MIS2502: Data Analytics Course Introduction Aaron Zhi Cheng http://community.mis.temple.edu/zcheng/ acheng@temple.edu

Contact info Aaron Zhi Cheng Office: 201F Speakman Email: acheng@temple.edu Office hours: 3:50 – 4:50 PM, Mondays & Wednesdays, or by appointment ITA: Nathaly Gonzalez Email: nathaly.gonzalez@temple.edu

About Me A Ph.D. candidate in Business Administration, with a specialization in MIS @Temple Fox An external researcher and data scientist for a few companies IBM (Beijing, China) TalkingData (Beijing, China) Vivat Insurance (Amsterdam, the Netherlands)  Hersha Hospitality Trust (Philadelphia, USA) National Health Development Research Center (Beijing, China). community.mis.temple.edu/zcheng

Course Overview A foundation for designing database systems and, analyzing business data Hands-on experience with various tools: MySQL, Excel, and R

https://www. kdnuggets https://www.kdnuggets.com/2018/05/poll-tools-analytics-data-science-machine-learning-results.html

Modules No. Module Introduction 1 Relational Data Modeling 2 Introduction 1 Relational Data Modeling 2 SQL 1: Getting Information Out of a Database 3 SQL 2: Putting Information Into a Database 4 Extract, Transform, Load (ETL) 5 Dimensional Data Modeling 6 Principles of Data Visualization 7 Advanced Data Analytics and R 8 Classification Using Decision Trees 9 Clustering 10 Association Rules

Course Materials No textbook is required for this course. Where to get course materials?

Course Websites community.mis.temple.edu/mis2502sec003f18 Canvas: Usage Community Site: community.mis.temple.edu/mis2502sec003f18   syllabus, schedule, class announcements, slide decks, in-class exercises, assignment instructions, as well as other course documents. Canvas: canvas.temple.edu assignment submission, sharing videos/recordings, posting grades.

Evaluation and Grading Item Percentage Exams (3) 60% Assignments (8) 24% Group Project 6% In-class activities 5% Presence & Participation

Exams There will be three exams Despite some natural overlap in material between the exams, the exams are not intended to be cumulative Make-up exams will not be given under most circumstances.

Assignments Deliverables: Submit through Canvas # Assignment 1 ER Modeling 2 SQL #1 – Getting Data out of the Database 3 SQL #2 – Putting Data into the Database 4 ETL in Excel 5 Introduction to working with R/RStudio 6 Decision Trees 7 Clustering 8 Association Rules

Late Assignment Policy All assignments will be assessed a 50% penalty (subtracted from that assignment’s score) for the first day (i.e. 24 hours) they are late. No credit will be given for assignments turned in more than 24 hours past the deadline. Equipment failure is not an acceptable reason for turning in an assignment late

A Note on Regrade Requests Must be submitted within 1 week of the date when the grade was returned. I reserve the right to regrade the entire assignment/exam and thus your grade may go up or down.

In-Class Activities Very hands on in nature Deliverables: examples and datasets Deliverables: Submit through Blackboard Allowed to miss two submissions for in-class activities Graded by success or fail

Presence & Participation Perfect score = frequent presence + active participation Unexcused absence: Two unexcused absence without penalty E.g., missing a class because of a job interview, contests or meetings related to professional development Excused absence: Only allowed for extreme circumstances such as illness or family emergency and requires documentation Please contact me by e-mail as soon as possible

Plagiarism and Academic Dishonesty Assignments and exams are to be done individually and should represent your own work. Plagiarism and academic dishonesty are prohibited, such as Copying from another student’s assignment or exam 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 Submitting work done for a different course or section without the instructor’s approval ahead of time Helping others to plagiarize or cheat, or doing the work of another person

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