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Business Analytics in Healthcare

(WYU-7030-MDMBA.APC1)
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1

Introduction to Analytics

  • The Importance of Quality Source Data
  • Data Structure Types
  • Centralized Data Benefits
  • Structured vs. Unstructured Data
  • Types of Data
  • Typical Sources of Business Data
  • Data Protection Policies
  • Search Engine Optimization
  • Data Life Cycle Management (DLM)
  • Data Analysis Process
  • What’s in a Name?
  • Why the Sudden Popularity of Analytics and Data Science?
  • The Application Areas of Analytics
  • The Main Challenges of Analytics
  • A Longitudinal View of Analytics
  • A Simple Taxonomy for Analytics
  • The Cutting Edge of Analytics: IBM Watson
  • What Is Data Mining?
  • What Data Mining Is Not
  • The Most Common Data Mining Applications
  • What Kinds of Patterns Can Data Mining Discover?
  • Popular Data Mining Tools
  • The Dark Side of Data Mining: Privacy Concerns
2

Predictive Analytics Process and Data Sources

  • The Knowledge Discovery in Databases (KDD) Process
  • Cross-Industry Standard Process for Data Mining (CRISP-DM)
  • SEMMA
  • SEMMA Versus CRISP-DM
  • Six Sigma for Data Mining
  • Which Methodology Is Best?
  • Data E-Harmony: Working with Different Departments to Bring Data Together
  • The Purpose of Customer Relationship Management (CRM)
  • CRM Integration: A Banking Scenario
  • Obtaining Data from Email and User Forums
  • Obtaining Data from Other Knowledge Bases
  • Obtaining Data from CRM and Business-to-Business Frameworks
  • Transaction, Payment and Inventory Data
  • Using Multiple Data Sources
3

Tools for Capturing, Analyzing, and Reporting Data

  • Data Analytics Tools
  • Capturing Data: Tableau Public
  • Capturing Data: Google BigQuery
  • Capturing Data: OpenRefine
  • Overview: Hadoop-Based Environments
  • Capturing and Analyzing Data in Hadoop
  • The R Project
  • Additional Software for Data Capture
  • Network Traffic
  • Data Integration
  • Why Testing is Important?
  • Statistical Computing and Programming
  • Organizational Efforts and Business Outcomes
  • Best Methods to Capture and Report Specific Data
  • Data Analysis and Reporting Dashboards
  • Create Reports and Charts
  • Create a Presentation for Reporting Data
  • Frequently Asked Questions for Presentations
4

Methods and Algorithms for Predictive Analytics

  • The Nature of Data in Data Analytics
  • Preprocessing of Data for Analytics
  • Data Mining Methods
  • Prediction
  • Classification
  • Decision Trees
  • Cluster Analysis for Data Mining
  • k-Means Clustering Algorithm
  • Association
  • Apriori Algorithm
  • Data Mining and Predictive Analytics Misconceptions and Realities
  • Naive Bayes
  • Nearest Neighbor
  • Similarity Measure: The Distance Metric
  • Artificial Neural Networks
  • Support Vector Machines
  • Linear Regression
  • Logistic Regression
  • Time-Series Forecasting
5

Introduction to Health IT and Data Flow in HIT

  • The Importance of the HITECH Act
  • Getting to Know Healthcare
  • The Goals of Healthcare IT
  • Learning Support Flow
  • Getting to Know Information Systems
  • Getting to Know HIS
  • Getting to Know the Interface
  • Following the Data Flow
6

Advanced Topics in Predictive Modeling

  • Model Ensembles
  • Bias–Variance Trade-off in Predictive Analytics
  • Imbalanced Data Problems in Predictive Analytics
  • Explainability of Machine Learning Models for Predictive Analytics
7

Bigdata for Predictive Analytics

  • Big Data
  • The Importance of IT Data Management
  • IT Business Environments
  • Cloud-Based Data
  • Cloud-Native Data
  • In-House Data
  • When to Migrate In-House Data to the Cloud
  • Variations of Cloud-Based Systems
  • Typical Databases Used for Data Analysis
  • Data-Driven Business Decisions
  • Impact of Data Errors
  • Importance of Organizational Strategy and Data Quality in Data Analytics
  • Data Modeling 
  • Importance of Data Maintenance and Data Backup
  • Where Does Big Data Come From?
  • The Vs That Define Big Data
  • Fundamental Concepts of Big Data
  • The Business Problems That Big Data Analytics Addresses
  • Big Data Technologies
  • Data Scientists
  • Big Data and Stream Analytics
  • Data Stream Mining
8

Deep Learning and Cognitive Computing

  • Introduction to Deep Learning
  • Basics of “Shallow” Neural Networks
  • Elements of an Artificial Neural Network
  • Deep Neural Networks
  • Convolutional Neural Networks
  • Recurrent Networks and Long Short-Term Memory Networks
  • Computer Frameworks for Implementation of Deep Learning
  • Cognitive Computing
9

Text Analytics, Topic Modeling, and Sentiment Analysis

  • Natural Language Processing
  • Text Mining Applications
  • The Text Mining Process
  • Text Mining Tools
  • Topic Modeling
  • Sentiment Analysis

1

Introduction to Analytics

  • Creating a Decision Tree in Python
  • Creating a Decision Tree in KNIME
2

Predictive Analytics Process and Data Sources

  • Calculating the Churn Rate
  • Analyzing Customer Relationship Management
  • Calculating Consumer Lifetime Value in Banking
  • Understanding the RFM Analysis for Customer Segmentation
3

Tools for Capturing, Analyzing, and Reporting Data

  • Creating a Stacked Bar Chart
  • Using RStudio
  • Creating a Gantt Chart
  • Comparing Prezi and PowerPoint Presentations
  • Creating a PowerPoint Presentation
4

Methods and Algorithms for Predictive Analytics

  • Running k-Means Clustering Algorithm in KNIME
  • Using the k-Nearest Neighbor Algorithm
  • Using ANN and SVM for Prediction Type Analytics Problems
  • Implementing Linear Regression in Python
  • Implementing Linear Regression Model in KNIME
5

Introduction to Health IT and Data Flow in HIT

  • Identifying acute care facilities
  • Understanding acuity level index
  • Understanding standard departments of hospitals
  • Understanding HIS
  • Understanding types of HL7 messages
  • Understanding functions of an interface engine
  • Identifying primary functions of HIS
  • Identifying the real-time transfer of data
  • Identifying the steps for the EDIS ADT data flow
  • Identifying steps in the pharmacy IS
  • Identifying steps to follow the EDIS data flow
6

Advanced Topics in Predictive Modeling

  • Showcasing Better Practices With a Customer Churn Analysis
7

Bigdata for Predictive Analytics

  • Analyzing and Utilizing Big Data
  • Adapting to Changing Data Requirements
  • Comparing Relational Database Management Systems
  • Analyzing DDDM and Data for Blanket Technology
8

Text Analytics, Topic Modeling, and Sentiment Analysis

  • Performing Topic Modeling
  • Performing Sentiment Analysis

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