Three 1-Day Workshops

Business Analytics
Three One Day Workshops


This program is made up of three 1-day workshops:

Day 1: TDWI Analytics Fundamentals
Day 2: TDWI Predictive Analytics Fundamentals
Day 3: TDWI Data Visualization Fundamentals

You can register to attend 1, 2 or 3 days of the workshop.

The continuum of data analysis disciplines has grown to encompass a wide range of techniques, such as data discovery, predictive models, and exploratory data visualization. Those who seek the benefits of big data and advanced analytics without solid understanding of widely used descriptive, prescriptive, and predictive analytics practices are sure to struggle. The TDWI Predictive Analytics course is designed to build that foundation and prepare you for the future advances in analytics that are sure to come. Delegates can attend one, two or three days of the course. 

Click here for an in-house quote request or for further information regarding in-house training.

Day 1
TDWI Analytics Fundamentals
25 April 2016, London

Analytics is not only a hot topic but also a complex one. This continuously growing field now includes descriptive, diagnostic, predictive, and prescriptive analytics. Applied analytics, including optimization, simulation, and automation, expand the scope. Data growth also fuels the complexity—unstructured data, big data, social data, data streams, and more. Advanced analytics continues to expand with complex event processing, machine learning, cognitive computing, etc.  In the growing and evolving world of analytics, we’re also experiencing a shift of roles and responsibilities. The “data things” that were once seen as IT responsibilities have become critical business skills. Analytics spans a continuum that encompasses IT departments, data scientists, data analysts, business analysts, business managers, and business leadership. It seems that everyone has a stake in analytics. Coordination, cross-functional analysis, data sharing, and governance have all become important skills.

Learning Objectives:

  • The concepts and practices of analytic modeling
  • An analytics topology to make sense of the variety of analytic types and techniques
  • The data side of analytics including data sourcing, data discovery, data cleansing, and data preparation
  • Analytic techniques for exploration, experimentation, and discovery
  • The human side of analytics: communication, conversation, and collaboration
  • The organizational side of analytics: self-service, central services, governance, etc.
  • A bit about emerging techniques and technologies shaping the future of analytics

Seminar & Workshop Outline

Module One: Concepts of Analytics

  • Analytics Defined
  • Data Analytics and Business Analytics
    • Variations of Purpose
    • Variations of Skills
  • Why Analytics
    • Cause and Effect
    • Strategy and Analytics
    • Tactics and Analytics
    • Operations and Analytics
    • Systemic Analytics
  • Analytics Processes
    • Problem Framing
    • Problem Modeling
    • Solution Modeling
    • Visualization and Presentation
    • Understanding and Action
  • Analytics Foundations
    • Data
      • Scope of Data
      • Finding Data
      • Observations and Populations
      • Raw Data vs. Summary Data
      • Data Preparation
    • Statistics
      • Histograms
      • Distribution and Deviation
      • Correlation
      • Regression
    • Visualization
      • Visual Design
      • Choosing Charts and Graphs
    • Business Impact
      • Simulation
      • Optimization
      • Automation

Module Two: The Analytics Environment

  • Analytics Stakeholders
    • The Participants
      • Business Stakeholders
      • Analytic Modelers and Data Scientists
      • IT and Data Organizations
  • Analytic Culture
    • Values, Beliefs, and Competencies
      • Numeracy
      • Collaboration
      • Conversation
      • Decision Styles
  • Analytics Organizations
    • Organization Models
      • Self Service
      • Shared Services
      • Central Services
      • Hybrid Organizations
    • Analytics and Governance
  • Analytics Capabilities
    • Business Capabilities
      • Planning
      • Executing
      • Adapting
      • Innovating
    • Analysis Capabilities
      • Evaluating
      • Detecting
      • Predicting
      • Classifying
      • Recommending
      • Monitoring
    • Data Capabilities
      • Measuring
      • Searching and Acquiring
      • Blending and Integrating
      • Securing
      • Provisioning
    • A Capability-Based Framework
      • Discovery Analytics
      • Descriptive Analytics
      • Diagnostic Analytics
      • Predictive Analytics
      • Prescriptive Analytics

 Module Three: Analytics Architecture

  • The Big Picture
  • Data Architecture
    • Data Sources and Types
    • Data Acquisition
    • Data Ingestion
    • Persistence
    • Data Management Topology
    • Data Quality and Utility
    • Data Usage / Information Delivery
  • Process Architecture
    • Next Generation BI
      • Extending BI
    • Basic Data Analysis
      • Statistical Analysis
      • Time-Series Analysis
    • Discovery and Prediction
      • Data Mining
      • Predictive Modeling
      • Ensemble Modeling
    • Text and Language Analysis
      • Natural Language Processing
      • Text Mining
    • People and Behaviors
      • Sentiment Analysis
      • Behavioral Analytics
    • People and Social Media
      • Social Network Analysis
      • Social Media Analytics
    • Events and Data Streams
      • Stream Processing
      • Complex Event Processing
    • Smart Machines
      • Machine Learning
      • Cognitive Computing
  • Technology Architecture
    • Connectivity
      • SQL
      • Messaging
      • Services
      • Replication
      • Virtualization
    • Data Stores
      • RDBMS
      • Columnar
      • MPP
      • MDDB
      • NoSQL
      • In Memory
    • Data Analysis
      • Data Mining
      • Analytic Modeling
      • Big Data Analytics
      • Streaming Analytics
      • Event Processing
      • Machine Learning
      • etc.
    • Data Flow
      • Batch
      • Real-time
      • Streams
    • Management
      • Workflow
      • Service Levels
    • Platforms
      • Servers
      • Appliances
      • Cloud

Module Four: Analytic Modeling

  • The Roles of Models
    • Understanding the Problem Space
    • Understanding the Data
    • Understanding the Language
    • Understanding the Business Dynamics
  • Kinds of Models
    • Framing Models
      • Questioning
      • Kernel Seeking
    • Cause-Effect Models
      • Influence Models
      • Causal Loop Models
    • Data Models
      • Physical Models
      • Logical Models
      • Conceptual Models
    • Language Models
      • Ontology
      • Taxonomy
      • Lexicon
      • Semantics
    • Solution Models
      • Formula Based
      • Algorithm Based
  • Problem Modeling
    • Framing the Problem
    • Influence Diagramming
    • Causal Modeling
  • Solution Modeling
    • Formula Based Modeling
      • Structuring
      • Defining
      • Documenting
      • Developing
      • Parameterizing
      • Visualizing
    • Algorithm Based Modeling
      • Business Understanding
      • Data Understanding
      • Data Preparation
      • Model Building
      • Evaluation
      • Deployment

Module Five: Applied Analytics

  • Discovery Analytics
    • Description
    • Techniques
      • Experimental Design
      • Rule Discovery
      • Data Mining
      • Regression Models
    • Enabled Business Capabilities
    • Examples
  • Descriptive Analytics
    • Description
    • Techniques
      • Statistical Models
      • Probability Distribution Models
      • Monte Carlo Models
    • Enabled Business Capabilities
    • Examples
  • Diagnostic Analytics
    • Description
    • Techniques
      • Control Charts
      • Classification Models
      • Abnormal Condition Models
    • Enabled Business Capabilities
    • Examples
  • Predictive Analytics
    • Description
    • Techniques
      • Regression Models
      • Neural Network Models
      • Time Series Forecasting Models
      • Bayes Theorem Models
    • Enabled Business Capabilities
    • Examples
  • Prescriptive Analytics
    • Description
    • Techniques
      • Discrete Event Models
      • Continuous Simulation Models
      • Optimization Models
      • Linear Programming Models
    • Enabled Business Capabilities
    • Examples

Module Six: Summary and Conclusion

  • Summary of Key Points
  • References and Resources

Audience:

Business leaders and managers seeking to understand business dynamics through analytics; IT leaders and managers responsible for delivering and supporting analytics initiatives; BI and analytics architects guiding the design, development, and deployment of analytics; BI and analytics designers and developers; business analysts, data analysts, data scientists, and those who aspire to these roles.

Day 2
TDWI Predictive Analytics Fundamentals
26 April 2016, London

Predictive analytics is a set of techniques used to gain new knowledge from large amounts of raw data by combining data mining, statistics, and modeling. Predictive analytics goes beyond insight (knowing why things happen) to foresight (knowing what is likely to happen in the future). Predictive models use patterns in historical data to identify and quantify probabilities of future opportunities and risks. Virtually every industry—insurance, telecommunications, financial services, retail, healthcare, pharmaceuticals, and many more—uses predictive analytics for applications such as marketing, customer relationship management, fraud detection, collections, cross-sell and up-sell, and risk management.

This course introduces predictive analytics skills, which encompass a variety of statistical modeling techniques, including linear and logistic regression, time-series analysis, classification and decision trees, and machine-learning techniques. Beyond statistics skills, predictive analytics requires knowledge of problem framing, data profiling, data preparation, and model evaluation.

Learning Objectives:

  • Definitions, concepts, and terminology of predictive analytics
  • Common applications of predictive analytics
  • How and where predictive analytics fits into a BI program and the relationships with business metrics, performance management, and data mining
  • To distinguish among various predictive model types and understand the purpose and statistical foundations of each
  • Organizational considerations for predictive analytics, including roles, responsibilities, and the need for business, technical, and management skills

Seminar & Workshop Outline          

Module 1. Predictive Analytics Concepts

  • What and Why of Predictive Analytics
    • Predictive Analytics Defined
    • Business Value of Predictive Analytics
  • The Foundation for Predictive Analytics
    • Statistical Foundation
    • Data Mining Foundation
  • Predictive Analytics in BI Programs
    • Predictive Analytics in the BI Stack
    • Predictive Analytics in the BI Roadmap
    • Business, Technical & Data Dependencies
  • Common Applications for Predictive Analytics
    • What Business Needs to Predict

Module 2. Data Mining Fundamentals

  • Statistics and Data Mining
    • Data Description Basics
    • The Shape of the Data
    • Variables
    • Relationships and Dependencies
    • Probability
  • Data Mining Processes
    • Data Mining Projects
    • CRISP-DM
    • SEMMA
    • CRISP-DM & SEMMA Compared
  • Data Mining People
    • A Team Effort
    • Roles and Responsibilities
  • Data Mining Models
    • What Are Models?
    • Kinds of Models
    • Some Examples
    • How Are They Built?
  • Data Mining Techniques
    • Classification
    • Segmentation
    • Association
    • Sequencing
    • Forecasting
  • Data Mining Technology
    • Features and Functions Overview
    • The Tools Landscape
  • Data Mining Algorithms
    • What and Why
    • Some Examples

Module 3. Predictive Mining and Modeling

  • Introductory Concepts
    • Distribution View
    • Model Types View
    • Process View
    • Process Overview
  • Business Understanding
    • Activities and Deliverables
    • Pragmatics
  • Data Understanding
    • Activities and Deliverables
    • Pragmatics
  • Data Preparation
    • Activities and Deliverables
    • Pragmatics
  • Modeling
    • Activities and Deliverables
    • Pragmatics
  • Evaluation
    • Activities and Deliverables
    • Pragmatics
  • Deployment
    • Activities and Deliverables
    • Pragmatics

Module 4. Human Factors in Predictive Analytics

  • Analytic Culture
    • Executive Buy-In
    • Strategic Positioning
    • Enterprise Range and Reach
    • Decision Processes
  • People and Predictive Analytics
    • The Range of People
    • The Range of Knowledge
    • Readiness
    • Trust and Motivation
    • Expectations and Intent
    • Getting from Analytics to Impact
  • Ethics and Predictive Analytics
    • Why Ethics Matters
    • Data and Ethics
    • BI, Analytics, and Ethics
    • Managing Ethics in Business

Module 5. Getting Started with Predictive Analytics

  • Predictive Analytics Readiness
    • Readiness Checklist
    • Executive Commitment
    • Organizational Buy-In
    • Data Assets
    • Human Assets
    • Technology Assets
  • Predictive Analytics Roadmap
    • A Plan to Evolve
    • An Evolving Plan
  • Predictive Analytics Success Factors
    • Pragmatism and Realism
    • Best Practices
    • Mistakes to Avoid
  • Building Skills and Competencies

Audience:

BI program managers, architects, and project managers; business analysts who want to extend from gaining insight to providing foresight; business managers who need new tools to help them shape the future of the business; anyone interested in the basics of predictive analytics.

Day 3
TDWI Data Visualization Fundamentals
27 April 2016, London

Data visualization has rapidly become a critical part of business analytics and business communications. Without visualization, the numbers and statistics of analytics are difficult to interpret and incomprehensible to many who need to turn data into knowledge. The advent of big data, with increasing volume and velocity of data, emphasizes visualization as a technique to compress large volumes of data into digestible presentations and observe streaming data in motion.

Elegant and well-designed data visuals often appear to be easy because skilled visual developers are able to hide the complexities and hard work behind the scenes. Business intelligence and business analytics professionals need to communicate as effectively in visual forms as they do with their verbal and written communications skills. Get started by learning the fundamentals of data visualization.

Learning Objectives:

  • Visualization as a communication medium
  • Preparing data for visualization
  • Components of visualization
  • Choosing and using charts and graphs
  • Visual exploration and analysis
  • Visual design techniques
  • Extending visualization with infographics
  • Visual storytelling
  • Data visualization tools

Seminar & Workshop Outline

Module 1: Data Visualization Concepts and Principles

  • Communication and Visualization      
    • Communicating with Words
    • Communicating with Numbers
    • Communicating with Visuals
  • Communication and Data      
    • The Purpose of Data
    • The Content of Data
    • Uncertainty in Data
  • Data Visualization Components        
    • The Parts of Data Visuals
  • Visual Cues    
    • Placement
    • Lines
    • Shapes
    • Color
    • Human Perception
  • Coordinate Systems  
    • Cartesian Coordinates
    • Polar Coordinates
    • Geographic Coordinates
  • Measurement Scales 
    • Linear and Logarithmic Scales
    • Ratio and Interval Scales
    • Percent Scales
    • Time Scales
  • Visual Context
    • Explicit and Implicit Context

Module 2: Data Visualization Techniques     

  • Data Visualization Functions
    • What We Visualize
    • Comparisons
    • Proportions
    • Relationships
    • Patterns
  • Data Visualization Methods   
    • How We Visualize
    • Tables
    • Plots
    • Maps
    • Infographics
  • Common Charts and Graphs
    • Line Graphs
    • Column Graphs
    • Bar Graphs
    • Pictographs
    • Pie Charts and Donut Charts
    • Choropleth Maps
    • Scatter Graphs
    • Area Graphs
    • Surface Graphs
    • Bubble Graphs
  • Visualization with Purpose     
    • What Do You Want to Show?
    • Comparisons
    • Proportions
    • Relationships
    • Patterns
  • Choosing Charts and Graphs
    • From What to How
    • From Questions to Visuals

Module 3: Visual Design        

  • Data Preparation        
    • Getting Ready for Data Visualization
  • Design Techniques    
    • Design Objectives
    • Object Properties
    • Spatial Properties
    • Visual Hierarchy
    • Pre-Attentive Processing
  • Visual Design Considerations
    • Colors and Fonts
    • Size and Scale
    • Two-Dimensional vs. Three Dimensional
  • Design with Impact
    • Purpose
    • People
    • Encodings and Cues
    • Good Visualizations

Module 4: Applied Data Visualization

  • Visual Data Exploration         
    • Profiling Data
    • Data Exploration as a Process
    • Exploring Categorical Data
    • Exploring Time Series Data
    • Exploring Spatial Data
  • Visual Data Analysis  
    • From Questions to Answers
  • Visual Reporting        
    • Dashboards, Scorecards, and Reports
    • Specialty Graphs
    • Small Multiples
  • Infographics   
    • Visual Design plus Graphic Design
    • Simple Graphs vs. Infographics
    • Design
    • Team and Process
  • Data Storytelling        
    • Statistics vs. Stories
    • Visual with Narrative

Module 5. Summary and Conclusion

  • Data Visualization Tools        
    • A Technology Overview
  • Best Practices in Visualization           
    • Advice from the Master 

Audience:

Business analysts and data analysts; data scientists and analytics modelers; business analytics leaders and managers; BI leaders and managers; anyone who develops charts and graphs to communicate about data.

Speaker's Biography

Deanna Larsen

Dr. Larson is an active practitioner and academic focusing on business intelligence and data warehousing with over 20 years of experience. Dr. Larson completed her doctorate in management in information technology leadership. Her doctoral dissertation research focused on a grounded theory qualitative study on establishing enterprise data strategy. She holds project management professional (PMP) and certified business intelligence professional (CBIP) certifications. Larson attended AT&T Executive Training at the Harvard Business School in 2001, focusing on IT leadership. She is a regular contributor to TDWI publications and presents several times a year at conferences. Dr. Larson is associate faculty at American Public University and University of Phoenix in the United States.

In-House Training
If you require a quote for running an in-house course, please contact us with the following details:

  • Subject matter and/or speaker required
  • Estimated number of delegates
  • Location (town, country)
  • Number of days required (if different from the public course)
  • Preferred date

Please contact:
Jeanette Hall
E-mail: jeanette.hall@irmuk.co.uk
Telephone: +44 (0)20 8866 8366
Fax: +44 (0) 2036 277202

Speaker: Deanna Larsen
Deanna Larsen


 

IRM UK Conferences

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2 co-located conferences
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Enterprise Data and BI Conference Europe 2017
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