahead of print).
(UWM Library Full Text);
Veeranjaneyulu, et al. 2014. Approaches for Managing
and Analyzing Unstructured Data. International Journal on
Computer Science and Engineering (IJCSE) Vol 6(1).
http://www.enggjournals.com/ijcse/doc/IJCSE14-06-01020.pdf;
Log File Analysis: The Ultimate Guide
http://builtvisible.com/log-file-analysis/
Loshin. 2015. Integrating Data from Multiple Sources
http://community.embarcadero.com/index.php/blogs/entry/i
ntegrating-data-from-multiple-sources-by-david-loshin;
Cody, et al. (n.d.) Data Cleaning 101
http://www.ats.ucla.edu/stat/sas/library/nesug99/ss123.pdf
;
Rahm. (n.d.) Data Cleaning: Problems and Current
Approaches, University of Leipzig, Germany.
http://lips.informatik.uni-leipzig.de/files/2000-45.pdf;
Top Ten Ways to Clean Your Data. Microsoft.com.
https://support.office.com/en-us/article/Top-ten-ways-toclean-your-data-2844b620-677c-47a7-ac3ec2e157d1db19;
Using a spreadsheet to clean up a dataset. 2013.
http://schoolofdata.org/handbook/recipes/cleaning-datawith-spreadsheets/;
Data Journalism Handbook
http://datajournalismhandbook.org/1.0/en/understanding_d
ata_2.html;
Foreman. 2015. Data Smart. Wiley. Chapters TBD (UWM
Library Full Text);
Sonnad. 2002. Describing data: statistical and graphical
methods. Radiology. Dec; 225(3): 622-8.
http://pubs.rsna.org/doi/pdf/10.1148/radiol.2253012154
Online Statistics Education: An Interactive Multimedia
Course of Study, Chapter 3, 4
http://onlinestatbook.com/2/index.html
Foreman. 2015. Data Smart. Wiley. Chapters TBD (UWM
Library Full Text);
Online Statistics Education: An Interactive Multimedia
Course of Study, Chapter 11, 12, 14
http://onlinestatbook.com/2/index.html
Foreman. 2015. Data Smart. Wiley. Chapters TBD (UWM
Library Full Text);
582: Introduction to Data Science
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

NIST/SEMATECH e-Handbook of Statistical Methods,
Chapter 1: Exploratory Data Analysis
http://www.itl.nist.gov/div898/handbook/;
Kandel et al. Research directions in data wrangling:
Visualizations and transformations for usable and credible
data. Information Visualization 0(0) 1–18.
http://research.microsoft.com/enus/um/people/nath/docs/datawrangling_ivj2011.pd f;
No Readings
•
TBD

14
15
April
27
May
4
Work on
Projects
Wrapup and
Current Events
ASSIGNMENTS
Assignment
Selecting a dataset
Identify a question that interests you.
Identify a data set on this topic that
could be used to answer the
question. Explain the kinds of
information available in the dataset
and how the data is structured. (400
words)
Metadata
Select 2 objects and create a
metadata record for each using a
metadata schema of your choice.
Your records should contain enough
information to fully describe the
object. It is recommended that you
use Dublin Core or Schema.org
encoded in XML, RDF or JSON.
Database
Create a simple database in Access
or MySQL with at least three joined
tables. Populate the tables with
enough data to provide useful results
for your queries. Create two SQL
queries that extract useful data from
at least two tables of the database.
Short Paper
Write a short paper on a data
science related topic. (800-1000
words)
Cleaning Data
Graduate
5
Undergrad
uate [1]
5
Associated
Classes
1-3
Deadline
Class 4 [2]
5
5
5
Class 6
5
5
6-7
Class 7
20
n/a
All
5
5
7-10
Proposal:
Class 3
Paper: Class
10
Class 11
582: Introduction to Data Science
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Identify problems in a data set which
might interfere with analysis of the
data (e.g. typos, structure problems,
poor adherence to standards).
Describe the problems and write
policy statements or suggested
solutions for solving these problems.
(400 words)
Analysing Data
Develop a basic analysis of a data
set using the tools discussed in
class. Describe your findings in
report format. Use graphs, charts or
other tools to present your findings
as required. (400U/500G words)
Note: It is recommended that you
submit a rough draft/early draft of
your project, though you may choose
to do a separate data analysis.
Project
Select a topic and gather a small test
set of data, clean it, analyse it and
present the results in a report. Your
report may be written, oral or
multimedia based. (800U/1200G
words or equivalent, charts, tables,
etc. do not count towards the word
limit) Note: Graduate students are
expected to provide additional critical
analysis and reflection of the data
including potentially locating and
citing appropriate supporting
materials from published sources.
Participation (see below)
10
10
1-12
Class 13
30
50
All
Proposal:
Class 8
Project: Last
class
20
20
All
Last class
[1] Different requirements for graduate and undergraduate levels will be specified in the directions for
each assignment where appropriate.
[2] Class numbers are listed in the Course Outline Table. Each class has an associated Class Number
(#), Date, Topic, Readings and may have In-class Exercises, Discussions or Tutorials. The
assignment table is keyed to the course outline's class numbers. To determine the exact date an
assignment is due, go to the appropriate class number in the course outline table or use the D2L
calendar.
* There is no final exam in this course. *
Working with Classmates
All assignments except the short paper and participation may be completed in pairs or trios.
582: Introduction to Data Science
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Assignments completed in pairs/trios must identify all work partners by full name at the top of
the assignment. You must each submit the same assignment to the dropbox. If you simply
assisted each other but did not do the whole assignment together, you must also note this at
the top of the assignment. Unacknowledged borrowing is seen as plagiarism, so be sure to
document your teamwork to avoid this.
Formatting Guidelines for Assignments
Assignments should be written using Arial or another Sans-Serif style font. Do not use red for
emphasis or to highlight your answers to questions. Remove all extraneous information
before submission (e.g. assignment instructions or tips).
Use whatever citation format you prefer, but do not use footnotes. If you are not using a
common format such as MLA or APA you should include information about which style guide
you are using in the assignment.
Paper submissions will not be accepted. All assignments must be typed on a computer and
submitted electronically. Handwritten submissions will not be accepted, even if scanned and
submitted electronically.
Assignments may not be submitted in Pages, Microsoft Works, or Microsoft Project as I
cannot open these formats. You should save these as a PDF instead. Other common file
formats should be acceptable including Open Office formats. If you are using an unusual
format you can always check with me first before submission to ensure I can open it.
Due Dates and Assignment Submission
All assignments and projects should be submitted through D2L to the appropriate dropbox
before midnight (Central Time) on the recommended due date. Students should strive to
submit assignments by the recommended due date, but may have until the assignment's
Final Deadline to submit. Points for late assignments will be reduced 10% per day late after
the Final Deadline. The dropbox will remain open for the submission of late assignments until
the late penalty reaches 100%.
Participation items should be submitted to the appropriate discussion group (see the
participation section below) before the discussion group closes. Discussion groups will be
open for 1 week before and 1 week after the date of the associated class.
Emailed submissions will only be accepted as a backup to a D2L submission (or in case of
D2L errors).
Everything must be submitted by the Last Class (this includes all assignments, papers,
projects, and participation). All project and assignment deadlines are in the syllabus. For
discussion deadlines check the discussion groups or the D2L calendar. The D2L calendar
also contains all project and assignments deadlines. It is your responsibility to keep track of
deadlines using the tools provided or by creating your own calendar of deadlines.
Items submitted early will not be evaluated until their Final Deadline (or Recommended Due
Date). Students are encouraged to complete all Associated Classes listed under Assignments
582: Introduction to Data Science
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before submitting the assignments since the material in these classes constitutes preparation
for the assignments. Submission well before the recommended due date is not encouraged.
Extensions
Students must contact the instructor before each Final Deadline listed under Assignments for
any extensions. Extension requests made prior to the Final Deadline do not require any
documentation as long as they are not longer than a week. Simply provide a date/time by
which you will submit the assignment. After the deadline the penalties listed under Due Dates
will be enforced. Material submitted late after an extension will also be subject to these
penalties. Plan your time accordingly.
Extra Credit or Other Special Considerations
Per university policies (see http://www4.uwm.edu/secu/policies/saap/upload/S29.htm) extra
credit assignments and other special consideration are not possible. Students should make
use of the extensions policy outlined above or provide appropriate documentation of special
circumstances as outlined elsewhere in the syllabus.
Participation
Students are expected to participate in discussion and in-class exercises as a demonstration
of their ability to articulate key concepts. Discussion will include individual and group
components. Participation is mandatory and constitutes one quarter of the points available for
this class. Participation will consist of all of the following: individual summaries of readings,
participation in group discussions, contributed articles, and responses to others.
Participation will consist of all of the following:
• Completion of the Syllabus Quiz
◦ The syllabus quiz must be completed in the first 2 weeks of class. Points will
automatically be entered in D2L.
• Individual Summaries of Readings
◦ Post 3 summaries of the weekly readings to the appropriate weekly discussion
group based on the class associated with each reading.
◦ You must post 3 summaries in total, but you may choose the classes for which you
wish to contribute the summaries.
◦ Sign up for 3 sets of readings on the signup sheet posted in the news section of
D2L.
◦ Responses need not exceed 300 words.
◦ Summaries posted before the date of the class earn a half bonus point each. Be
sure to mark this on your course completion checklist to ensure you receive the
bonus.
• Participation in Weekly Discussions
◦ Participation in the in-class exercises and discussions included each week in the
weekly discussion group. Points will be allocated based on your participation level
(i.e. frequently, infrequently, no participation).
◦ Generally frequent participation requires that you participate at least once a week
in most weeks.
◦ Responses need not exceed 300 words.
582: Introduction to Data Science
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•
•
•
Contributed Article
◦ Contribution of a new article, video, cartoon, etc. relevant to the class and a short
summary (approximately 100 words) explaining its relevance to class. This should
be posted to the appropriate weekly discussion group based on the topic. You may
choose which week you wish to contribute this item.
◦ A signup sheet will be posted in the news section of D2L.
Responses to Others
◦ Reading and/or responding to weekly reading summaries and other information
posted to the weekly discussion groups by classmates. Points will be allocated
based on your reading level (i.e. many, few, nothing read) and/or your responses to
others.
Submission of the Course Checklist to the participation dropbox
◦ The completed checklist with all required course elements listed submitted to the
dropbox before the last class. You should complete as much as possible of the
checklist. Use the checklist throughout the term to ensure you are on track to
complete all course requirements.
Code of Conduct/Expectations for this Class
This is a professional programme and professional, courteous behaviour is expected of all
participants. It is expected that class members will show consideration for all other members
of the class and contribute in a constructive manner which is conducive to a good learning
environment. Class members should consider the relevance and appropriateness of their
contributions to the class before contributing to the class. Violations of these expectations will
result in reduced participation points or other sanctions depending on severity.
Plagiarism and Referencing
It is expected that you will consult and cite the research and professional literature where
merited and not rely solely on encyclopaedias, newspapers or unpublished, online sources.
Papers where the majority of sources are blogs and Wikipedia (or similar sites) will not be
accepted.
Use a common style manual for citations (e.g. APA, MLA, Chicago, etc.). Ideally you would
choose a citation style guide you have used before, or one you are using in another class.
Plagiarism is the unacknowledged borrowing of ideas or material from someone else's work.
It is considered an academic offence and can be considered grounds for failure in a course or
expulsion from the programme. Cite all references and provide credit for all other materials.
This applies to all material including images, sounds or videos. A citation (in the format of
your choice) with a functioning URL (if relevant) is the minimum required for a reference.
(http://guides.library.uwm.edu/content.php?pid=235714&sid=1949820#6509804)
You may not resubmit assignments already submitted in other courses or in a previous
instance of this course, nor may you submit other people's work as your own. Plagiarism will
be dealt with on a case by case basis but will result in a lowered mark on the assignment,
failure on the assignment or failure in the course depending on severity and the number of
plagiarized items submitted. Points lost through plagiarism may not be replaced by bonus
points on other assignments.
582: Introduction to Data Science
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GRADING SCALE
96-100 A
Superior work
91-95
A87-90
B+
84-86
B
Satisfactory, but
undistinguished work
80-83
B77-79
C+
74-76
70-73
67-69
64-66
C
CD+
D
60-63
Below 60
DF
Work is below standard
Unsatisfactory work
GRADE REQUIREMENT FOR A CORE COURSE
If you are pursuing an MSIST degree, you need to earn at least a B (does not include B-) in
this course.
UWM AND SOIS ACADEMIC POLICIES
The following link will take you to UWM pages/links which contain university policies affecting
all UWM students. http://www.uwm.edu/Dept/SecU/SyllabusLinks.pdf
The following link will take you to pages/links which contain SOIS policies affecting all SOIS
students. http://www4.uwm.edu/sois/resources/formpol/policies.cfm
Undergraduates may also find the Panther Planner and Undergraduate Student Handbook
useful (http://www4.uwm.edu/dos/student-handbook.cfm).
For graduate students, there are additional guidelines from the Graduate School
(http://uwm.edu/graduateschool/).
This document is licensed under a Creative Commons Attribution-NoncommercialShare Alike 3.0 United States Licence except where other rights exist. Any
commercial use of this work requires a separate licence.
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