This course introduces students to the science of business analytics while casting a keen eye toward the artful use of numbers found in the digital space. The goal is to provide businesses and managers with the foundation needed to apply data analytics to real-world challenges they confront daily in their professional lives. Students will learn to identify the ideal analytic tool for their specific needs; understand valid and reliable ways to collect, analyze, and visualize data; and utilize data in decision making for their agencies, organizations or clients.
강의 계획 - 이 강좌에서 배울 내용
주
1완료하는 데 6시간 필요
Course Introduction
With the first module, we will measure and identify satisfied customers to adjust product or service accordingly. To measure the customer satisfaction we will measure expectations, performance and disconfirmation of the offered product or service. We will also provide an overview of marketing analytics used to gauge the effectiveness of different marketing activities. Finally, we will go through measurement and scaling techniques.
20개 동영상 (총 132분), 6 readings, 5 quizzes
20개의 동영상
Interview with Monica Penagos – MOOC intro8m
Tour of R and RStudio5m
Projects3m
Math Function4m
Scalar Variables6m
Column Vectors9m
Data Frame6m
Data Frame Import2m
Help and Cheat Sheets2m
Interview with Monica Penagos – Intro to Customer Satisfaction1m
Lesson 1-1.1 Introduction to Marketing Analytics and Customer Satisfaction5m
Lesson 1-1.2 Marketing Analtyics – A Brief History12m
Lesson 1-2 Customer Satisfaction12m
Lesson 1-3.1 Measurements and Scaling Techniques – Introduction3m
Lesson 1-3.2 Measurements and Scaling Techniques – Primary Scales of Measurement11m
Lesson 1-3.3 Measurements and Scaling Techniques – Comparative Scaling9m
Lesson 1-3.4 Measurements and Scaling Techniques – Non-Comparative Scaling9m
Lesson 1-4.1 Experiment Design: Key Concepts9m
Lesson 1-4.2 Experiment Design: Controlling for Experimental Errors8m
6개의 읽기 자료
Syllabus30m
About the Discussion Forums10m
Glossary10m
Update Your Profile10m
Module 1 Overview20m
Module 1 Readings1시 30분
5개 연습문제
Orientation Quiz10m
Lesson 1-1 Practice Quiz4m
Lesson 1-2 Practice Quiz4m
Lesson 1-3 Practice Quiz10m
Module 1 Graded Quiz30m
주
2완료하는 데 4시간 필요
Module 2
We will explore the marketing world through process of A/B testing, design of experiments, data analysis, and hypothesis testing. Next, we study Analysis of Variance (ANOVA) which is used to determine significant differences between two or more categorical groups. We will study ANOVA’s assumptions, test inference and different types of ANOVA. We also spend some time in designing experiments.
8개 동영상 (총 78분), 2 readings, 4 quizzes
8개의 동영상
Lesson 2-1.1 A/B Testing: Introduction11m
Lecture 2-1.2 A/B Testing: Types of Tests7m
Lesson 2-1.3 A/B Testing: R Example10m
Lesson 2-2.1 ANOVA – Introduction14m
Lesson 2-2.2 One-Way ANOVA – Insect Spray Example8m
Lesson 2-2.3 One-Way ANOVA – Insect Spray Example in R10m
Lesson 2-2.4 Two-Way ANOVA – Tooth Growth Example11m
2개의 읽기 자료
Module 2 Overview20m
Module 2 Readings7m
3개 연습문제
Lesson 2-1 Practice Quiz8m
Lesson 2-2 Practice Quiz16m
Module 2 Graded Quiz30m
주
3완료하는 데 4시간 필요
Module 3
We will learn about the Binary Outcome model using Logit function. The Logistic regression is sued when the dependent variable has a binary outcome. Next, we cover Multidimensional Scaling (MDS). MDS is used in marketing to understand the pair-wise similarity of the individual cases of data-set. This technique maps the individual cases onto a 2-dimensional Cartesian graph for visual analysis.
7개 동영상 (총 63분), 2 readings, 4 quizzes
7개의 동영상
Lesson 3-1.1 Binary Outcome Model – Logit Model12m
Lesson 3-1.2 Binary Outcome Model – Logit Model Example 111m
Lecture 3-1.3 Binary Outcome Model – Logit Model Example 27m
Lesson 3-2.1 Multi-Dimensional Scaling Introduction10m
Lesson 3-2.2 Conducting an MDS Analysis9m
Lesson 3-2.3 Multi-Dimensional Scaling – R Example9m
2개의 읽기 자료
Module 3 Overview20m
Module 3 Readings30m
3개 연습문제
Lesson 3-1 Practice Quiz4m
Lesson 3-2 Practice Quiz5m
Module 3 Graded Quiz30m
주
4완료하는 데 4시간 필요
Module 4
We will learn about Conjoint Analysis to understand how individuals combine and weigh different attributes. We will introduce the concept of conjoint analysis and part-worth utilities. We will survey different approaches before focusing our attention to the classical conjoint analysis. We will reinforce our understanding by studying an example in detail before running examples of the analysis in R
11개 동영상 (총 70분), 2 readings, 3 quizzes
11개의 동영상
Lecture 4-1.1 Conjoint Analysis – Introduction7m
Lesson 4-1.2 Conjoint Analysis – Data Collection Methods9m
Lesson 4-1.3 Conjoint Analysis – Types of Conjoint Analysis2m
Lesson 4-1.4 Conjoint Analysis – Part-Worth Utilities3m
Lesson 4-1.5 Conjoint Analysis Example – Ferry Fares10m
Lesson 4-1.6 Conjoint Analysis – Attributes10m
Lesson 4-1.7a Conjoint Analysis R Example – Setup5m
Lesson 4-1.7b Conjoint Analysis R Example – Part 110m
Lesson 4-1.7c Conjoint Analysis R Example – Part 25m
Course Summary: Applying Data Analytics in Marketing1m
2개의 읽기 자료
Module 4 Overview20m
Module 4 Readings1h
2개 연습문제
Lesson 4-1 Practice Quiz24m
Module 4 Graded Quiz30m
강사
Sung Won Kim
Associate ProfessorBusiness Administration
일리노이대학교 어버너-섐페인캠퍼스 정보
The University of Illinois at Urbana-Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni. Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs.
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