PREDICTIVE ANALYTICS & DATA MINING
A Work-Along Course for Data Preparation,
Modeling Methods, Tools And Techniques
DEVELOP MODELS FOR ACTION AND IMPACT
The “Model Development” course dives into the data mining process at the tactical level. Attendees will observe live demonstrations of machine learning methods and computer-aided pattern discovery techniques for extracting and interpreting complex patterns and relationships from large volumes of data. Participants then participate in work-along labs that build upon an overall project.
This course is designed to be taken independently, yet is part of a larger course series that covers a 6-Phase Model Development Methodology for low-risk, high-impact projects. The scope of this course extends to the second and third phases: Prepare and Build.
You need not be an experienced statistician or mathematician to track well in this course – though quantitative experts will greatly benefit from the strategic referencing to the Plan phase and the pragmatic mind shift required in this event. The machine learning algorithms are covered from a functional perspective. Modern software does a great job of handling the mathematical complexity. Those seeking a deep drill-down into the mathematical or theoretical underpinnings of predictive analytics algorithms should refer to other available academic offerings.
This vendor-neutral course utilizes popular commercial and open-source analytic tools in its demonstrations. The tools are used to illustrate the methods conveyed, but not to showcase the products. If you desire an intensive tactical orientation to predictive modeling methods, techniques and practice, then this event is designed for you.
WHO SHOULD ATTEND
- Data Scientists: who desire to extend their analytical toolbox and underscore the scientist aspect of the role with formal process and hands-on methodological practice
- Big Data Analysts: who are under increasing pressure to transform their deluge of data from a liability to an asset
- IT Professionals: who wish to gain a better understanding of the data preparation, analytics and analytic sandbox development requirements to more fully support the growing demand for analytic IT support
- Project Leaders: who desire to have a more detailed understanding of predictive modeling methods and techniques to better manage and interact with their practitioners
- Business Analysts: who must develop and interpret the models, communicate the results and make actionable recommendations
- Functional Analytic Practitioners: Customer Relationship Managers, Risk Analysts, Business Forecasters, Statistical Analysts, Social Media and Web Data Analysts, Fraud Detection Analysts, Audit Selection Managers, Direct Marketing Analysts, Medical Diagnostic Analysts, Market Timers
- Anyone Overwhelmed with Data and Starved for Actionable Insights
KEY SKILLS YOU’LL TAKE AWAY IMMEDIATELY
- Apply a formal process for data preparation, model development and validation of results
- Recognize and avoid common but costly pitfalls in data prep, method selection and results interpretation
- Ensure that your model is adequately generalized and has not memorized the training set
- Take an incremental low-risk / high-impact approach to model development along with vendor-neutral tool exposure that will save months in product surveying
- Consider trade-offs between model accuracy and explainability when selecting modeling methods
- Guide your IT staff to build an analytics sandbox for rapid model development and minimal IT dependency
- Proceed confidently upon return with the formal process, session files, and direct hands-on experience gained through follow-along labs in the Prepare and Build phases of an overarching 6-Phase Model Development Methodology
THE ORGANIZATIONAL CHALLENGE FOR ANALYTICS
The rapid emergence of data processing and collection methods has propelled the IT industry into the age of big data. Organizations now contain so much data that it has become very difficult to understand just what all that data is telling us.
There is hardly a transaction that does not generate a computer record somewhere. All this data has meaning with respect to making better prospective organizational decisions and anticipating customer needs and preferences. But how do you discover those needs and preferences within data stores that contain endless amounts of seemingly incomprehensible numbers and facts? Predictive analytics does just that.
Only predictive analytics can transform the rapidly growing mass of inert data into actionable information assets. These assets can be leveraged for measurable gain through improved and automated decision-making. The practitioners who develop these transformative skills will be the leaders in their field.
The intent of this course is to present participants with a roadmap for data certification and preparation, model-building techniques, how various methods and tools apply to different kinds of data intensive problems, and how to overcome limitations that cause the majority of predictive models to under-perform.
UPON COMPLETION, YOU’LL BE ABLE TO
- Understand the purpose, function and impact of the 6-Phase Model Development Methodology
- Proceed through the general implementation of the two tactical phases: Prepare and Build
- Realize that model-building for actionable production need not be highly technical or complex
- Construct a valid data set and transform data for superior model performance
- Describe each of the five steps for preparing raw data for predictive analysis
- Select appropriate methods for each of the Four Core Analytic Project Types
- Assess the degree to which a model meets a predefined performance objective
- Leave with resources, contacts and actionable plans to substantially increase your analytic capabilities while minimizing dead ends
WHAT MAKES THIS COURSE UNIQUE
This course does not restrict or skew the presentation of data mining methods through a single product. Rather, the Model Development course gives broad consideration of the capabilities and limitations of all resources from a vendor-neutral perspective.
Live modeling demonstrations projected from the presenter’s machine will precede the follow-along lab exercises. The demonstrations will reveal what works… as well as what doesn’t. The instructor will show how to evaluate various features and available products based upon strengths, limitations, value and general performance.
The highly seasoned faculty possesses a wealth of pragmatic experience in applying predictive analytics across industries in current real-world applications. This course, like no other, insists upon making predictive analytics purposeful, measurable and actionable in a business or organizational setting.
While this course is designed to be taken independently, it is important to understand its place and function within the overall Predictive Analytics & Data Mining Course Series.
Registrants will be required to view a four-hour asynchronous “Core Concepts” orientation prior to attending this event. Access details for the Core Concepts orientation will be shared with participants prior to the start of the course. Prior education or experience in data analytics or statistics is helpful, but not required.
Participants need only supply a laptop computer with Microsoft Excel. Instructions on how to download lab data and any analytic tools will be provided in the preparatory email. The instructor can assist participants with any preparation during breaks, and before or after class.
What You Will Get in this Presentation
- Prerequisite four-hour preparatory orientation
- View the full Core Concepts Topic Outline
- Process Objectives/Goals
- Sourcing and Combining Data
- Attribute Preparation
- Attribute Types (Scales)
- Missing Value Identification & Treatment
- Outlier Identification & Treatment
- Data Construction
- Data Representations
- Data Transformations
- Structuring Data for Modeling
- When and Why We Sample?
- Determining Sample Sizes
- Core Sampling Methods
- Train, Test, Validate
- Cross Validation
- Balanced Sampling
- Condition Attribute Identification
- LAB: Data Sandbox Construction
- LAB: Clustering for Data Preparation
- Process Objectives/Goals
- Experimental Design: TRAIN Revisited
- Selecting Condition Attributes
- Analytic Model Assessment (General/Types)
- Ensemble Modeling Conceptualization
- LAB: Classification Models
- Stage 1P – Positive Impact Models
- Stage 1N – Negative Impact Models
- Stage 2 – Conflict Resolution
- Stage 3 – Ranking Across the Continuum
- Stage 4R – Resolution Enhancement
- Stage 4P – Precision Enhancement
- Algorithm Selection for Classification Models
- Logistic Regression
- Decision Trees
- K-Nearest Neighbor
- Neural Networks
- LAB: Forecasting Models
- Stage 3 – Ranking Across the Continuum
- Stage 4P – Precision Enhancement
- Algorithm Selection for Forecasting Models
- Linear Regression
- Bayesian Regression
- Neural Networks
- LAB: Clustering Models
- Overview and Demonstration of Selected Software Tools
Wrap-up and Next Steps
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Why Train With TMA?
“The instructor is knowledgeable, well organized, and interacts extremely well with participants. If you have only two days to learn about data mining, TMA’s Model Development course is the class you should attend.”
“This course is a great overview of available predictive analytics methods and techniques that provides an organizing framework for your model development efforts.”
Lead Workforce Analytics
“Both instructors in the series did a fantastic job of getting me up to speed in predictive analytics much faster than any book (or probably any other training class or conference) available.”
Knowledge-Based Systems Engineer
Compliance Technologies, Inc.
“The scope and pace of the class were spot on for me, exactly what I was looking for. The real world examples from industry veterans were both insightful and beneficial. The class ultimately helped to strip away the buzz words and hype surrounding the industry, and revealed the truly valuable foundation of this remarkable combination of art and science.”
Senior Business Analyst
“This is a must-attend course for those who would like to get started in the amazing world of Predictive Analytics & Data Mining. Without going into theoretical details, it covers every important step in the development of predictive models.”
Database Marketing & Predictive Analytics Expert
“This course was very enlightening. I was suprised to see the many ways predictive analytics can be applied!”
Business Operations Analyst
Toshiba America Medical Systems
“The wealth of information covered in these courses, as well as the in-depth demonstrations of multiple software packages, made the sessions valuable from a wide range of perspectives. I will certainly recommend that others attend.”
AVP, Managed Care
Analytics / Business Development
Health Smart Preferred Care
“The instructor was great and the instructor was very knowledgeable. It was just at the right level, not to general and not too technical.”
“The instructor and course material are first rate. Any organization that believes data mining should be a part of their business operations portfolio would be making a wise investment by attending this course.”
Information Computing Sciences
“The instructor was really great. Tim not only had a very clear understanding of what he was presenting, he was also a very good teacher who solicited a lot of audience participation. Most people with a highly technical background can’t always communicate effectively. Tim had no such problem.”
Current Employment Statistics
Bureau of Labor Statistics
“The Modeling Agency takes a daunting subject and brings it to a very understandable level to attack any problem you may be facing as an organization.”
Personal Lines Underwriting Manager
Brethren Mutual Insurance Company