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Predictive Analytics Essential Training: Data Mining

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Keith McCormick

1:54:53

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  • 01 - Data mining and predictive analytics.mp4
    00:53
  • 01 - Introducing the essential elements.mp4
    02:39
  • 02 - Defining data mining.mp4
    04:31
  • 03 - Introducing CRISP-DM.mp4
    01:49
  • 01 - Beginning with a solid first step Problem definition.mp4
    00:48
  • 02 - Framing the problem in terms of a micro-decision.mp4
    01:34
  • 03 - Why every model needs an effective intervention strategy.mp4
    01:52
  • 04 - Evaluate a project's potential with business metrics and ROI.mp4
    02:48
  • 05 - Translating business problems into data mining problems.mp4
    03:09
  • 01 - Understanding data requirements.mp4
    01:09
  • 02 - Gathering historical data.mp4
    01:45
  • 03 - Meeting the flat file requirement.mp4
    01:42
  • 04 - Determining your target variable.mp4
    01:40
  • 05 - Selecting relevant data.mp4
    03:14
  • 06 - Hints on effective data integration.mp4
    02:49
  • 07 - Understanding feature engineering.mp4
    02:45
  • 08 - Developing your craft.mp4
    01:20
  • 01 - Skill sets and resources that you'll need.mp4
    00:36
  • 02 - Compare machine learning and statistics.mp4
    02:15
  • 03 - Assessing team requirements.mp4
    03:40
  • 04 - Budgeting sufficient time.mp4
    02:19
  • 05 - Working with subject matter experts.mp4
    02:35
  • 01 - Anticipating project challenges.mp4
    00:46
  • 02 - Addressing missing data.mp4
    03:30
  • 03 - Addressing organizational resistance.mp4
    02:44
  • 04 - Addressing models that degrade.mp4
    03:15
  • 01 - Preparing for the modeling phase tasks.mp4
    01:05
  • 02 - Searching for optimal solutions.mp4
    04:06
  • 03 - Seeking surprise results.mp4
    02:32
  • 04 - Establishing proof that the model works.mp4
    03:21
  • 05 - Embracing a trial and error approach.mp4
    02:05
  • 01 - Preparing for the deployment phase.mp4
    00:48
  • 02 - Using probabilities and propensities.mp4
    03:23
  • 03 - Understanding meta modeling.mp4
    03:02
  • 04 - Understanding reproducibility.mp4
    02:34
  • 05 - Preparing for model deployment.mp4
    01:39
  • 06 - How to approach project documentation.mp4
    03:04
  • 01 - CRISP-DM and the laws of data mining.mp4
    00:49
  • 02 - Understanding CRISP-DM.mp4
    02:39
  • 03 - Advice for using CRISP-DM.mp4
    04:47
  • 04 - Understanding the nine laws of data mining.mp4
    01:52
  • 05 - Understanding the first and second laws.mp4
    02:07
  • 06 - Understanding the data preparation law.mp4
    04:04
  • 07 - Understanding the laws about patterns.mp4
    04:13
  • 08 - Understanding the insight and prediction laws.mp4
    02:12
  • 09 - Understanding the value law.mp4
    02:18
  • 10 - Understanding why models change.mp4
    02:39
  • 01 - Next steps.mp4
    01:27
  • Description


    Are you a data science practitioner, looking to develop or enhance your skills in predictive analysis and data mining? This course provides several “big picture” insights, via instructor Keith McCormick, a veteran practitioner who has completed dozens of real-world projects. Keith begins by introducing you to key definitions and processes that you will need to complete the course successfully. He steps you through defining the problem you need your predictive analysis to address, then focuses on how to make sure you meet the data requirements and how good data preparation improves your data mining projects. Keith dives into the skill sets and resources that you need and the problems you will face. Then he goes over the steps to find the solution and put it to work with probabilities, propensities, missing data, meta modeling, and much more. Keith finishes up with detailed explanations of CRISP-DM and Tom Khabaza’s nine laws of data mining, plus Tom’s new 10th law.

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    Keith McCormick
    Keith McCormick
    Instructor's Courses
    I'm an independent consultant, trainer, speaker, and author of seven books. My consulting specializes in helping analytics leaders build and manage their data science teams. My training, including 20 LinkedIn Learning courses and frequent conference workshops, has reached 1000s of individuals trying to learn statistics, machine learning, and data science. I love that I am able to train and consult. Training allows me to interact with (and learn from) 100s of clients in dozens of industries every year. It prevents me from obtaining too narrow a focus, and it keeps me current. Consulting allows me to work with a smaller number of clients in detail and in-depth, working with them on real problems of immediate concern to them. It keeps me sharp. If you've encountered me through my LinkedIn Learning courses, please consider following me here on LinkedIn. I'm not able to connect with everyone, so I connect only with clients and colleagues that I know directly. But please do follow me here because I'm quite active on LinkedIn and frequently post excerpts from the courses and other content. Follow #freefirstfridays to see when I post a link to watch a course for free. My favorite kind of consulting work involves: - working with analytics management to create effective data science teams - listening carefully to my client explain their business in detail - turning their description into a research question that can be answered with their data - coaching my client on presenting possible solutions to decision-makers - working behind the scenes to get the solution deployed Specialties: For the last several years, my emphasis has been working with analytics management to more efficiently run their teams and to nurture new hires as they expand their teams. I am skilled at explaining complex methods to new users or decision-makers and can do so at any level of technical detail. I specialize in predictive models and segmentation analysis, including classification trees, neural nets, general linear model, cluster analysis, and association rules. Books and Courses The best way to find out more about me is to check out my courses on LinkedIn Learning. They have received over 500,000 views, and each one has some free content. My books can be found on Amazon, and typically that allows you to view some free content as well. I'm very proud of all of this content (listed below in my profile), but I am still primarily an active consultant. If you need consulting help, private training, or a keynote speaker, contact me, and we can discuss.
    LinkedIn Learning is an American online learning provider. It provides video courses taught by industry experts in software, creative, and business skills. It is a subsidiary of LinkedIn. All the courses on LinkedIn fall into four categories: Business, Creative, Technology and Certifications. It was founded in 1995 by Lynda Weinman as Lynda.com before being acquired by LinkedIn in 2015. Microsoft acquired LinkedIn in December 2016.
    • language english
    • Training sessions 48
    • duration 1:54:53
    • Release Date 2023/01/14