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BAA0: Business Analytics in Action Learning Plan

In the Business Analytics in Action Learning Plan, instructor Prashanth Southekal closely examines the concepts key to enterprise data analytics and machine learning, along with their associated values, and how they are integral for any organization. Each course focuses on the critical skills that are required across four main analytics domains and also disseminates how these tools are vital in your data career. As simple and clear as these tools may sound initially, the truth is much more complex. Throughout the six-course learning plan, we evaluate not just what analytics, machine learning, enterprise systems, data engineering, and quality data mean today, but how they are constantly evolving, and why they will require ongoing refinement for a long time to come. If all six courses are completed, a certificate of completion will be issued. This learning plan also includes a number of practical exercises that students can work on at home.

In the Business Analytics in Action Learning Plan, instructor Prashanth Southekal equips participants with key enterprise data analytics and machine learning concepts and skills across four main analytics domains: Data Management, Data Engineering, Data Science, and Data Monetization. This training comprises six courses, and these courses explore business analytics techniques to formulate and solve business problems and to support managerial decision-making. Students will also learn how to use and apply Excel and Excel add-ins to solve business problems that rely on data analytics.

This learning plan has four key learning objectives:

Understanding business data and systems
Learning the three main types of business analytics
Applying analytics techniques and interpreting the results in a business context
Communicating the data and insights derived to the business stakeholders

This is not intended to be a highly technical offering, but rather is valuable to data and business professionals ranging from technically proficient to not technically oriented people. Even those considering a path in Data Management, or working closely with data engineers, will find many useful insights throughout these courses. It does have in-depth discussions and exercises around the mathematics, statistics, and practical usage of different types of analytics, but the instructor walks students though each of the exercises.

Learning Plan Price: $699
Individual Course Price: $99 - $159
Learning Plan CEUs: 9.0 hours

Each Course Includes:

A 37- to 120-minute educational training video
A 13- to 26-question exam
Materials made available for download once the exam has been completed
Self-paced and on-demand e-learning
Unlimited course access
A number of practical exercises users can download and complete from home

Courses within the Business Analytics in Action Learning Plan:

Introduction to Business Analytics and Enterprise Systems
Essential Mathematics for Business Analytics
Quality Data and Descriptive Analytics
Predictive Analytics and Machine Learning
Prescriptive and Causal Analytics
Data Monetization and Wrap-Up

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

Business Analytics in Action Learning Plan

BAA1: Introduction to Business Analytics and Enterprise Systems

Domains in Enterprise Data Analytics
Introduction: Definition of Analytics
- Insight Source #1: Intuition
- Insight Source #2: Data
Three Key Developments in Business Analytics
Data and Market Capitalization
Limitations of Data
Enterprise Data
- What Is Business Data?
- Key Characteristics of Business Data
- Data and Business Value
Business Data Characteristics
- Multiple Stakeholders/Consumers
- Purpose-/Context-Driven
- Legal Implications
Types of Business Data
Structured vs. Unstructured Data
Business Data Based on Integration
Analytics Data Types
Business Data Taxonomy
Balancing Cost and Business Value
Business Data: Asset or Liability
Enterprise Systems
- Key Features of Enterprise Systems
- Types of Enterprise Systems
- Generic Enterprise System Architecture
Basics of Database
- Which Database to Use?
OLTP Systems
Types of Middleware Systems
- Application Integration Middleware Systems
- Data Integration Middleware Systems
- ETL vs. DV
OLAP Systems
Five Key Features of BI/OLAP Reports
OLAP Cube
Data Warehouses (Data Marts and Data Lakes)
Domains in Enterprise Data Analytics
Data Lifecycle (DLC)
Data Engineering Functions
Why Is Analytics Heavy on Data Engineering?

BAA2: Essential Mathematics for Business Analytics

Five Main Types of Statistics/Analytics
- Data Types
Two Areas in Exploratory Statistics
Measures of Central Tendency
- Mode
- Median
- IQR
- Arithmetic Mean (AM)
- Geometric Mean (GM)
- Harmonic Mean
Applying the Means — AM, GM, and HM
Comparing the Three Measures
Background to Understand Spread
- Variation in Business
  - Types of Variation in Business
- Normal Distribution (Bell Curve)
  - Plot of Normal Distribution
  - Why Normal Distribution Matters (in Business)
- CI/CL (Confidence Interval and Confidence Level)
Measures of Spread (Variability)
- Standard Deviation (SD)
  - Who Is a Reliable Performer (Vendor)?
  - Can Standard Deviation Be Negative?
- Standard Error
  - Accuracy (SD) and Precision (SE)
- Variance
- Range
- Kurtosis
- Skewness
  - Test for Normality?
  - Statistical Tests and Data Distribution
  - Causes for Business Data Not Being Normally Distributed
  - How to Get a Normally Distributed Data Set
- Z-Score
  - Application #1 of Z-Score — Comparison Against the Population
  - Application #2 of Z-Score — Outliers
  - Application #3 of Z-Score — Comparison Under Different Conditions
- Statistics Is Getting Answers to Questions
- Analytics in MS Excel
- Descriptive Analytics on Store Data XL File
- Linear Algebra — Introduction
  - Three Key Elements in Linear Algebra
- Operations on Scalar, Vector, and Matrix
- Examples of Matrix Operations
- Steps to Solve Linear Equations

BAA3: Quality Data and Descriptive Analytics

Definition of Analytics
- Main Types of Enterprise Analytics
The Challenge of Quality Data
- Definition of Quality Data
Why Enterprises Need Sample Data
Getting Quality Sample Data
- Sample Size (SS) for the “Right” Sample
- Representation for the “Right” Sample
- Representation (Runs Test) for the “Right” Sample
Descriptive Analytics Taxonomy
- TABULAR/OLTP Reports
- BI/OLAP Reports
What Is a Dashboard?
- Key Elements of a Good Dashboard
- Dashboard vs. Report
KPI (Key Performance Indicator)
- Business-Driven KPIs
- Technical-Driven KPIs
- Measurement Theory-Driven KPIs
Seven Principles for Converting Metrics into KPIs
KPIs, Dashboards, and Implementation
Key Data Visualization Objects
- Bar Charts (Pareto)
- Stacked Bar Charts — Area and Column
- Pie Charts (Pareto)
- Spider Plots (Radar Plots)
- Box and Whisker Plots
- Histograms
- Run Charts (Line Charts)
- Scatter Plots
- Bubble Charts
- Sunburst Charts
- Dials, Gauges, and Traffic Lights (DGT)
- Heat Maps
- Sankey Charts
Inferential and Associative Analytics
Statistical Tests and Data Distribution
Relationships Between Variables
Correlation Terms
Hypothesis Testing
Level of Significance and P-value

BAA4: Predictive Analytics and Machine Learning

Introduction to Predictive Analytics
- Use Cases of Predictive Analytics
Regression Analysis
Correlation vs. Regression
Important Types of Regression Analysis
Simple Regression Model
- Simple Linear Regression (SLR) Exercise
- Simple Linear Regression with Excel
- SLR Output
- SIX Key Elements for SLR Evaluation
  - Coefficient of Determination (R²)
  - Standard Error (SE)
  - Coefficients
  - Confidence Interval for Variable
  - P-value of Independent Variable
  - F-value of the Model
- Multiple Regression Model
- Multiple Linear Regression (MLR)
- Seven Key Elements in the MLR Output
  - Multicollinearity in MLR
  - Adjusted R2
  - Standard Error
  - Coefficients
    - Which Independent Variables Are Important?
  - Confidence Interval
  - P-value of the Independent Variables
  - F-value
- Using the Model for Prediction
- Multiple Regression Model with Dummy Variables
  - Dummy Variables in Regression
  - HR Analytics
  - MLR-Dummy Variable Output
  - Predicting Employee Performance
- Logistic Regression
  - The Problem
  - The Insight Derivation Model
  - Sales/MBH Transaction + SLV
  - Store Performance Volatility (SPV)
  - Predict the SPV for the Retailers in Scope
  - Categorize Risk Levels of SMV and SLV
  - Integrate SPV and SLV
  - Assign Stores into Four Categories Based on SPV and SLV
  - Personalized Support to Retailers
  - Validating the Predictive Analytics Model
- What Is Machine Learning?
- ML and Predictive Analytics
- The Seven Steps of Machine Learning
- ML vs. Non-ML Solutions
- Types of ML Algorithms
  - Bi-class
  - Multi-class
- Types of Classification Algorithms
  - Logistic Regression
  - Support Vector Machines (SVM)
  - Decision Trees
  - Nearest Neighbor (NN)
- Clustering ML Algorithms
  - K-Means
  - Sturges’s Rule
  - Classification vs. Clustering
  - K-Means Flowchart
- Association ML Algorithms
  - Correlation
  - Pure Association ML Algorithms

BAA5: Prescriptive and Causal Analytics

Prescriptive Analytics
Forecasting vs. Backcasting
Introduction to Sensitivity Analysis
One-Variable Data Table
Two-Variable Data Table
Casual Analytics Provides the “Why”
Correlation Is Not Causation
Distinguishing Correlation and Causation
Executing Causal Analytics
A/B Testing Steps
Experimental Data for Causal Analytics
Create Your Own Data
- Feature Engineering (FE)
- DOE/Experiments
- Proof of Concept (PoC)/Prototype
- Surveys
Data Blending
- Data Acquisition
- Joining Data
- Data Cleansing

BAA6: Data Monetization and Wrap-Up

Table of Contents
- Data Monetization
- Data Governance
- Careers in Analytics
- Wrap-Up
Data Monetization
- Data Products
  - Data-Enhancing Platforms
  - Data-Exchanging Platforms
  - Data-Experiencing Platforms (External and Internal)
- Financial Benefits
  - Key Business Metric
  - Calculating ROIC
- Data Storytelling
  - How to Tell a Data Story
  - Five Elements of Persuasive Narration
- Store Data Governance in a Retail Chain
- Nine Key Data Governance Principles
- Data Security Strategies
- Data Privacy Regulations
- Data Analytics Roles
  - Starting Your Analytics Career
  - Types of Analytics Skills in the Industry
- Popularity of Three Main Data Science Tools
- Magic Quadrant for Data Analytics
- Resources on Analytics
- Analytics Framework — Project Playbook
- Tool Selection Flow Chart for Generating Insights
- How to Get Good Data for Business Analytics