Services: Data Mining Project Assessment, Data Preparation For Data Mining, Data Mining Model Development, Data Mining Model Deployment, Data Mining Course: Overview for Project Managers, Data Mining Course: Overview for Practitioners, Customized Data Mining Engagements
Insight 1: Find Correlated Variables Prior to Modeling Topic: Data Understanding and Data Preparation Sub-Topic: Feature Selection Insight 2: Beware of Outliers in Computing Correlations Topic: Data Preparation Sub-Topic: Outliers Insight 3: Create Three Sampled Data Sets, not Two Topic: Modeling Sub-Topic: Sampling Insight 4: Use Priors to Balance Class Counts Topic: Modeling Sub-Topic: Decision Trees Insight 5: Beware of Automatic Handling of Categorical Variables Topic: Data Understanding and Data Preparation Sub-Topic: Feature Selection and Creation Insight 6: Gain Insights by Building Models from Several Algorithms Topic: Modeling Sub-Topic: Algorithm Selection Insight 7: Beware of Being Fooled with Model Performance Topic: Data Evaluation Sub-Topic: Model Performance
Client List and Case Studies
Upcoming Data Mining Seminars A Practical Introduction to Data Mining Upcoming courses (nationwide) Data Mining Level II: A drill-down of the data mining process, techniques, and applications Data Mining Level III: A hands-on day of data mining using real data and real data mining software Anytime Courses Overview for Project Managers: Train project managers on the data mining process. Overview for Practitioners: Train practitioners (data analysts, project managers, managers) on the data mining process.
Data Mining Resources, Books, Websites, White Papers, Presentations, Tutorials
Mr. Abbott is a seasoned instructor, having taught a wide range of data mining tutorials and seminars for a decade to audiences of up to 400, including DAMA, KDD, AAAI, and IEEE conferences. He is the instructor of well-regarded data mining courses, explaining concepts in language readily understood by a wide range of audiences, including analytics novices, data analysts, statisticians, and business professionals. Mr. Abbott also has taught applied data mining courses for major software vendors, including Clementine (SPSS), Affinium Model (Unica Corporation), Model 1 (Group1 Software), and hands-on courses using S-Plus and Insightful Miner (Insightful Corporation), and CART (Salford Systems).
Services Overview
Data Mining Project Assessment
Objective: Determine the feasibility of completing a successful data mining project.
Process: By defining data mining, aligning business objectives, and determining all data sources needed to mine through a single data table.
Deliverables:
- Data Inventory Checklist
- Data Joining Blueprint
- Data Mining Feasibility Assessment
Data Preparation for Data Mining
Objective: Prepare data for building data mining models.
Process: By identifying and correcting data problems, identifying and creating new features, and extracting samples from the database(s).
Deliverables:
- Data audit report
- Procedures for correcting data problems
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Variable selection and feature creation report
- List of fields to remove from consideration for modeling
- List of proposed new features and procedure for calculating new features
Data Mining Model Development
Objective: Develop a data mining model solution to business objectives.
Process: By identifying data mining methods to use and creating actionable models for execution.
Deliverables:
- Data Mining Models
- Model Performance Reports
- Model Interpretation Reports
- Model Deployment Plan
Data Mining Model Deployment
Objective: Deploy data mining models by creating software infrastructure that uses data mining models to score new data.
Process: By developing software to complete the deployment plan, and test the software to ensure accurate deployment of the models.
Deliverables:
- Data Mining Model Deployment Plan
- Software for data preparation, data mining models, model scoring, and interfaces to existing software environment
- Software Test Suite
- Model Deployment Report
Data Mining Course: Overview for Project Managers
Objective: Train project managers on the data mining process.
Process: By describing key steps in the data mining process in clear, practical, and non-technical language to assist decision-makers in how to use data mining to improve business efficiency and understanding.
Deliverables:
- Data Mining Training Course, 1-2 days
- Data Mining Training Workbook
Data Mining Course: Overview for Practitioners
Objective: Train practitioners (data analysts, project managers, managers) on the data mining process.
Process: By describing key steps in the data mining process and tips for how to successfully accomplish the steps.
Deliverables:
- Data Mining Training Course, 1-3 days
- Data Mining Training Workbook
Customized Data Mining Engagements
Objective: Supply data mining expertise customized for needs of organization.
Process: As needed via the phone or occasional onsite support between normal business hours (8am-5pm PST), Monday through Friday.
Deliverables:
- Weekly or Monthly Status Reports (depends on consulting structure)
Invoice Fraud Detection: Successful application of data mining by Abbott Analytics
TDWI Data Science Bootcamp Seminar (Austin, TX / Virtual Classroom): September 20 - 22, 2021
PAW for Business (Virtual Classroom): May 20 - 25, 2021
Abbott, D.W., Combining Models to Improve Classification Accuracy and Robustness (PDF), The 2nd International Conference On Information Fusion - FUSION'99, San Jose, CA, July 6, 1999.