Applied Data Mining for Business Analytics

This hands-on video guide to using data mining to enable timely, actionable, evidence-based decision-making throughout your organization!

Applied Data Mining for Business Analytics

Course Description

This easy video tutorial from Pearson 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 they’ve 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... Read More »

This easy video tutorial from Pearson 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 they’ve 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. You’ll 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, you’ll be ready to analyze huge volumes of data, discover crucial new insights, and make better faster decisions!

What you’ll 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
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Course Details:

Target Audience

  • Professionals on analytics teams
  • Professionals seeking a certification
  • University students in operations research, MIS, decision sciences, management science, analytics and data mining
  • Data Analytics

Prerequisites

  • There are no prerequisites for this course, however, a basic knowledge in data, information technology, or managerial decision-making is considered an asset, but not essential.
Certificate Info:

Type of Certification

Certificate of Completion

Format of Certification

Digital and Print

Professional Association/Affiliation

This certificate is issued by Pearson LearnIT

Method of Obtaining Certification

Upon successful completion of the course, participants will receive a certificate of completion.

Course Outline

Understand how data mining and analytics fit together, why analytics has become so popular, key analytics applications and challenges, and today’s cutting edge of analytics: IBM Watson.
Discover what data mining is and isn’t...explore today’s 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.
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.
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.
Use nearest neighbour and similarity measure algorithms...explore artificial neural networks and support vector machines...use linear and logistic regression...perform time-series forecasting.
Explore Natural Language Processing... text mining applications, processes, and tools...and RapidMine r demonstration.
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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