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Data Science

Spring 2019 - Section 002 - Larry Dignan

Data Science

MIS 0855.002 ■ SPRING 2019 ■ LARRY DIGNAN
  • Announcements
  • Schedule
  • About
    • Course Objectives & Policies
    • Evaluation & Grading
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Course Objectives & Policies

When and Where

Alter Hall 607

5:30 p.m. to 8 p.m., Mondays

Course Description

We are all drowning in data, and so is your future employer. Data pours in from sources as diverse as social media, customer loyalty programs, weather stations, smartphones, and credit card purchases. How can you make sense of it all? Those that can turn raw data into insight will be tomorrow’s decision-makers; those that can solve problems and communicate using data will be tomorrow’s leaders. This course will teach you how to harness the power of data by mastering the ways it is stored, organized, and analyzed to enable better decisions. You will get hands-on experience by solving problems using a variety of powerful, computer-based data tools virtually every organization uses. You will also learn to make more impactful and persuasive presentations by learning the key principles of presenting data visually.

Course Objectives

  •  Describe how advances in technology enable the field of data science
  • Locate sources of data relevant to their field of study
  • Identify and correct problems with data sets to facilitate analysis
  • Combine data sets from different sources
  • Assess the quality of a data source
  • Convey meaningful insights from a data analysis through visualizations
  • Analyze a data set using pivot tables
  • Determine meaning in textual data using text mining
  • Identify when advanced analytics techniques are appropriate
  • Predict events that will occur together using association mining

Required Textbook

 There is no required textbook for this course. However, there are a set of required readings available for free either online or from the bookstore. Refer to the schedule and reading list for more detail.

Class Structure and Participation

 You are expected to be an active part of the learning process. In the first part of each class session, we’ll discuss the readings. This will be followed by an in-class activity.

Preparation for 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 that session relate to each other.

Most Tuesdays there will be a short pre-class quiz, taken online (check 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 to the Community Site.

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

Participation during class

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 class does not qualify as participation.

Classroom Etiquette

 The environment you and your fellow students create in class directly impacts the value gained from the course. To that end, the following are my expectation of your conduct in this class:

  • Arrive on time and stay until the end of class.
  • Turn off cell phones and alarms while in class.
  • Limit the use of electronic devices (e.g., laptop, tablet computer) to class-related usage such as taking notes. Restrict the use of an Internet connection (e.g., checking email, Internet browsing, sending instant messages) to before class, during class breaks, or after class.
  • During class time speak to the entire class (or breakout group) and let each person “take their turn.”
  • Be fully present and remain present for the entirety of each class meeting.

 Plagiarism and Academic Dishonesty

Plagiarism and academic dishonesty can take many forms.  The most obvious is copying from another student’s exam, but the following are also forms of this:

  • Copying material directly, word-for-word, from a source (including the Internet)
  • Using material from a source without a proper citation
  • Turning in an assignment from a previous semester as if it were your own
  • Having someone else complete your homework or project and submitting it as if it were your own
  • Using material from another student’s assignment in your own assignment

If you use text, figures, and data in reports that were created by someone other than yourself, you must identify the source and clearly differentiate your work from the material that you are referencing. There are many different acceptable formats that you can use to cite the work of others (see some of the resources below). You must clearly show the reader what is your work and what is a reference to somebody else’s work.

Plagiarism and cheating are serious offenses. Penalties for such actions are given at my discretion, and can range from a failing grade for the individual assignment, to a failing grade for the entire course, to expulsion from the program.

Student and Faculty Academic Rights and Responsibilities

The University has adopted a policy on Student and Faculty Academic Rights and Responsibilities (Policy # 03.70.02) which can be accessed through the following link:
http://policies.temple.edu/getdoc.asp?policy_no=03.70.02

 

 

 

Primary Sidebar

Recent Announcements

  • Final Exam Reminder
  • Study Guide for Exam 3 (Final Exam)
  • In class exercise 13.2 driver download
  • In-Class Exercise 13.2: Simple Predictive Analytics
  • Primer on descriptive, prescriptive, predictive analytics

For student help:

Larry Dignan

Phone: 267.614.6467

Office hours: 5:00-5:30pm and 8 to 8:30pm, Mondays, main campus; By appt via Zoom, Skype, FaceTime etc.

ITA: Nhi Nguyen

Great Data Sites

  • FiveThirtyEight
  • Guardian Data Blog
  • Flowing Data
  • Financial Times Data Blog
  • Socrata Open Data
  • Pew Research Data

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