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- 86% Case Studies in Data Mining with R

Case Studies in Data Mining with R

$94.99 $12.99Track price

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8.2/10 (Our Score)
Product is rated as #37 in category R

Case Studies in Data Mining was originally taught as three separate online data mining courses. We examine three case studies which together present a broad–based tour of the basic and extended tasks of data mining in three different domains: (1) predicting algae blooms; (2) detecting fraudulent sales transactions; and (3) predicting stock market returns. The cumulative hands–on 3–course fifteen sessions showcase the use of Luis Torgo’s amazingly useful Data Mining with R (DMwR) package and R software. Everything that you see on–screen is included with the course: all of the R scripts; all of the data files and R objects used and/or referenced; as well as all of the R packages’ documentation. You can be new to R software and/or to data mining and be successful in completing the course. The first case study, Predicting Algae Blooms, provides instruction regarding the many useful, unique data mining functions contained in the R software ‘DMwR’ package. For the algae blooms prediction case, we specifically look at the tasks of data pre–processing, exploratory data analysis, and predictive model construction. For individuals completely new to R, the first two sessions of the algae blooms case (almost 4 hours of video and materials) provide an accelerated introduction to the use of R and RStudio and to basic techniques for inputting and outputting data and text. Detecting Fraudulent Transactions is the second extended data mining case study that showcases the DMwR (Data Mining with R) package. The case is specific but may be generalized to a common business problem: How does one sift through mountains of data (401,124 records, in this case) and identify suspicious data entries, or outliers ? The case problem is very unstructured, and walks through a wide variety of approaches and techniques in the attempt to discriminate the ormal , or ok transactions, from the abnormal, suspicious, or fraudulent transactions. This case presents a large number of alternative modeling approaches, some of which are appropriate for supervised, some for unsupervised, and some for semi–supervised data scenarios. The third extended case, Predicting Stock Market Returns is a data mining case study addressing the domain of automatic stock trading systems. These four sessions address the tasks of building an automated stock trading system based on prediction models that utilize daily stock quote data. The goal is to predict future returns for the S&P 500 market index. The resulting predictions are used together with a trading strategy to make decisions about generating market buy and sell orders. The case examines prediction problems that stem from the time ordering among data observations, that is, from the use of time series data. It also exemplifies the difficulties involved in translating model predictions into decisions and actions in the context of ‘real–world’ business applications.

Instructor Details

Dr. Geoffrey Hubona has held full-time tenure-track, and tenured, assistant and associate professor faculty positions at 4 major state universities in the United States since 1993. Currently, he is an associate professor of MIS at Texas A&M International University where he teaches for-credit courses on Business Data Visualization (undergrad), Advanced Programming using R (graduate), and Data Mining and Business Analytics (graduate). In previous academic faculty positions, he taught dozens of various statistics, business information systems, and computer science courses to undergraduate, master's and Ph.D. students. He earned a Ph.D. in Business Administration (Information Systems and Computer Science) from the University of South Florida (USF) in Tampa, FL; an MA in Economics, also from USF; an MBA in Finance from George Mason University in Fairfax, VA; and a BA in Psychology from the University of Virginia in Charlottesville, VA. He is the founder of the Georgia R School (2010-2014) and of R-Courseware (2014-Present), online educational organizations that teach research methods and quantitative analysis techniques. These research methods techniques include linear and non-linear modeling, multivariate methods, data mining, programming and simulation, and structural equation modeling and partial least squares (PLS) path modeling.

Specification: Case Studies in Data Mining with R

Duration 22 hours
Year 2020
Level All
Certificate Yes
Quizzes No

7 reviews for Case Studies in Data Mining with R

3.4 out of 5
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  1. Avatar

    Steffen Weiler

    Geht erst am Ende eines Clips auf interessante, tiefergehende Punkte ein.

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  2. Avatar

    sai Kumar SVS

    quality is good but could improve further.

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  3. Avatar

    Brenden Morley

    Awesome info and demostratrions. Just a bit wordy

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  4. Avatar

    Raviteja Mulpuri

    I love the comments mentioned in the scripts. They help me to understand the script without having to look at the video second time. SO far I have completed two sections and this is my second course from this professor. Amazing explanations. Love his courses 🙂

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    William Maphosa

    good pace

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    Roberto Garuti

    The case studies are useful to understand how to approach such problems in real life. However the scripts are not easy to understand and the explanation is not very clear. It’s not that different from reading the book this course is based on. It’s overall valuable, it presents packages and functions that can be very useful for these types of problems even though it would take a lot more than this course to get a good grasp of them. I suggest to download the scripts and workspaces and try to go through them first by yourself and just go to a lecture only if something is not clear. It definitely saved me time to work this way

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    Samuel Kruse

    Really slow talking but when sped up and amount to fast

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    Case Studies in Data Mining with R

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