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PA0: Predictive Analytics for Business Results Learning Plan

In the Predictive Analytics for Business Results Learning Plan, Dr. Prashanth H. Southekal equips participants with crucial data and analytics concepts. The course offers a deep dive into applying predictive analytics in business to enhance success, providing essential tools for data analysis and informed decision-making. Each of the four courses explores foundational principles that empower organizations not only to understand past events but also to anticipate future outcomes effectively, thereby enabling better preparation and response strategies.

In today's volatile, uncertain, complex, and ambiguous (VUCA) global market, the increasing value of predictive data and analytics initiatives cannot be overstated. Most organizations still rely on descriptive analytics insights that are easy to derive and deterministic, yet without forward visibility, they lack the scope of information necessary to make data-driven predictions that improve planning and response capabilities. Traditional descriptive analytics focused on past performance as a foundation for historical analysis, but this approach presents significant limitations for modern organizations.

The power of predictive analytics in deriving forward-looking insights is crucial for proactive planning. By applying advanced analytics to leverage data for strategic decision-making, predictive analytics offers numerous possibilities for enterprises to create a sustainable competitive advantage and be the next frontier for innovation and productivity in business.

In this 4-course Predictive Analytics for Business Results Learning Plan, instructor Dr. Prashanth H. Southekal equips participants with the necessary foundation, processes, and techniques of predictive data analytics (including machine learning) and its key characteristics to successfully formulate data analytics models for deriving future insights and communicate these insights to the organization and business stakeholders. With a strong focus on the application of data and insights for improved business performance, this training is designed for professionals seeking to build on technical and leadership competencies.

In the Predictive Analytics for Business Results Learning Plan you will:

Understand key data and data analytics concepts for implementing predictive data analytics solutions
Learn to formulate predictive data analytics models to derive insights for the future
Grasp the application of predictive analytics techniques to home in on insights and communicate those to the business stakeholders
Gain awareness of how to spot business opportunities and use cases for predictive analytics
Explore the capabilities and risk for implementing predictive data and analytics solutions/models
Learn to improve the adoption of predictive data and analytics solutions in any organization

Individual Course Price: $99
Learning Plan Price: $336
Learning Plan CEUs: 8.0 hours

Each Course Includes:

A 90- to 120-minute educational training video
The videos for each course are divided into smaller sections for convenient viewing
A 20- to 27-question exam
“Check for Understanding” quizzes after each course section
Self-paced and on-demand e-learning
Unlimited course access
Downloadable documents/resources include:
- Practice Data Sets (used in the course) (Excel file)
- 12 Dimensions of Data Quality (.pdf download)
- And an additional 7 .pdf article downloads to assist understanding of the core course concepts

Courses within the Predictive Analytics for Business Results Learning Plan:

Introduction to Data and Analytics
Predictive Data Analytics Foundations
Predictive Data Analytics Techniques
Data Storytelling & Wrap-up

We offer several bulk licensing options for corporate and group use.

Predictive Analytics for Business Results Learning Plan

Course 1: Introduction to Data Analytics

Course 1 Goals
Learning Plan Focus and Course Plan
Learning Plan Objectives
Expected End State
Prerequisites for this Course
Course 1: Table of Contents
- Introduction to Data Analytics
- Four Key Components in Business Analytics
- IT Systems and DLC
Introduction to Data Analytics
- Definition of Analytics
- The Measurement of Continuum: Ignorance to Insights
- Benefits of Data Analytics to Business
  - Types of Data Analytics – The Analytics Continuum
- What is Insight?
  - Insight Source #1 – Intuition (and Science)
  - Insight Source #2 – Data and Analytics
  - Holistic Insight
- FAAR Framework
Four Key Components in Business Analytics
- 1. Business Data
  - Types of Business Data
    - 1. Business Data Based on Storage
    - 2. Business Data Based on Integration
      - Example of Integrated Business Data
    - 3. Business Data Based on Security
      - Data Conversion
    - 4. Business Data Based on Analytics
      - Data Transformation in Analytics
- 2. Models
  - Algorithms
  - Analytics Models
  - Analytics Models Taxonomy
- 3. Ethics
  - Ethics in Analytics
- 4. Assumptions
  - Assumptions in Analytics
  - Data Availability and Question/Insight Complexity
  - Techniques to Manage Assumptions
IT Systems and DLC
- Types of Enterprise Systems
- Generic Enterprise System Architecture
  - Example of Enterprise System Architecture
- Data Lifecycle (DLC) (Data Lineage)
Summary of Course 1

Course 2: Predictive Data Analytics Foundations

Course 2 Goals
Learning Plan Focus and Course Plan
Summary of Course 1
Course 2 - Table of Contents:
- Introduction to Predictive Data Analytics
- Key Predictive Data Analytics Concepts
- Smart Data Acquisition for Analytics
Introduction to Predictive Data Analytics
- Use Case of Predictive Data Analytics
- Predictive Data Analytics Characteristics
Key Predictive Data Analytics Concepts
- Key Statistical Concepts in Predictive Analytics
  - 1. Variables in Analytics
    - Types of Variables in Analytics
    - Hypothesis Formulation: Oil Pipeline Prediction
  - 2.Correlation
    - Spurious Correlation
    - Correlation Vs. Causation
    - How to Measure
    - Hypothesis Testing
  - 3. P-Value
    - P-Value and Hypothesis Testing
    - Adam V/S Roger
    - 3 Key Points in Hypothesis Testing
Smart Data Acquisition for Analytics
- 1. Data Sampling
  - 1.1 Sample Size
  - 2. Representative of Calgary’s Population
    - Representation for the Right Sample
  - 3. Sourcing Random Data Records
- 2. Feature Engineering
  - Feature Engineering Example
  - Feature Engineering Attribute Construction Steps
- 3. Acquire and Blend
  - 3.1 Data Acquisition
  - 3.2 Combine Data
  - 3.3 Data Cleaning
- 4. Synthetic Data
  - Creating Synthetic Data with ChatGPT Prompts
  - Synthetic Data: UAT and Mystery Shopper
- Data Profiling with Exploratory Data Analytics
- Central Tendency and It’s Measures
- Normal Distribution (Bell Curve)
  - Why Normal Distribution Matters (in Business)
- Spread (Variability) and Its Measures
- Analytics in Excel (1/2) (in PC)
  - Analytics in Excel (2/2) (in PC)
- Analytics in Excel (1/2) (in Mac)
  - Analytics in Excel (2/2) (In Mac)
- Data Profiling Exercise (Open Excel File)
- Descriptive Analytics on Store Data Excel File
- Data Profiling
Summary of Course 2

Course 3: Predictive Analytics Techniques

Course 3 Goals
Learning Plan Focus and Course Plan
Summary of Course 2
Predictive Analytics Techniques
Course 3 – Table of Contents:
- Trend Predictive Data Analytics Technique
- Regression Predictive Data Analytics Techniques
- Machine Learning
Trend Predictive Data Analytics Techniques
- Trend Line
  - 1. Trend Line in Correlation
  - 2. Trend Line using TREND () Function
Regression Predictive Data Analytics Technique
- Regression
- Types of Regression
- Assumptions in Regression
- Regression Model (SLR)
- Steps in Regression
  - 1. Simple Linear Regression (SLR)
    - SLR Example
    - SLR in Excel
      - Step 1: Model Determination
        
        Determining Model Coefficients from OLS
      - Step 2: Model Validation
        
        2.1 Coefficient of Determination (R2)
        
        2.2 RMSE
        
        2.3 Standard Error/Homoscedasticity Test
        
        2.4 P-Value of Independent Variables
        
        2.5 F-Value of the Model
        
        2.6 Confidence Interval for the Variable
      - Step 3: Using the Model for Prediction
  - 2. Multiple Linear Regression (MLR)
    - Key Elements in MLR Output
    - Multicollinearity in MLR
    - Redundancy in Variables
      - Criteria to Identify Redundant Variables
    - Adjusted R2
    - 1. Model Determination
    - 2. Model Validation
    - 3. Using the Model for Prediction
Machine Learning (ML)
- Where Does Machine Learning Fit?
- ML and Predictive Analytics
- ML Process
- ML V/s Non-ML Solutions
- ML Implementation – Important ML Platforms
- When ML Goes Wrong
- Types of ML Algorithms
- Ensemble Machine Learning
  - Basic Ensemble Techniques
    - Quiz
      - Quiz Answers
  - Ensemble Methods – Case Study on Revenue Forecasting
  - Improving the Performance of Analytics Models
Summary of Course 3

Course 4: Data Storytelling and Wrap-Up

Course 4 Goals
Learning Plan Focus and Course Plans
Summary of Course 3
Course 3 – Table of Contents:
- Data Storytelling
- Bases in Analytics
- Summary + Wrap-Up
Data Storytelling
- Analytics Value Chain
  - 1. Define Goals
    - Why Analytics?
  - 2. Source Data
    - Business Data
    - Data Extraction (with SQL)
    - Data Warehouse (DWH) – The SOR for Analytics
  - 3. Prepare Data
    - Data Engineering
      - Key Data Engineering
      - Wrangle, Enrich, and Transform
      - Govern Data in the SoR
  - 4. Build Models
    - Data Analytics Models
    - Data Analytics Fabric = Fri (Business Goals, Capabilities, Resources)
  - 5. Derive Insights
    - Insights
      - 1. Performance Insights
      - 2. Actionable Insights
      - FAAR Framework to Render Insights
      - The Solution
  - 6. Communicate Findings
    - Communication Process
    - Visual/Charts
      - Key Data Visualization Objects (1-4)
      - Visuals in Dashboard
      - Design of Visuals
    - Narration
    - Freytag’s Pyramid in D&A
    - 5 Elements of Persuasive Narration
  - 7. Decision Making
    - Data-driven decision-making (3DM)
    - Decision Tree
  - 8. Business Outcomes
    - Executing of Analytics Projects
      - 1. Transactions: Mainstay of Business Analytics
      - 2. Key-Performance Indicator
    - ROIC
    - Calculating ROIC
    - Risk
    - Business Value from Analytics
Biases in Analytics
- Introduction to Biases
  - Types of Biases
  - Reducing Biases
  - Algorithmic Fairness
Summary + Wrap-Up
- Summary of Course 1
- Summary of Course 2
- Summary of Course 3
- Summary of Course 4
Other Analytics Training at DATAVERSITY
Books on Data and Analytics
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