代写MISY262: Fundamentals of Business Analytics Summer 2024代写R语言

MISY262: Fundamentals of Business Analytics

Semester: Summer, Year: 2024

2. Course Description

Description

Introduction to the basic tools and methods of data analytics for business. Topics include inferential statistics, predictive models, business processes, and methods of presenting results. Includes light programming.

Objectives

Analytics involves both artistic and scientific approaches, aiming to unveil and comprehend historical trends within an organization's  data to predict and enhance business performance considering anticipated environmental, economic, and competitive factors. Consequently, businesses are more inclined to utilize business analytics for assessing and refining decision-making processes. As such, proficiency in gathering, analyzing, visualizing, and leveraging data for crucial decisions is an essential skill for contemporary business students. This course serves as an introductory exploration into the realm of Business Analytics, highlighting its reliance on exploratory and predictive models to offer evidence-based suggestions, aiding organizations in optimizing their decisions and actions. This course will introduce students to Exploratory Data Analysis (EDA), Data Visualizations, Handling Missing Data and Imputation, Basic Statistical Techniques, Linear Regression, and Logistic Regression. Students will also acquire a managerial understanding of business analytics applications in business decisions through hands-on use of R/Rstudio tool.

Learning Outcomes

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

1.   Understand the big picture of business analytics as a transformative force in the world of modern business.

2.   Define the basic language and concepts within the field of business analytics.

3.   Explain the differences between descriptive, predictive, and prescriptive analytics, and the business questions that can be answered with each approach.

4.   Critique the different types of data being used in business analytics.

5.   Evaluate the steps of the exploratory data analysis (EDA) process including data visualization, data imputation, and statistical analysis.

6.   Use R/Rstudio to apply all steps of the EDA process.

7.   Evaluate and apply linear and logistic regression models using R/Rstudio.

Prerequisites

MATH 201 or STAT 200 (or equivalent). Probability distributions and hypothesis testing must be learned in advance of this course.

Course Delivery

This class will be delivered Online Asynchronous.

3. Learning Resources

Required Learning Materials

No textbook is required for this course. All learning materials, readings, assignments, etc. will be available on Canvas.

Technology

•    A laptop computer (preferably PC not Mac) and Internet Connection.

•     In this course we will use R/R Studio. These are open-source free software applications. You do not need to buy any software for this course. We will install these on your computers during class.

Watch this video to download and install R and Rstudio for Windows

https://www.youtube.com/watch?v=H9EBlFDGG4k

Watch this video to download and install R and Rstudio for Mac

https://www.youtube.com/watch?v=I5WIMX4LK8M

• Canvas: In this class, Canvas, UD's online learning management system, will be used for all course activities and communication channels. All assignments will be posted through the Canvas course site unless otherwise directed. Announcements concerning the class, e.g., snow days, change of due dates, upcoming requirements, will be posted on Canvas. It  is expected that you should be reading these announcements as they are sent.

You should receive announcements from your professor on a regular basis.  Ensure your canvas is set up to send those announcements to your email or mobile device.  See How do I manage my Canvas notification settings as a student?

Information on how to use Canvas is available through theCanvas Student Guide.

Canvas can also be accessed viaMyUD.

• Zoom: In this class, Zoom may be used for live office hours.  Zoom is a web-based application and be sure to install the small program that will download to your computer.  You only need to install the program once. From a mobile device, download the free Zoom app and type in the class meeting ID number.

Information on how to test your computer's audio and video can be found on Zoom's website.

Learn more info aboutZoom at UD.

4. Learning Assessments

Final Grade Breakdown

The final course grade will be calculated using the following components:

Course Component	Percentage of Total
Homework Assignments	30
Exam 1 (after module 2)	20
Exam 2 (after module 4)	20
Final Exam (cumulative exam)	30
Grand Total	100%

Homework Assignments

All homework assignments are to be done individually NOT collaboratively with other students. You must complete these assignments without assistance from other students, from the instructor, or any other person. You cannot ask anyone about how to complete the tasks in these assignments. You may not share files or parts of files with other students by any means (no photographing, etc.). The homework assignments are due via Canvas on the day and time designated in the syllabus or posted in Canvas. These assignments cannot be emailed directly to your instructor. For the homework assignments, you must download the starter file from Canvas, complete the assignment by using that file, and submit your work as an html file in Canvas. No rmd files are accepted since all the calculations and graphs should already be presented in your submitted files. Submitting someone else’s file or part of someone else’s file is prohibited.

Exams

The exams will be closed readings, closed notes, and no Internet/Web browsing. The exams will be based on the material covered during the class and the homework assignments. You must work alone on the exams and sign the department Honor Code. Any suspected violations of the Honor Code will be reported to the Office of Student Conduct.

Late Submissions

A penalty of 20% per day late will be assessed on homework assignments submitted after the due time. This includes any assessment submitted late on the due date. A day is defined as 24 hours. Thus, any assessment submitted within 24 hours after the due date can, at best, earn 80% of the points for that assessment. Zero credit will be given for any assessment submitted more than 48 hours (2 days) after the due date.

Grading Scale

Students will be assigned the following letter grade based on the calculation coming from the course assessment section.

A (94 – 100) A- (90 – <94)

B+	(87 – <90)	C+	(77 – <80)	D+	(67 – <70)
B	(84 – <87)	C	(74 – <77)	D	(64 – <67)
B-	(80 – <84)	C-	(70 – <74)	D-	(61 – <64)

F (< 61)

Final Grade Notes

All grades are final except for obvious grading errors.

Your final grade includes bonus or extra points that may be offered throughout the semester. You CANNOT ask for more bonus or extra points, with or without extra assignments or projects. Your instructor CANNOT keep on adding bonuses or extra points to your final grade until you get the grade that you want.

Your instructor CANNOT give extra or make up assignments or projects to individual students to help improve their grade. This would be unfair to other students.

You CANNOT ask for extra or make up assignments or projects to help improve your grade.

Your instructor reserves the right to adjust the point requirements for a given grade depending on the performance of the class as a whole.

Your instructor’s evaluation process concerning your work is not open to debate, but suggestions are welcomed.

If an assignment or exam that is listed above is not assigned (e.g., because of time concerns or a change in schedule), grades will be assigned on a prorated basis using the remaining categories.