# Most Viewed Data Mining Videos on YouTube

Tags: Ayasdi, Data Mining, Google, Grant Marshall, R, Rattle, Revolution Analytics, Statistica, Text Mining, Weka, Youtube

The top Data Mining YouTube videos by those like Google and Revolution Analytics covers topics ranging from statistics in data mining to using R for data mining to data mining in sports.

**By Grant Marshall.**

As with Big Data, Data Mining is a topic with many great videos on YouTube. However, it can be hard to filter through the mass of videos to find this solid content. Previously, we looked at the top Big Data YouTube videos by views. This week, we see what YouTube has to offer by way of Data Mining videos. If you want to view all of the videos in one place, a playlist can be found here. View counts as of May 17, 2015.

**1. Statistical Aspects of Data Mining (Stats 202) Day 1**(155,651 views)

This video covers the first day of a multi-day course produced by Google Tech Talks. There are actually twelve videos in this series that aren't afraid to get into the nitty-gritty of statistics in data mining. If you're looking for very detailed workshop-like videos, this is great for you.

**2. Weka Data Mining Tutorial for First Time & Beginner Users**(152,825 views)

This is a brief (23 minute) introduction to using Weka for data mining. It's good if you're interested in a brief introduction into how to apply Weka to data mining from a beginner's perspective, this is a good resource.

**3. Data Mining with STATISTICA - Session 1**(117,173 views)

This video is the first in a series of data mining tool videos by StatSoft. This series goes over numerous interesting tools, starting with this first session on STATISTICA. Take a look at this series to see if any of the tools interest you.

**4. Introduction to R for Data Mining**(103,078 views)

This introduction to R for data mining is produced by Revolution Analytics. It's a great, mostly-recent, introduction to the subject by a very knowledgeable group of people in the subject. Considering the popularity of R in the field, this is a very useful video for those looking to begin experimenting with hands-on applications of data mining.

**5. Lecture - 34 Data Mining and Knowledge Discovery**(89,698 views)

This video is part of a lecture series in a database systems class. It focuses on data mining and knowledge discovery and acts as a good introduction to the topic in an academic setting. If you prefer lecture-style videos, this is a good introduction to the subject.

**6. Introduction to Data Mining (1/3)**(85,758 views)

This video is the first in a three-part video series on data mining. It takes an application-driven approach and uses commonly-available tools in a business environment. I think this is a good video to show business users to show them how to apply data mining techniques to business cases.

**7. MATHS & STATISTICS | Data mining tutorial from John Elder (1)**(31,236 views)

This brief video details ten top things to avoid in data mining. It's a fun short video that offers some solid tips. Anyone working in the field could benefit from being reminded of these tips from time to time.

**8. Text Mining for Beginners**(23,993 views)

This brief video is somewhat more focused than the previous videos. It details how data mining can be applied to text for beginners, and more specifically, goes into how Linguamatics attacks the problem. This is a good video for those interested in text mining, NLP, or Linguamatics.

**9. Rattle for Data Mining - Using R without programming (CRAN)**(17,608 views)

This is another R-focused data mining video. This one instead focuses on how to use Rattle for data mining. This is a great video because of how exciting of a prospect Rattle is for making data mining easier to perform. If you haven't had a chance to dive into Rattle yet, this is worth the watch.

**10. "The New Positions of Basketball" - Muthu Alagappan @ Ayasdi**(12,666 views)

This is a much more applications-focused video than any of the others. This video goes into how data mining was applied to the analysis of basketball positions by Muthu Alagappan. If you're interested at all in applications of data mining to sports, this is an interesting watch.

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