As Big Data takes center stage for business operations, data mining becomes something that salespeople, marketers, and C-level executives need to know how to do and do well. Generally, data mining is the process of finding patterns and correlations in large data sets to predict outcomes. There are a variety of techniques to use for data mining, but at its core are statistics, artificial intelligence, and machine learning. Companies and organizations are using data mining to get the insights they need about pricing, promotions, social media, campaigns, customer experience, and a plethora of other business practices.
To help you get a better handle on data mining, we have searched for resources from Big Data and data mining experts, top marketers and data scientists, leading Big Data and data analysis software solutions providers, and other data mining thought leaders, to compile our list of the top online learning resources for data mining. Below, you will find everything from articles and journals to tutorials and techniques for data mining.
While we have listed our top data mining resources in no particular order, we have included a table of contents to make it easier for you to jump to the resources categories that are of most interest to you.
- Articles and Journals
- Courses and Lecture Notes
- Videos, Webinars, and Wikis
Business 2 Community contributors cover news and trends in social media, digital marketing, content marketing, social selling, and more. Liran Malul’s Business 2 Community data mining article explains that one of the best approaches to data mining is to first identify the problem you have and how you would like to solve it, and then determine the best data mining technique to gain the insights you need. Malul then highlights the various important data mining techniques that are in CRM solutions.
Three key ideas we like from Data Mining Techniques in CRM to Improve Data Quality Management:
- Anomaly detection is a key data mining technique because anomalies can provide actionable information, since they deviate from the data set’s average
- Clustering is important for identifying similar data sets and understanding the similarities and differences within data
- Regression analysis is an advanced data mining technique that determines customer satisfaction levels and how they affect customer loyalty
Elizabeth Dwoskin, Wall Street Journal reporter covering privacy, innovation, and algorithms in Big Data, reminds us in this data mining article that data mining is not just for corporations. The New York City Fire Department is using data mining to predict which buildings will erupt in fire, and their data analysts have been working since July 2014 to determine which buildings to inspect.
Three key ideas we like from How New York’s Fire Department Uses Data Mining:
- Data correlations are key to making predictions
- There are so many relevant factors in data that it is necessary to build algorithms to correctly mine the data
- Municipalities increasingly are using data to improve services
KISSmetrics helps organizations optimize their digital marketing, so they know a thing or two about data mining. In a recent blog post, VP of Marketing at KISSmetrics Neil Patel explores the top 10 ways to use data mining and become more competitive. As he explains, data mining helps companies to deliver more value to customers and generate more revenue in return.
Three key ideas we like from 10 Ways Data Mining Can Help You Get a Competitive Edge:
- Data mining provides insight that can increase customer loyalty, unlock hidden profitability, and reduce client churn
- Basket analysis is not just for stores: it is a data mining technique appropriate for evaluating use of credit cards, evaluating patters of phone use, identifying insurance claim fraud, and more
- Use data mining to forecast sales and create three cash flow projections – realistic, optimistic, and pessimistic
As part of CNN Money’s Small Business Resource Guide, Cindy Waxer shares anecdotes about small businesses using data mining to crunch customer data and increase sales while reducing customer turnover. Waxer explains that large corporations can afford expensive servers and data scientists, but small businesses can take advantage of web-based, cost-effective data mining alternatives.
Three key ideas we like from How Data Mining Can Boost Your Revenue by 300%:
- Data mining needs to be a quick and easy process so that companies can use their data to make real-time decisions
- Segment your customers with various attributes to make better customer behavior predictions
- Use predictive analytics from data mining to customize campaigns and services
Business News Daily Assistant Editor Nicole Fallon explores the fine line customers walk between wanting companies to gather their data and not wanting companies to analyze their digital data. Marketers and companies need to balance “being helpful and being invasive by only collecting social data from customers who follow them, and avoid anything that appears to be part of a conversation among other users.” Erring on the side of caution is important when mining customer data because the last thing companies want to do is drive customers away when gathering the very data they want to use in order to keep them.
Three key facts we like from Customers to Retailers: Don’t Stalk My Twitter!:
- The majority of consumers are open to data mining if it results in a better online shopping experience, especially when it comes to personalized discounts
- 55% of consumers approve of companies mining their website search history, but more than 75% of consumers are uncomfortable with companies analyzing their social media posts
- Companies should observe customer responsiveness and shopping history and use that data to customize outreach
The Marketing Research Association is a nonprofit that represents the survey, opinion, and marketing research profession. In this data mining article for the Marketing Research Association, Eric Wright, VP of solutions consulting at Allegiance, Inc., explores how to use text analytics and data mining to gain actionable customer insights. He also recognizes the fact that there is so much data and information available that it can be difficult to find the most valuable information in the middle of the unstructured content.
Three key ideas we like from The Power of Babble: Using Text Analytics and Data Mining to Uncover Actionable Customer Insights:
- Data mining enables companies to uncover information’s hidden value and identify and refine patters and trends among hundreds or thousands of variables
- Using text analytics prior to data mining for customer loyalty makes it possible to determine which variables will have the largest impact on loyalty scores and satisfaction ratings
- Combining text analytics with data mining produces actionable insights for achieving business goals that include operational efficiency, customer engagement, and product innovation
7. Data Mining
Dr. V. Kumar is the executive director of the Center for Excellence in Brand & Customer Management and the director of the Ph.D. program in marketing at the J. Mack Robinson College of Business at Georgia State University. His course material on data mining is a treasure trove of everything data mining and covers such topics as data mining and business value and the date mining process.
Three key ideas we like from Data Mining:
- “Data mining provides businesses with the ability to make knowledge-driven strategic business decisions”
- Companies must implement standardized data mining procedures to extract customer intelligence and value from their data
- Data mining helps companies target customers and identify customer segments withs similar behaviors and needs
MIT OpenCourseWare offers free lecture notes, exams, and videos from MIT, without any registration process. The Data Mining Course, instructed by Professor Nitin Patel, is a graduate course featuring selected lecture notes, exams, and assignments.
Key course content:
- Data Mining Overview Lecture Notes
- k-Means Clustering, Hierarchical Clustering Lecture Notes
- Course Materials Package
Dr. Gregory Piatetsky-Shapriro is president of KDnuggets and an analytics, Big Data, data mining, and data science expert. He and Professor Gary Parker of Connecticut College offer the teaching modules for a one-semester introductory course on Data Mining. This data mining course is intended for advanced undergraduate students or first-year graduate students.
Key course content:
- Introductory Data Mining Tutoria
- Lecture notes
- Additional lecture – From Data Mining to Knowledge Discovery: An Introduction
RDataMining.com is a leading resource for R and data mining, offering examples, documents, tutorials, resources, and training on data mining and analytics with R. RDataMining.com also offers a list of free online data mining courses, covering data analysis, a data mining specialization, social network analysis, and more.
Three key data mining courses:
Stanford CPD delivers Stanford courses, certificates, and degrees for qualified professionals online, at Stanford, and at work. The STATS202 – Data Mining and Analysis course is a data mining course instructed by Lester Mackey, assistant professor of statistics and Rajan Patel, instructor. The course will help students to learn how to apply data mining principles and dissect complex data sets, including those in large databases or through web mining.
Key course topics:
- Decision trees
- Association rules
- Case-based methods
Cost: Contact for tuition and fee pricing
Coursera strives to provide universal access to a great education. Their data mining course, Mining Massive Datasets, is instructed by Jure Leskovec, Anand Rajaraman, and Jeff Ullman, all of Stanford University. The seven-week course is available at various times throughout the year, and it is best if students have taken courses in database systems, algorithms and data structures, and multivariable calculus and linear algebra.
Key course topics:
- Data stream mining
- Recommender systems
- Support-vector machines
Cost: Contact for course pricing
“The world’s largest destination for online courses,” Udemy offers Data Mining, an introductory course for understanding the patterns, processes, and tools associated with data mining. Data Mining includes 58 lectures and 6 hours of video and requires students to have a basic understanding of the IT industry and a knowledge of the English language.
Key course topics:
- Knowledge discovery in databases
- Advantages and disadvantages in data mining
- Minable information
The Online Graduate Data Mining Certificate Program is an online program for working professionals looking to acquire data mining or predictive analytics or data science skills through online courses. In addition to the graduate certificate in business data mining, students in the program may also earn three other certificates – SAS and OSU Data Mining Certificate, SAS and OSU Predictive Analytics Certificate, or SAS and OSU Marketing Data Science Certificate – depending on which courses they take and the credentials they achieve.
Key course content:
- Hands-on application of data analysis
- A unique blend of coursework in analytics, marketing, statistics, business, MIS and industrial engineering
- Quantitative approaches, statistical modeling, and machine learning algorithms, plus data visualization and exploration
Cost: Contact for tuition and fee pricing
Ian Witten is a professor of computer science in New Zealand, who originally is from the University of Calgary in Canada. His data mining course, Data Mining with Weka, provides an introduction to practical data mining with Weka. Weka is “a powerful, yet easy to use tool for machine learning and data mining.” It is worth noting that the course is ranked #3 in data science and Big Data for Class Central course rankings.
Key course topics:
- Data mining algorithms
- Implementing data mining with Weka
- Machine learning
Statistics.com offers more than 100 online statistics courses taught by leading authorities. All courses are four weeks in length. Data Mining in R is taught by Dr. Luis Torgo, an associate professor inthe Department of Computer Science at the University of Porto and a researcher at the Laboratory of Artificial Intelligence and Data Analysis (LIAAD). Dr. Torgo also authored the course.
Key course topics:
- Clustering and classification methods
- k-Nearest neighbors
- Strategies for handling unknown variable values
The Data Mining Specialization is offered by Coursera and was created by the University of Illinois at Urbana-Champaign. Consisting of five courses and a capstone project, students completing Data Mining Specialization will earn a certificate.
Key course topics:
- Pattern discovery in data mining
- Text retrieval and search engines
- Text mining and analytics
Data Mining: Concepts and Techniques is a data mining eBook by Jiawei Han and Micheline Kamber of the University of Illinois at Urbana-Champaign. This data mining eBook offers an in-depth look at data mining, its applications, and the data mining process.
Three key topics we like from Data Mining: Concepts and Techniques:
- Mining frequent patterns, associations, and correlations
- Advanced data, information systems, and advanced applications
- Integrating a data mining system with a database or data warehouse system
A publisher of science, technology, and medical reference books, textbooks, handbooks, and monographs, CRC Press offers Statistical and Machine-Learning Data Mining, a data mining eBook available for purchase or on a six-month or twelve-month rental agreement. Written by Bruce Ratner, the 542-page data mining book covers data mining methods, the importance of data, profit modeling, and much more.
Three key ideas we like from Statistical and Machine-Learning Data Mining:
- For better predictive modeling and analysis of big data, it is important to distinguish between statistical data mining and machine-learning data mining techniques
- Good statistical practice is crucial to proper data mining
- Statistical data mining has limitations that can be addressed by an alternative data-mining solution
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If you are looking for a quick introduction to data mining and data mining algorithms in R, the Data Mining Algorithms in R Wikibook is a good place to start. This data mining eBook includes a description and rationale for data mining, implementation details, and use cases.
Three key topics we like from Data Mining Algorithms in R:
- Principal Component Analysis (PCA) technique
- Frequent pattern mining
- Sequence mining
A Programmer’s Guide to Data Mining is a wildly popular data mining eBook by Ron Zacharski, a computer programmer and computational linguist. Zacharski currently is an assistant professor of computer science at University of Mary Washington in Fredericksburg, Virginia. The data mining guide covers practical data mining, collective intelligence, and building recommendation systems.
Three key topics we like from A Programmer’s Guide to Data Mining:
- Social filtering
- Implicit ratings and item-based filtering
IKANOW is an open, scalable information security platform that provides business intelligence to drive organization change. Their data mining eBook, Data Mining Tools and Techniques, is a robust resource that helps readers learn how to turn Big Data into actionable intelligence, especially for those in the healthcare, insurance, and finance fields.
Three key topics we like from Data Mining Tools and Techniques:
- Using data to create valuable industry opportunities
- Data mining techniques
- Key tools for deep web data mining
Cost: FREE, with email registration
ScienceDirect is a resource for full-text articles and chapters from more than 2,500 peer-reviewed journals and 33,000 books. Data Mining: Concepts and Techniques (Third Edition) is a comprehensive data mining resource offering 13 chapters on the concepts and techniques used in the data mining process. The data mining eBook focuses on data mining and the tools used in discovering knowledge from the data collected.
Three key topics we like from Data Mining: Concepts and Techniques (Third Edition):
- Data Preprocessing
- Advanced pattern mining
- Classification: Basic concepts and advanced methods
Robust Data Mining, a data mining eBook available through Springer, is the work of authors Petros Xanthopoulos, Panos M. Pardalos, and Theodore B. Trafalis. This data mining resource summarizes recent applications of robust optimization in data mining.
Three key topics we like from Robust Data Mining:
- There is a need for new algorithms to optimize existing data mining techniques
- Robust data mining research is a growing field
- Machine learning algorithms have robust counterpart formulations and algorithms that can address some of the challenges posed by machine learning algorithms
Written by Charu C. Aggarwal, Data Mining: The Textbook is a data mining resource that discusses the fundamental methods of data mining, data types, and data mining applications. This data mining resource is appropriate for any level of data mining student, from introductory to advanced.
Three key topics we like from Data Mining: The Textbook:
- Clustering, classification, association pattern mining, and outlier analysis methods and problems
- Various domains of data, including text data, time-series data, sequence data, graph data, and spatial data
- Data mining applications, including stream mining, web mining, ranking, recommendations, social networks, and privacy preservation
Written by Gordon S. Linoff and Michael J. A. Berry, Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, 3rd Edition, is a hefty data mining eBook at 888 pages. Considered a leading introductory book to data mining, this data mining resource centers on using the latest data mining methods and techniques to solve common business challenges.
Three key topics we like from Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, 3rd Edition:
- Core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, survival analysis, and more
- Best practices for data mining using even simple tools like Excel
- Data mining techniques and methods for addressing business problems and gaining business intelligence
Graham Williams writes about programming with data and a few of the more popular algorithms for data mining in this data mining eBook. A resource appropriate for readers without strong backgrounds in computer science and statistics, Data Mining with Rattle and R focuses on the hands-on end-to-end process of data mining.
Three key topics we like from Data Mining with Rattle and R:
- People completing the process of data mining need to make choices in methodology, data, tools, and algorithms
- Practical applications for data mining and using Rattle Data Mining Software
- Data understanding, data preparation, data refinement, model building, model evaluation, and practical deployment
In this data mining eBook chapter, Xavier Amatriain, Alejandro Jaimes, Nuria Oliver, and Josep M. Pujol provide an overview of the data mining techniques used in recommender systems. They explore the three steps of a basic process of data mining: data preprocessing, data analysis, and result interpretation, making this data mining eBook an approrpriate resource for beginners.
Three key topics we like from Chapter 2: Data Mining Methods for Recommender Systems:
- Sampling and dimensionality reduction as common preprocessing data mining methods
- Important classification techniques, including Bayesian networks and support vector machines
- K-means clustering algorithm and alternative algorithms
Dell offers a data mining resource that focuses on data mining techniques as part of its online statistics textbook. Covering an introduction to data mining for both predictive analytics and Big Data, Dell’s Data Mining Techniques is a useful data mining resource that also includes a video, visuals, and links to external resources.
Three key ideas we like from Data Mining Techniques:
- The goal of data mining is prediction, and predictive data mining is the most common type of data mining with the most direct business applications
- Data mining is a popular business information management tool that reveals knowledge structures for guiding decisions
- Crucial concepts in predictive data mining include boosting, data preparation, data reduction, deployment, and drill-down analysis
Kurt Thearling, VP of analytics at WEX, offers information about analytics and data science on his website, Thearling.com. One of his data mining resources is an overview of data mining techniques, which is excerpting from the book Building Data Mining Applications for CRM, which he co-wrote with Alex Berson and Stephen Smith.
Three key ideas we like from An Overview of Data Mining Techniques:
- Classical data mining techniques include statistics, neighborhoods, and clustering
- Next generation data mining techniques include trees, networks, and rules
- Next generation data mining techniques may be used for discovering new information within large databases or for building predictive models
ZenTut.com provides programming tutorials that are easy to follow in several languages and technologies. They also offer a data mining resource, Data Mining Techniques, that covers a range of the major data mining techniques have been recently developed to address data mining projects.
Three key ideas we like from Data Mining Techniques:
- Some of the most current data mining techniques include association, classification, clustering, prediction, sequential patterns, and decision trees
- The association technique, also known as the relation technique, is often used in market basket analysis, in order to identify a set of products that customers frequently purchase together
- Profit prediction is a data mining technique that relies on historical sale and profit data, in order to create a fitted regression curve that is used for predicting profits
Internal Auditor Magazine is on a mission to “arm practitioners with the cutting-edge information and practices they need to do their jobs today and tomorrow.” Their Data Mining 101: Tools and Techniques is a resource that provides an in-depth overview of data mining and is well suited to data mining beginners.
Three key ideas we like from Data Mining 101: Tools and Techniques:
- Auditors can implement data mining tools and techniques to provide recommendations for improving business processes and discovering fraud
- Data mining helps to reduce the cost of acquiring new customers and improve the sales rates of new products and services
- While data mining is not new, changes in data mining techniques have helped organizations collect, analyze, and access data in new ways
Datafloq offers Big Data knowledge with the goal of helping everyone understand it better. Their blog post, Five Data Mining Techniques That Help Create Business Value, is a data mining resource rife with information about data mining techniques.
Three key ideas we like from Five Data Mining Techniques That Help Create Business Value:
- While people often use the term “data mining” to refer to a broad range of Big Data analytics, including collection, extraction, analysis, and statistics, data mining actually is the discovery of previously unknown interesting patterns, unusual records, or dependencies
- Data mining involves a few important classes of tasks, including anomaly or outlier detection, association rule learning, clustering analysis, classification analysis, and regression analysis
- More data results in better models, created through the use of data mining techniques, which lead to more business value for organizations
Tutorials Point offers tutorials on a host of topics, from programming languages to web design. With over 15 million readers reading 35 million pages per month, Tutorials Point is an authority on technical and non-technical subjects, including data mining. In fact, the data mining tutorial from Tutorials Point is intended for computer science graduates who are seeking to understand all levels of concepts related to data mining. This data mining resource is better suited to individuals with a basic understanding of schema, ER model, structured query language, and data warehousing.
Three key topics we like from Data Mining Tutorial:
- Data mining systems
- Decision tree induction
- Data mining applications and trends
The Data Mining Server (DMS) is an internet service providing online data analysis based on knowledge induction. Their data mining tutorial is a data mining resource that includes an introduction to the data mining process, its techniques, and its applications. This particular data mining resource is better suited to beginners.
Three key ideas we like from DMS – Data Mining Tutorial:
- Data mining begins first by identifying a problem to solve through the data mining process: problems may include optimizing response of customers to marketing campaigns, preventing fraudulent usage of credit cards, etc.
- Possible data mining goals may include increasing sales or preventing credit card or insurance fraud
- There are five main data mining modeling techniques: classification, prediction, dependency analysis, data description and summarization, and segmentation or clustering
Kurt Thearling, VP of analytics at WEX, created a comprehensive data mining tutorial that is 93 slides in length. His Introduction to Data Mining is a data mining resource that clearly explains exactly what data mining is and is not, goals of data mining, predictive models, and much more.
Three key ideas we like from An Introduction to Data Mining by Kurt Thearling:
- You must understand the patterns discovered through data mining so that you are able to act on them
- Data mining goes far beyond data warehousing and data visualization
- Data mining includes decision trees, nearest neighbor classification, neural networks, rule induction, and K-means clustering
The dean of Carnegie Melon University School of Computer Science, Andrew Moore has a background in statistical machine learning, artificial intelligence, robotics, and statistical computation for large volumes of data. He offers statistical data mining tutorials in the hopes that readers find them useful in their quest to learn more about data mining.
Three data mining resources we like from Statistical Data Mining Tutorials:
- Decision Trees
- Probability and Bayesian Analytics
- Regression and Classification with Neural Networks
With more than 127,300 views, Data Mining with STATISTICA – Session 1 is a popular data mining video available on YouTube. From StatSoft, Inc., now a part of Dell, this data mining video offers an introduction to data mining and covers hands-on tutorials of data mining applications. The video is presented by Jennifer Thompson, MS, and is the first in a series of 35 video sessions centering on data mining with STATISTICA.
Three key ideas we like from Data Mining with STATISTICA – Session 1:
- Data mining application types include classification, regression, and clustering
- STATISTICA Data Miner is a useful tool for solving business problems
- Data mining saves companies time and money
VideoLectures.NET, an award-winning free and open access educational video lectures repository, features lectures given by top scientists and scholars at conferences, workshops, and other events. VideoLectures.NET features more than 760 data mining video lectures from distinguished speakers, making it a robust data mining resource.
Three videos we like from VideoLectures.NET:
- The Battle for the Future of Data Mining
- Twitter Sentiment in Financial Domain
- Network Mining and Analysis for Social Applications
The Modeling Agency offers predictive modeling and data mining public training. One of their data webinars, Data Mining: Failure to Launch – How to Get Predictive Modeling Off the Ground and Into Orbit, typically is offered once a month. This 90-minute live interactive event is a vendor-neutral webinar that helps participants learn how to get started with data mining and persevere when data mining projects do not meet their full potential
Three key topics we like from Data Mining: Failure to Launch – How to Get Predictive Modeling Off the Ground and Into Orbit:
- The commonality of data mining implementation failure
- The rewards of proper data mining design and implementation
- Establishing an internal predictive modeling practice
A leading commercial provider of software and support for the R statistics language, Revolution Analytics offers the Introduction to R for Data Mining webinar, available on-demand. Presenter Joseph Rickert is technical marketing manager at Revolution Analytics, and the webinar focuses on data mining as an application area and how to use a basic knowledge of data mining techniques to become productive in R.
Three key topics we like from Introduction to R for Data Mining:
- The open source data mining GUI, Rattle, enables users to perform basic data mining functions such as exploring and visualizing data, building classification models on data sets, and using models to classify new data
- Simple R commands can take the place of Rattle
- Using R for both small and large data sets
The Analysis Factor provides statistical consulting, resources, and training to help researchers conduct quality work. One of their data mining resources, Data Mining Webinar with Peter Bruce, President, Statistics.com, features guest speaker Peter Bruce, co-author of Data Mining for Business Intelligence. The webinar gives a general overview of data mining techniques and is a good resource for those just beginning to become familiar with data mining.
Three key topics we like from Data Mining Webinar with Peter Bruce, President, Statistics.com:
- Using predictive modeling to predict known and unknown values
- Using clustering in customer segmentation
- Applying text analysis to Twitter feeds, Facebook content, emails, and more
Cost: FREE, with email registration
ShowWare, a full service, real-time ticketing solution that is redefining how facilities and event planners sell tickets to patrons, offers an analytics and data mining webinar on YouTube. Presented by Joseph Wettstead and Amy Russell of ShoWare, the webinar is 45 minutes in length and explores how data mining lowers costs and raises revenue.
Three key topics we like from Fall Webinar Series: Analytics & Data Mining:
- Utilizing data mining to manage costs
- Analyzing data with Google Analytics
- Using data mining to determine where and when to spend money on marketing
Social Science Space is a social network that joins social scientists who want to explore, share, and shape important issues in social science. Their share three webinars that offer tips on data mining, and all three are equally valuable data mining resources.
Three ideas we like from Three Webinars Give Tips on Data Mining:
- There are complex data needs associated with data mining projects
- International trade and economics pose challenges to data mining processes
- Emerging topics in data mining include the shift between open access and commercial publication of international data
Peter Leonard, Director of Digital Humanities Laboratory, and Lindsay King, Assistant Director of the Haas Art Library, both of Yale University, explore the rise in popularity of text and data mining in this 80-minute webinar. The webinar also demonstrates the Robots Reading Vogue project to demonstrate the data mining research applications.
Three key ideas we like from Text and Data Mining in the Humanities and Social Sciences – Strategies and Tools:
- Data mining is being spurred by three trends: unprecedented amount of digitized source material that is now available, software that handles the huge amounts of data, and improvements in data mining techniques and technology
- Text and data mining is an emerging technique and trend in research
- Text and data mining is applicable to social sciences, which can help marketers better understand customer behavior
A leading research and teaching institution, Stanford University offers Data Mining: The Tool of the Information Age, a webinar made available through the Stanford Center for Professional Development. The webinar features Dr. Rajan Patel, a visiting instructor in the statistics department at Stanford, who also is a search scientist at Google. This data mining webinar is nearly an hour in length.
Three key ideas we like from Data Mining: The Tool of the Information Age Revolution:
- The internet and improved computing technologies have made data mining necessary for companies and organizations seeking to gain valuable insight from the data
- Data mining is appropriate for healthcare, e-commerce, and several other sectors of the economy
- Data sets of nearly any scale may be subjected to data mining to help companies and organizations make meaning of their data and draw conclusions
Also made available by the Stanford University Center for Professional Development, Mining Online Data Across Social Networks is a webinar featuring Dr. Jure Leskovec, assistant professor of computer science at Stanford and author of the Stanford Network Analysis Platform (SNAP). The data mining webinar is a little over 60 minutes in length and explores the various approaches for tracking and predicting information in online networks.
Three key topics we like from Mining Online Data Across Social Networks:
- New data mining techniques are required to make predictions about the large-scale behavior of information networks
- Data mining blog posts and news media articles leads to finding patterns in the news over a daily scale-time
- Models for quantifying the influence of individual media sites on news stories’ popularity
BLG Data Research is a research center that explores the use of innovative and complex smart data to gain insights for business and local government. Their 45-minute data mining webinar, Data Mining with R, features Richard Skeggs of BLG Data Research and his discussion of the process of data mining using R.
Three key topics we like from Data Mining with R:
- Preprocessing, anomaly detection, association rule learning, clustering, classification, regression, and summarization with R
- Using data mining to determine consistent patterns or systematic relationships between variables
- R is a useful tool for data mining, even for those who may not have much experience with data mining
Tatvic is all about web analytics, and they seek to help companies utilize their data successfully. Their data mining webinar, Google Analytics Data Mining with R (Includes 3 Real Applications), is intended for data analysts, web analysts, and digital marketing managers who are data mining beginners. The webinar features speakers Andy Granowitz, developer advocate at Google Analytics, and Kushan Shah, web analyst at Tatvic.
Three key topics we like from Google Analytics Data Mining with R (Includes 3 Real Applications):
- Using R with Google Analytics
- Predicting product revenue with R
- Calculating long-term value of marketing campaigns using R
Cost: FREE with email registration
A Hitachi Data Systems company, Pentaho is a data integration and business analytics company that offers an enterprise, open source platform for Big Data deployments. Their data mining Wiki, Data Mining Algorithms and Tools in Weka, contains links to overview information regarding the various types of learning scheme and tools included in Weka for data mining.
Three key resources we like from Data Mining Algorithms and Tools in Weka: