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Sunday, November 6, 2016

7 Ways Teachers Use Social Media in the Classroom

7 Ways Teachers Use Social Media in the Classroom



Millennials live and breathe on social media, so teachers are learning how to incorporate the medium into the classroom successfully.

In doing so, teachers not only encourage students to engage actively in the material, but they also provide online communities for students that might not exist for them in real life.

SEE ALSO: The Teacher's Guide to Social Media

But how are teachers infusing social media into their everyday lessons? We've highlighted several different examples and offered our own ideas on how to best engage students.

1. Encourage students to share work socially.

Anna Divinsky created an iTunes U class at Penn State University called Art 10: Introduction to Visual Studies, which she then adapted into a massive open online course (MOOC) on Coursera. The MOOC, called Introduction to Art: Concepts and Techniques, amassed more than 58,000 students.

For each class assignment, students were responsible for evaluating each other's work. Because the class was online, social media played an essential role in connecting students and creating an online community.

Students shared their work on a variety of platforms. On Flickr, they tagged their artwork with "artmooc." On Twitter, they included the #artmooc hashtag. Others posted to Facebook, and continue to do so to this day, even though the course has been over for quite sometime.

24 Jan 13

Penn State TLT @psutlt

Share your artwork with us that you created while exploring Introduction to Visual Studies on #iTunesU. http://itun.es/i6DQ5wz #art10psu


Wendy S Dixson @WendyDixson

@psutlt #art10psu Art in the style of Rousseau (done in pencil)pic.twitter.com/oOA9UrlX6E

7:24 PM - 16 Jul 2013

View image on Twitter

"It was fascinating to see learners from all over the world wanting to connect with one another in order to build a sense of community," Divinsky says.

But what was even more surprising was how social media allows students to self-organize into smaller, independent groups. These groups were based on commonalities like age, language and art proficiency levels. By allowing students to share on the site of their choosing, social sharing will come more naturally.

2. Use a hashtag to facilitate guest speaker discussions.

According to a recent YPulse survey, 21% of Millennials use Twitter as their primary source for finding news. Encouraging students to engage with guest speakers via Twitter makes them more engaged with the platform and prepares them to raise important questions online.

During an investigative journalism class at New York University, one professor invited prominent journalists to come speak to the class of more than 200 people, and encouraged students to live-tweet the interview using the hashtag #IJNYU. Because the class was so big and the tweets so frequent, the hashtag occasionally became a trending topic in New York City. Students were then required to turn in a Storify summary based on their classmates' tweets, within 24 hours.

Another way to incorporate hashtags during classroom discussions is to encourage students to tweet questions to a guest speaker as the speaker is talking. This is exactly what Mara Einstein and Chad Boettcher did for NYU's Innovations in Marketing class. This method ensures that students don't interrupt the speaker while he or she is talking. More importantly, however, is that it also engages the students' social communities outside of the classroom, so people who aren't taking the class can also chime-in with questions for the guest speaker.

3. Require students to keep a blog.

While teaching The Business of Media, another class at NYU, Ted Magner required students to keep a "trends" blog on the media sector of their choosing. Not only did this activity keep the students reading relevant articles every day, but it also required them to become familiar with hyperlinks, image embeds and how to cite sources digitally. Perhaps most importantly, it gave them material to include in portfolios after graduation.

Keeping a blog is a phenomenal way to work on your voice as a writer, and to truly explore and hone in on your personal interests. However, between essays and homework assignments, many college and high school students see blogging as more of a chore than a positive career move. By requiring students to keep a blog in place of some traditional assignments, you make your job as a teacher easier, and you help them establish their digital presence as an emerging thought leader.

SEE ALSO: 10 Creative Social Media Resumes To Learn From

4. Require original expert sources.

For journalists, LinkedIn has proven to be an invaluable tool to reach out to sources, from CEOs to corporate PR representatives. Teachers can foster this skill by encouraging students to reach out to sources directly through LinkedIn.

It should be noted, however, that free accounts on LinkedIn are mostly intended to be used for professional networking. Features that come with a LinkedIn Premium subscription may make the source-gathering process easier.

5. Use Google Hangouts.

If you're teaching remotely, or if you're teaching an online class, Google Hangouts can be a great way to check in with students face-to-face.

This is also a good option for adjunct professors who wish to conduct office hours but may not be on campus often enough to meet with all of their students.

6. Create a social classroom on Edmodo.

Edmodo helps you create a social, digital classroom. On Edmodo, you can vote, post assignments, create a class assignments calendar, and upload photos and messages to students.

With more 17 million users, Edmodo has been a highly successful endeavor. It allows students to get real-time feedback by taking quizzes online. Teachers can also engage socially with one another by sharing lesson plans online and asking questions to their online communities.

Edmodo's Global Read Aloud program encourages students to practice their reading and public speaking skills with other students from around the world.

7. Hold a class in Second Life.

For the class Philosophy of Cyberspace at Northwestern University, students created accounts on Second Life to explore themes such as online identity, online community building and in-game economics.

Some days the students would meet in the virtual world instead of meeting at a real-life lecture hall. The professor would send out an email saying, "Class on Tuesday will be held in Second Life instead of the lecture hall. I'll email you all the coordinates soon."

Editor's note: The writer of this article took both Innovations of Marketing and The Business of Media while a student at NYU.

Image: Flickr, Jason Howie

Incorporating Social Media Into Your Exercises #SMEM

Incorporating Social Media Into Your Exercises #SMEM

Posted on June 4, 2015 | 8 comments

Post by: Kim Stephens


Social media is now a common tool emergency management and response organizations turn to in order to interact with the public before, during and after a disaster event. However, testing the use of social media during exercises has proven to be problematic. Agencies are reluctant to use their own social accounts in a live environment because they do not want to confuse or scare their followers with exercise updates; fake social platforms often fall-flat in terms of realism.  In today’s post, I want to suggest a third alternative.

Using Real but Unidentifiable Accounts

You can provide an exercise environment that allows participants to engage with content online, with social platforms agencies use everyday. This can be done by utilizing real but unidentifiable social media accounts in a closed or semi-closed environment (e.g. Twitter protected accounts and Facebook groups marked as private, closed or secret, and a blog site set up with disabled search engine indexing). In a recent exercise, my team and I employed a total of 7 different types of online platforms in order to accomplish our goal of providing as realistic as possible news and social environment. However, for the purposes of this post, I would like to focus on the processes we used for Twitter, Facebook and WordPress.

Protected Twitter Accounts


Our team used protected Twitter accounts to provide participants the ability to operate in a social platform they were used to and it gave us the ability to limit access.  “Accounts with protected Tweets require manual approval of each and every person who may view that account’s Tweets”  (support.Twitter.com). Twitter states the following:

When you protect your Tweets, the following restrictions are put in place:
  • People will have to request to follow you; each follow request will need approval. Learn more.
  • Your Tweets will only be visible to users you’ve approved.
  • Others will not be able to retweet or quote your Tweets.
  • Protected Tweets will not appear in Google search; protected Tweets will only be searchable on Twitter by the account holder and approved followers.
  • @Replies you send to people who aren’t following you will not be seen by those users (because you have not given them permission to see your Tweets).
  • You cannot share permanent links to your Tweets with anyone other than your approved followers.

For the exercise, we took the following steps:

  • Created numerous fake protected accounts for the simulation cell and for the participants (we also had to create new Google accounts to provide an email address);
  • We ensured all pre-created protected accounts followed each other;
  • Other approved exercise participants were allowed to follow the accounts at the start of the exercise;
  • Content was developed in advance and pre-scheduled based on the exercise timeline, however, the simulation cell followed the stream and interacted with the participants in order to push them to achieve the desired outcome.

There are some pros and cons to using protected Twitter accounts versus a simulated Twitter environment, or unprotected accounts:


  • Real Twitter user-interface
  • Users saw their own stream as well as exercise content, which added realism
  • People did not have to be re-trained on how to use the platform
  • Content never was accidentally released into the agency’s real stream.


  • Cannot ReTweet a protected Tweet
  • If exercise participants (other than those designated to interact with the stream) wanted to see the content they had to have their own Twitter account. They were also reminded to “view only”.
Closed Facebook Groups

Facebook was actually quite easy to use in a closed environment because closed groups are fairly common. To take full advantage of this feature, we did the following:

  • Created a closed group and invited designated participants via email;
  • Wrote content in advance and populated the page based on the scenario timeline. As with Twitter,  the sim-cell interacted with the participants “live” in order to provide realism to the exercise environment.


  • Real Facebook user-interface
  • People did not have to be re-trained on how to use the platform
  • Content could not accidentally be released into the agency’s real stream.
  • Content posted in a closed group does NOT show up on the user’s personal/public timeline.


  • If an exercise participant wanted to view the content they had to have their own personal Facebook account.
  • Fake Facebook accounts are not as easy to establish, so the sim-cell also used their own personal accounts.  By doing this, however, the timeline of the FB page can look a little lop-sided and less realistic. For large exercises, this issue could be addressed by simply adding the entire sim-cell team for the exercise to the FB Group and asking them to post pre-scripted content.

A common complaint of exercise participants is that they get lost in the scenario. Scenario-time jumps and fast paced injects can mean that participants are aware of their own ESF actions, but often don’t have the situational awareness provided during real events by news and social media, which is often displayed in EOCs on large screens. In order to help address this issue, we utilized a blog site as a hub for all information. (I should note that during the exercise we also used a Flickr stream to broadcast to the EOC over 500 scenario-specific images, displayed multiple YouTube videos we created to portray the scenario, and provided text messages with scenario weather information and updates directly to everyone’s cell phone.)

The blog site was open to anyone who knew the URL, but we did limit the site visibility by selecting the option available in WordPress.com to discourage search engines from indexing the site. We did not have any issues regarding information getting released to the public or news media.

We did the following:

  • An exercise-specific blog served as a foundation for the exercise for everyone to view the situation manual and other relevant documents including maps, etc.
  • During the exercise, participants were encouraged to use the blog to post content they were developing including press releases, updates from their ESF (e.g. road closures), shelter locations, etc.


  • Served as the main site for “exercise” situational awareness. Allowed everyone to understand what was happening on the timeline.


  • Developing content for a blog can be much more time consuming than writing Tweets and Facebook posts since stories versus sentences have to be developed. However, it should be noted, news stories from real-world disaster events make great starting points.

This Single Mother Makes Over $700 per Week Helping Businesses With Their Facebook and Twitter Accounts....... And Now You Can Too!

This Single Mother Makes Over $700 per Week Helping Businesses With Their Facebook and Twitter Accounts....... And Now You Can Too!

Hi, I'm Annie Jones. This is my story...

Like most single parents around the world, my mornings are pretty busy with the mad dash to get the kids out of bed, washed, dressed and fed in time to leave for school.

I love this time though because I know that once the stress of getting them to the school gate is over, I get to go home and start 'work'. That might sound a little strange because not a lot of people love their work, and I used to be the same, but my life has changed so drastically in the last 12 months that I now LOVE getting back home to start work.

Nowadays work for me involves logging on to Facebook, Twitter and YouTube, reading and replying to some comments and scheduling some posts for the day. The businesses that I do this for don't have the time to do this work themselves and it's not enough work to hire someone full time, so they pay me to do the work for them part time from home.

The best part is that ANYONE who knows how to use Facebook, Twitter and YouTube can do this 'work', and there are millions of businesses around the world hiring for these positions RIGHT NOW!

I sometimes find it hard to believe how great my life is now because it wasn't always this good...

A few years ago I was heavily in debt...

I got laid off my from my job as a small motel manager. At first I thought I’d be ok because I had a bit of money saved up and was sure that I’d find another job.

However I burned through my savings much faster than I thought and got absolutely nowhere on the job finding front. Nothing, zilch, nada. Nobody was hiring, even if I was willing to take a considerable step down in job position and salary.

Unfortunately someone forgot to notify the mailman that I didn’t have any money or income, so he kept dropping off new bills and final demands every day.

This was all made 10x worse by the fact that I am a single mom and have a young family to support

I wanted more than anything else in the world to be able to provide for them. I started biting my fingernails (which I had NEVER done before) and I’m pretty sure that I only got about 3 hours sleep in a whole month period because I was so stressed.

And then one day I was over at a friends house for a children’s play date when I got caught up talking to someone in a conversation that would literally change the course of my life.

Everything changed when another mother that I'd just met showed me a cheque she had received just for posting on Facebook

I’d never met this other mother before, she was a work colleague of one of my other friends, and she made the big mistake of asking me how I was going...

Now, I’m not sure if it was the 4 cups of coffee that I had consumed, or the build up of the stress, the kids getting into a fight, or a combination of everything, but that poor woman got a lot more than she bargained for in my response.....

I hadn’t talked to anyone about my financial troubles since they began, my close family didn’t even know that I’d lost my job. So when this woman asked how I was doing, it all just came pouring out.

All of the frustrations, stress, worry, sleepless nights and everything else, all in one big long rambling rant. She just stood there, nodding her head and listening sympathetically. Looking back now, she must have the patience of a saint because she didn’t seem phased at all!

When I had finished she looked me directly in the eyes and said

"I totally understand what you are going through. I went through almost exactly the same thing myself just six months ago"

It turns out that she had also been let go from her job, struggled to find a new one and had hit rock bottom financially with bills piling up.

She then went on to tell me that now, 6 months later, she was more financially secure than she had ever been, and that it was all thanks to the Facebook. She explained that most businesses have Facebook, Twitter and other social media accounts that they just don't have time to manage themselves, so they pay people like her to do it for them part time from home.

I Couldn't Believe What I Was Hearing!

How had I never heard of this before?!? She told me that there are millions of businesses hiring for these positions right now, I started to get really excited and asked her a barrage of questions. She taught me everything that she knew and connected me with some employers the very same day.

I had my first paid job within 24 hours

Now I was the one earning hundreds of dollars each week just for playing around on Facebook and Twitter!

Get started NOW! Get started TODAY!

I've been doing this social media work online for over 12 months now and I have learnt all the tricks and tips to make the most money right from Day 1, even if you have never done anything like it before. I will take you by the hand and show you everything you need to know to get started making money as soon as TONIGHT.

I'll show you how to find hundreds of easy social media jobs that you can begin right away!

What IS The Role Of Social Media In Education?

What IS The Role Of Social Media In Education?


What IS The Role Of Social Media In Education?



The contributing bloggers at edSocialMedia are an eclectic and knowledgeable bunch with varied backgrounds and experiences. The blog topics are never stale and include many different perspectives on social media with recent topics including a school’s internal audiences, community managers, andmaking time to blog.

I’ve recently completed a number of interviews for edSocialMedia and like asking what my interviewees think is the role of social media in education and I’ve gotten great answers from Chris Brogan, Brian Halligan, and Liz Strass. Their answers inspired me to write about my thoughts regarding the question but I quickly changed course and thought I would ask the contributing bloggers that question. Here are their feelings about the role of social media in education.

Jay Goulart (@jaygoulart)

This is the type of question that can take educators off task. I believe the question might be something like what should the role of education be in 2010, what is the world we should prepare our students for? Pulling social media out as a single topic creates the wrong conversation. Or perhaps you might ask What is the single most important question you might ask to design an educational experience that would help students excel in 2030?

Hans Mundahl (@hmundahl)

Social media allows students to do real work, not simulated work. Why not produce real TV, conduct realresearch, write for a real paper, or discuss real ideas with others? That’s what social media lets us do.

Chuck Will (@chuckbwill)

I see two roles for social media in education. The ultimate role–the interconnected education of our students–is immense, daunting, open to undreamed possibilities and demanding of both our creativity and skills. Easier and more immediate is the role of providing a superior platform for professional networking: educators and administrators collaborating, sharing, debating….doing just what we’re doing right here right now. In each case, however, we see both an elevation of quality and a leveling of resources. More resources are suddenly becoming accessible to more people….all people.

Jason Ramsden (@raventech)

When I think of Social Media in education I think of one word – tri-fecta. For individuals it’s about choosing and managing three social media tools (twitter, linkedin, facebook, nings, etc.) to help broaden one’s horizons and connect with peers. For institutions, however, the impact social media is having and the potential it will have on changing the a) marketing of our schools, b) the professional development of educators, and c) the teaching and learning happening in our schools is profound. The question then, should not be what is the role of social media in education, but rather, what is the role of education in a world connected by social media?

Page Lennig (@plennig)

As learning becomes more and more social, social media sites (or sites that allow for connections) will become the classrooms of the future. Sure there will still be “bricks & mortar” classrooms but they will be focused on face to face interaction, the arts, sports, etc. Much of the learning the students will do will be on these sites connecting with other students, experts, teachers (near & far). Social media will help break down the walls of our current system and expand the opportunity for learning.

Cassie Dull (@cassdull)

Social media brings the world to the classroom and enables students to communicate across the world. Social media breaks down time, distance and accessibility barriers and brings many opportunities for learning to happen anywhere, anytime. It fosters two-way communication and collaboration, which is really the essence of any learning experience. With social media, any one person now has a valuable voice to add to the conversation.

Travis Warren (@traviswarren)

Social Media is an affordable-personal-printing-press with built in global distribution capabilities. The quicker students grab on to this, the more effective and empowered their lives will be.

Alex Ragone (@alexragone)

Students need to develop digital footprints that are not just their social lives (ie. Facebook).

Curt Lewellyn (@clewellyn)

I think that the role of social media in education as it seems to playing out at this point seems to be two fold. On the curricular side of things, I see the role of social media and collaborative tools that allow students and teachers to connect and share ideas as being an extension of class discussions by extending the possibility of sharing beyond the classroom walls. On the institutional/marketing/communications side of things, I see social media as an effective and simple way for Schools, educators and administrators to communicate what is happening in our schools in a way that allows for greater transparency and participation from all interested parties (i.e. teachers, students, parents, alumni.)

Lorrie Jackson (@lorriej)

Social media in education is simply a new tool for timeless goals: to connect, share, and create with our school community. Whether in the classroom or for marketing a school or for connecting with fellow educators, social media forges new ties and exchanges content in meaningful ways that would have not been possible a few years ago.

Keep the conversation going. What do YOU feel is the role of social media in education? Please let us know by posting your opinion in the comments section below.

Photo Courtesy Mox & Dom

Saturday, November 5, 2016



data mining defined

Last update: June 17, 2016Molly GalettoBig Data, Blog1 comment

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.

Jump to:

Articles and Journals

1. Data Mining Techniques in CRM to Improve Data Quality Management

Data Mining Techniques in CRM to Improve Data Quality Management

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

Cost: FREE

2. How New York’s Fire Department Uses Data Mining

how ny uses data mining

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

Cost: FREE

3. 10 Ways Data Mining Can Help You Get a Competitive Edge

10 Ways Data Mining Can Help You Get a Competitive Edge

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

Cost: FREE

4. How Data Mining Can Boost Your Revenue by 300%

How Data Mining Can Boost Your Revenue By 300

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

Cost: FREE

5. Customers to Retailers: Don’t Stalk My Twitter!

Customers to Retailers Dont Stalk My Twitter

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

Cost: FREE

6. The Power of Babble: Using Text Analytics and Data Mining to Uncover Actionable Customer Insights

The Power of Babble Using Text Analytics and Data Mining to Uncover Actionable Customer Insights

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

Cost: FREE

Courses and Lecture Notes

7. Data Mining

Data MiningVK

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

Cost: FREE

8. Data Mining

Data MiningMIT

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:

Cost: FREE

9. Data Mining Course

Data Mining Course

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

Cost: FREE

10. Free Online Data Mining Courses

Free Online Data Mining Courses

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:

Cost: FREE

11. STATS202 – Data Mining and Analysis

STATS202 - Data Mining and Analysis

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

12. Mining Massive Datasets

Mining Massive Data Sets

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

13. Data Mining

Data Mining

“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

Cost: $149

14. Online Graduate Data Mining Certificate Program

Online Graduate Data Mining Certificate Program

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

15. Data Mining with Weka

Data Mining with Weka

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

Cost: FREE

16. Data Mining in R – Learning with Case Studies

Data Mining in R - Learning with Case Studies

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

Cost: $549

17. Data Mining Specialization

Data Mining Specialization Track

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

Cost: $294


18. Data Mining: Concepts and Techniques

Data Mining Concepts and Techniques

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

Cost: FREE

19. Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition

Statistical and Machine-Learning Data Mining

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


  • VitalSource 6 Month Rental: $79.01
  • VitalSource 12 Month Rental: $51.12
  • VitalSource purchase: $65.07

20. Data Mining Algorithms in R

Data Mining Algorithms In R

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

Cost: FREE

21. A Programmer’s Guide to Data Mining

A Programmer's Guide to Data 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
  • Classification

Cost: FREE

22. Data Mining Tools and Techniques

Data Mining Tools and Techniques

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

23. Data Mining: Concepts and Techniques (Third Edition)

Data Mining Third Edition

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

Cost: $31.50/chapter

24. Robust Data Mining

Robust Data Mining

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

Cost: $29.99

25. Data Mining: The Textbook

Data Mining The Textbook

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

Cost: $69.99

26. Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, 3rd Edition

Data Mining Techniques For Marketing Sales and Customer Relationship Management 3rd Edition

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

Cost: $32.99

27. Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery

Data Mining with Rattle and R The Art of Excavating Data for Knowledge Discovery

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

Cost: $49.99

28. Chapter 2: Data Mining Methods for Recommender Systems

Chapter 2 Data Mining Methods for Recommender Systems

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

Cost: FREE


29. Data Mining Techniques

Data Mining Techniques

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

Cost: FREE

30. An Overview of Data Mining Techniques

An Overview of Data Mining Techniques

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

Cost: FREE

31. Data Mining Techniques

Data Mining Techniques Zentut

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

Cost: FREE

32. Data Mining 101: Tools and Techniques

Data Mining 101 Tools and Techniques

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

Cost: FREE

33. Five Data Mining Techniques That Help Create Business Value

Five Data Mining Techniques That Help Create Business Value

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

Cost: FREE


34. Data Mining Tutorial

Data Mining Tutorial

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

Cost: FREE

35. DMS – Data Mining Tutorial

DMS Data Mining Tutorial

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

Cost: FREE

36. An Introduction to Data Mining by Kurt Thearling, Ph.D.

An Introduction to Data Mining by Kurt Theraling

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

Cost: FREE

37. Statistical Data Mining Tutorials

Statistical Data Mining Tutorials

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:

Cost: FREE

Videos, Webinars, and Wikis

38. Data Mining with STATISTICA – Session 1

Data Mining with STATISTICA Session 1

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

Cost: FREE

39. Video Lectures on Data Mining

Video Lectures on Data Mining

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:

Cost: FREE

40. Data Mining: Failure to Launch – How to Get Predictive Modeling Off the Ground and Into Orbit

Data Mining Failure to Launch How to Get Predictive Modeling Off the Ground and Into Orbit

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

Cost: FREE

41. Introduction to R for Data Mining

Introduction to R for Data Mining

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

Cost: FREE

42. Data Mining Webinar with Peter Bruce, President, Statistics.com

Data Mining Webinar with Peter Bruce President Statisticscom

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

43. Fall Webinar Series: Analytics & Data Mining

Fall Webinar Series Analytics Data Mining

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

Cost: FREE

44. Three Webinars Give Tips on Data Mining

Three Webinars Give Tips on Data Mining

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

Cost: FREE

45. Text and Data Mining in the Humanities and Social Sciences – Strategies and Tools

Text and Data Mining in the Humanities and Social Sciences Strategies and Tools

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

Cost: FREE

46. Data Mining: The Tool of the Information Age Revolution

Data Mining The Tool of the Information Age Revolution

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

Cost: FREE

47. Mining Online Data Across Social Networks

Mining Online Data Across Social Networks

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

Cost: FREE

48. Webinar: Data Mining with R

Webinar Data Mining with R

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

Cost: FREE

49. Google Analytics Data Mining with R (Includes 3 Real Applications)

Google Analytics Data Mining with R Includes 3 Real Applications

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

50. Data Mining Algorithms and Tools in Weka

Data Mining Algorithms and Tools in Weka

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: