Your Guide to Business Analysis Tools for 2025
January 29, 2025
Article
This course is part of Business Analytics Specialization
Instructors: Unnati Narang
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(158 reviews)
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Intermediate level
A basic familiarity with R is recommended.
(158 reviews)
Recommended experience
Intermediate level
A basic familiarity with R is recommended.
Apply quantitative techniques in marketing analytics to real-world scenarios.
Understand causal analysis, experimental and quasi-experimental approaches broadly.
Develop an understanding of linear/logistic regression and various computational text summarization methods.
Perform different analyses discussed using R programming.
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This course introduces students to marketing analytics through a wide range of analytical tools and approaches. We will discuss causal analysis, survey analysis using regression, textual analysis (sentiment analysis), and network analysis. This course aims to provide the foundation required to make better marketing decisions by analyzing multiple types of data related to customer satisfaction.
In the first module, we will discuss analytics in marketing and dive into causal analysis, an important tool for analytics. We will start with a broad overview of why analytics is important for marketers, what are the various types of data, the process of applying analytics in marketing, and the different types of analytics. We will then delve deeper into causal analysis.
19 videos7 readings2 assignments1 discussion prompt1 plugin
In the second module, we will focus on the analysis of survey data using regression. Surveys are one of the key tools used by organizations to measure important constructs like customer satisfaction. We will start with a broad understanding of the concept of customer satisfaction and various ways to measure it. Next, we will discuss the tools to analyze survey data. We will specifically focus on two regression methods—linear and logistic regressions. Finally, we will conclude the module with a hands-on logistic regression demonstration using an airline customer satisfaction survey dataset with R.
7 videos2 readings1 assignment
We will learn about the various methods of text analysis. We will first introduce you to sentiment analysis—the most prevalent means of analyzing customer satisfaction with textual data. We will demonstrate the sentiment analysis steps via R Studio. Then, we will shift our focus to text summarization techniques. We begin by listing the pre-processing steps required to bring the text to an analyzable form. Next, we look at how the frequency counts of multi-word phrases of pre-processed text can reveal the common terms being discussed. Building on top of the n-grams, we move onto a more intelligent method to automatically detect quality phrases. We will also discuss the LDA Topic Modeling - a very popular way to detect topics in a body of texts. We will wrap up this module with a highlight on supervised machine learning and an example of its application.
7 videos2 readings1 assignment1 peer review
We will introduce a method to analyze customer satisfaction influence using social media data. Social networks are the perfect dataset to utilize network analysis to understand how people are interacting with other people and forming networks. Identifying a pattern in social media relationships can be useful when making marketing decisions. We will also review influencer brand personality analysis that can be used as a method for brands to find influencers similar in personality to themselves.
6 videos4 readings1 assignment1 plugin
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
Instructor ratings
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
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This course is part of the following degree program(s) offered by University of Illinois Urbana-Champaign. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹
This course is part of the following degree program(s) offered by University of Illinois Urbana-Champaign. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹
University of Illinois Urbana-Champaign
Degree · 2 – 3 years
University of Illinois Urbana-Champaign
Degree · 8 months
University of Illinois Urbana-Champaign
Degree · 6-10 months
¹Successful application and enrollment are required. Eligibility requirements apply. Each institution determines the number of credits recognized by completing this content that may count towards degree requirements, considering any existing credits you may have. Click on a specific course for more information.
158 reviews
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Reviewed on Nov 26, 2020
This course is really insightful. Explanation done very well, quizzes is related and challenging. Although I suggest you have a statistical background before taking this course
Reviewed on Jan 3, 2021
Very informative. Good beginning to start the journey into analytics for marketers.
Reviewed on Jul 6, 2020
If the peer reviews were done faster it would be better
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