Applied Data Mining for Business Analytics LiveLessons Video Training
This easy video tutorial is the fastest way to master modern data science best practices and use them to promote timely, evidence-based decision-making! Applied Data Mining LiveLessons demystifies current best practices, showing how to uncover hidden patterns and leverage them to improve all aspects of business performance.
Drawing on extensive experience as a researcher, practitioner, and instructor, Dr. Dursun Delen shows you exactly how analytics and data mining work, why theyve become so important, and how to apply them to your problems. Delen reviews key concepts, applications, and challenges; introduces advanced tools and technologies, including IBM Watson; and discusses privacy concerns associated with modern data mining. Next, he guides you through the entire data mining process, introducing KDD, CRISP-DM, SEMMA, and Six Sigma for data mining. Youll watch him demonstrate prediction, classification, decision trees, and cluster analysis…key algorithms such as nearest neighbor…artificial neural networks…regression and time-series forecasting…text analytics and sentiment analysis…big data techniques, technologies, and more. In just hours, youll be ready to analyze huge volumes of data, discover crucial new insights, and make better, faster decisions!
6+ Hours of Video Instruction
The hands-on video guide to using data mining to enable timely, actionable, evidence-based decision-making throughout your organization!
– Beginner to Intermediate
What You Will Learn
– Core concepts of analytics, big data, and other related concepts
– The role and the importance of data mining in analytics and evidence-based managerial decision making
– Critical success factors for data mining
– Proven processes for carrying out successful data mining projects
– Basic concepts of these related fields: text mining, web mining, and social media mining
– Where to find commercial and free/open source data mining software resources
Who Should Take This Course
– For all professionals on analytics teams; professionals seeking certification; and for BA, MBA, and MS students in analytics programs (degree, certificate, or concentration)
– For students in online university programs in operations research, MIS, decision sciences, management science, analytics, data mining, and/or data analysis
– There are no prerequisites for this course
– Basic knowledge in data, information technology, or managerial decision-making will be helpful but is not essential
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Table of Contents
Lesson 1: Introduction to Analytics
Understand how data mining and analytics fit together, why analytics has become so popular, key analytics applications and challenges, and todays cutting edge of analytics: IBM Watson.
Lesson 2: Introduction to Data Mining
Discover what data mining is and isnt…explore todays most common data mining applications…see what kind of patterns data mining can discover…explore popular data mining tools…consider privacy issues associated with data mining.
Lesson 3: The Data Mining Process
Explore key data mining-related processes and methodologies, including KDD, CRISP-DM, SEMMA, and Six Sigma for Data Mining, and choose the best options for your own projects.
Lesson 4: Data and Methods in Data Mining
Understand the role of data in data mining…preprocess your data…use prediction, classification, decision trees, cluster analysis, and the k-Means Clustering and Apriori algorithms…replace data mining misconceptions with realities.
Lesson 5: Data Mining Algorithms
Use nearest neighbor and similarity measure algorithms…explore artificial neural networks and support vector machines…use linear and logistic regression…perform time-series forecasting.
Lesson 6: Text Analytics and Text Mining
Explore Natural Language Processing…text mining applications, processes, and tools…and RapidMiner demonstration.
Lesson 7: Big Data Analytics
See where big data comes from…review the Vs that define big data…explore big data concepts, business problems, and technologies…discover the role of data scientists…get started with stream analytics and data stream mining.
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