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Data Science

Fall 2021 - Section 001 - Aleksi Aaltonen

Data Science

MIS 0855.001 ■ Fall 2023 ■ Aleksi Aaltonen
  • Announcements
  • Schedule
  • Readings
  • About
    • Course overview and policies
    • Grading
    • Get Tableau software
    • Aleksi, your instructor
    • Gradebook

Grading

The assessment of the course consists of four components. The components have the following weights.

Item Weight
Exams (3) 40%
Assignment 1 and 2 (individual) 30%
Group project 3 (group) 20%
Quizzes (10) 10%

Exams

There will be three exams during the semester.  The date of the first exam is October 4 and the date of the second exam is November 1. The final exam is scheduled for December 6.

Missed exams cannot be made up – if you are ill you need to let the instructor know immediately, before the exam to make alternative arrangements.

You may not arrive late for exams.  If you are late, you cannot take the exam.

Assignments

There will be three mandatory assignments.

Individual projects are to be completed without consulting your fellow students and they should represent your own work. A group project should reflect the work of your group, but no one else.

Assignment Type Due
1. Analyze a Data Set Using Tableau Individual 10/11
2. Cleaning a Data Set Individual 11/8
3. Final group project Group 12/11

Participation and quizzes

You are expected to participate actively the learning process.

Prepare for each class

Carefully read the assigned material prior to each class. You may find it helpful to take notes on the major points of each reading, noting how the readings for the session relate to each other.

Most Mondays there will be a short pre-class quiz, taken online according to the course schedule. The quiz will cover all readings to be discussed that week. Your instructor will provide the link to the quiz through a post on the course website.

You must complete the quiz by yourself strictly before the start of class. It is ‘open book’ – you can use the readings to take the quiz.

Participate during classes

We will typically start each session with opening questions about the assigned readings. Students called on to answer should be able to summarize the key issues, opportunities, and challenges in the reading. All students should be prepared to be answer these questions.

While you’re not expected to say something in every single class meeting, simply showing up for a class does not qualify as participation.

Submission deadlines and grading

Weekly quizzes are due strictly before the session starts on Monday. Submissions after the session has started receive no credit.

All other assignment submissions are due by the end of the day.

OVERALL GRADING SCALE

Scale

Letter

93-100 A
90-92 A-
87-89 B+
83-86 B
80-82 B-
77-79 C+
73-76 C
70-72 C-
67-69 D+
63-66 D
60-62 D-
Below 60 F

LATE SUBMISSION POLICY

No late deliverables will be accepted without penalty; note that equipment failure is not an acceptable reason for turning in a deliverable late.

Assignments

Assignments will be assessed with a 20% penalty deducted from the assignment’s score for each day starting the assignment is late (i.e. if you are just one minute late, you will lose 20% of your score).

Weekly quizzes

Missed or late quiz submissions will receive a zero grade.

If you submit the quiz more than once, only the oldest one submitted will count (i.e. the original submission).

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Lecture recordings

Recordings of our sessions are available from here.

Recent Announcements

  • Group Project Submission Form
  • Week 13 Reading Quiz
  • Group List is Here
  • Post Your Selected Tweets as Comments Here!
  • Week 12 Reading Quiz

TEACHING TEAM

Aleksi Aaltonen (Instructor)
aleksi@temple.edu

I am available to meet students on Wednesdays 2–3pm at Speakman 206D. If the time is not convenient for you or you want to meet virtually using Zoom, please send me an email.

Ziyi Zhao (Graduate Teaching Assistant)
ziyi.zhao0001@temple.edu

Aesha Patel (B4USoar mentor)
aesha.patel0003@temple.edu

Great Data Sites

  • FiveThirtyEight
  • Guardian Data Blog
  • Flowing Data
  • Financial Times Data Blog
  • Pew Research Data
  • US Government Open Data
  • OpenDataPhilly

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