Healthcare It Decoded - Data Analytics

Using Excel Real Statistics Add-In

What you'll learn

Understand the End to End Data Analysis Workflow

Understand basics or Excel Real Statistics Addin

Apply different Machine Learning Algorithms using Excel Real Statistics AddIn

Demonstrate knowledge of applying Algorithms like Regression, KMeans using Healthcare Data

Requirements

No specific experience required.

Description

Are you Interested in learning how to apply some machine learning algorithms using Healthcare data and that too using Excel? Yes, then look no further. This course has been designed considering various parameters. I combine my experience of twenty two years in Health IT and twelve years in teaching the same to students of various backgrounds (Technical as well as Non-Technical).In this course you will learn the following:Understand the Patient Journey via the Revenue Cycle Management Workflow - Front, Middle and Back OfficeThe Data Visualization Journey - Moving from Source System to creating ReportsUnderstand Descriptive, Diagnostic, Predictive and Prescriptive Analytics  At present I have explained below AlgorithmsSimple Linear Regression | Multiple Linear Regression | Weighted Linear Regression | Logistic Regression | Multinomial Regression | Ordinal Regression | KNN Classification | KMeans Clustering Classic Time Series | ARIMA |  Some of the concepts key explained are listed below Homoscedasticity vs HeteroskedasticityBreusch-Pagan & White TestConfusion MatrixNominal vs Ordinal DataAUC & ROC CurveACF & PACF in Time SeriesDifferencing in Time Series Healthcare Datasets to create the algorithms.I have listed a the healthcare datasets used belowHealth Insurance DataCovid CasesAsthma DataObesity Member Enrollment Pharma SalesProstate CancerBreast CancerMaternal Health Risk**Course Image cover has been designed using assets from Freepik website.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Master of The Mystic Arts

Lecture 3 Installing Real Statistics AddIn

Section 2: Hospital Patient Journey

Lecture 4 Appointment to Registration

Lecture 5 Insurance Eligibility

Lecture 6 Admissions & Financial Counseling

Lecture 7 Point of Care OPD

Lecture 8 Point of Care IPD

Lecture 9 Overview of Codes & Standards

Lecture 10 Medical Transcription & Coding

Lecture 11 Back Office

Section 3: Data Visualization & Analytics - The Journey

Lecture 12 Understanding the Source Systems

Lecture 13 Extract the Data

Lecture 14 Transform the Data

Lecture 15 The Logical Data Model

Lecture 16 Load the Data

Lecture 17 Insights & Foresights

Lecture 18 A Quick Recap

Lecture 19 The Lion and The Fox

Lecture 20 Descriptive Analytics - Patient Appointments Example

Lecture 21 Diagnostic Analytics - Patient Appointments Example

Lecture 22 Predictive Analytics - Patient Appointments Example

Lecture 23 Prescriptive Analytics - Patient Appointments Example

Lecture 24 Descriptive to Prescriptive Analytics - Additional Examples

Section 4: Some Basics Definitions

Lecture 25 AI vs ML vs DL

Lecture 26 Supervised Learning

Lecture 27 Unsupervised Learning

Lecture 28 Reinforcement Learning

Section 5: Others

Lecture 29 Solving #Name Error in Excel

Section 6: Linear Regression

Lecture 30 Linear Regression - Download the Data

Lecture 31 Regression & Eugenics

Lecture 32 Simple Linear Regression - The Logic

Lecture 33 Simple Linear Regression - Overview of Insurance Data

Lecture 34 Simple Linear Regression - Method 1

Lecture 35 Simple Linear Regression - Method 1 Prediction

Lecture 36 Simple Linear Regression - Method 2

Lecture 37 Simple Linear Regression - Method 2 Prediction

Lecture 38 Simple Linear Regression - Method 3

Lecture 39 Simple Linear Regression - Method 3 Prediction

Lecture 40 Multiple Linear Regression

Lecture 41 Multiple Linear Regression - Dummy Variables

Lecture 42 Multiple Linear Regression - Missing Values

Lecture 43 Multiple Linear Regression - Add Gender Dummy Variable

Lecture 44 Multiple Linear Regression - Add Smoker Dummy Variable

Lecture 45 Multiple Linear Regression - Add Region Dummy Variable

Lecture 46 Multiple Linear Regression - Excel Base Data

Lecture 47 Multiple Linear Regression - Applying MLR

Lecture 48 Multiple Linear Regression - Understanding the Output (Overall Fit)

Lecture 49 Multiple Linear Regression - Understanding the Output (Model Compraison)

Lecture 50 Multiple Linear Regression - Understanding the Output (ANOVA)

Lecture 51 Multiple Linear Regression - Understanding the Output (Intercept)

Lecture 52 Multiple Linear Regression - Understanding the Output (Multicollinearity)

Lecture 53 Multiple Linear Regression - Understanding the Output (p-value)

Lecture 54 Multiple Linear Regression - Understanding the Output (Predicted Value)

Lecture 55 Multiple Linear Regression - Iteration #2

Lecture 56 Age - Descriptive Statistics

Lecture 57 All Variables - Descriptive Statistics

Section 7: Weighted Linear Regression

Lecture 58 Weighted Linear Regression - Download the Data

Lecture 59 Homoscedasticity vs Heteroskedasticity

Lecture 60 Homoscedasticity Data Example

Lecture 61 Heteroskedasticity

Lecture 62 Heteroskedasticity Test: Breusch-Pagan & White Test

Lecture 63 Weighted Linear Regression - The Logic

Lecture 64 Weighted Linear Regression - Assigning Weights

Lecture 65 Weighted Linear Regression - Checking Heteroskedasticity

Lecture 66 Weighted Linear Regression - Method #1

Lecture 67 Weighted Linear Regression - Method #1 Prediction

Lecture 68 Weighted Linear Regression - Method #2

Section 8: Logistic Regression

Lecture 69 Logistic Regression - Download the Data

Lecture 70 Logistic Regression Intuition - My Yoga Story

Lecture 71 Logistic Regression Intuition - My Yoga Story Part 2

Lecture 72 Logistic Regression - The Confusion Matrix Part 1

Lecture 73 Logistic Regression - The Confusion Matrix Part 2

Lecture 74 Logistic Regression - The Confusion Matrix Part 3

Lecture 75 Logistic Regression - Prostate Cancer Data

Lecture 76 Logistic Regression - Descriptive Statistics

Lecture 77 Logistic Regression - Missing Values

Lecture 78 Logistic Regression - Pivot Table for Mean

Lecture 79 Logistic Regression - Imputing the data

Lecture 80 Logistic Regression - Applying the model

Lecture 81 Logistic Regression - ROC Curve

Lecture 82 Logistic Regression - OP Confusion Matrix

Lecture 83 Logistic Regression - OP Parameters

Lecture 84 Logistic Regression - Other Output

Lecture 85 Logistic Regression Predicted OP Part 1

Lecture 86 Logistic Regression Predicted OP Part 2

Lecture 87 Logistic Regression Predicted OP Part 3

Lecture 88 Logistic Regression Predicted OP Part 4

Lecture 89 Logistic Regression - Iteration 2

Section 9: Multinomial Logistic Regression

Lecture 90 Multinomial Logistic Regression - Download the Data

Lecture 91 Nominal vs Ordinal Data

Lecture 92 Multinomial Logistic Regression - Asthma Data

Lecture 93 Multinomial Logistic Regression - Data Cleaning Part 1

Lecture 94 Multinomial Logistic Regression - Data Cleaning Part 2

Lecture 95 Multinomial Logistic Regression - Data Cleaning Part 3

Lecture 96 Multinomial Logistic Regression - Data Cleaning Part 4

Lecture 97 Multinomial Logistic Regression - Applying using Real Statistics

Lecture 98 Multinomial Logistic Regression - OP Summary

Lecture 99 Multinomial Logistic Regression - Predicted Value

Lecture 100 Multinomial Logistic Regression - Primary Tumor Data (2nd Example)

Lecture 101 Multinomial Logistic Regression - Tumor Data Cleaning Part 1

Lecture 102 Multinomial Logistic Regression - Tumor Data Cleaning Part 2

Lecture 103 Multinomial Logistic Regression - Tumor Data Cleaning Part 3

Lecture 104 Multinomial Logistic Regression - Tumor Data Applying MLR

Lecture 105 Multinomial Logistic Regression - Tumor Data Predicting Values

Section 10: Ordinal Regression

Lecture 106 Ordinal Regression - Download the Data

Lecture 107 Ordinal Regression - Obesity Data

Lecture 108 Ordinal Regression - Cleaning Data Part 1

Lecture 109 Ordinal Regression - Cleaning Data Part 2

Lecture 110 Ordinal Regression - Execution Part 1

Lecture 111 Ordinal Regression - Why the Error?

Lecture 112 Ordinal Regression - Calculate BMI

Lecture 113 Ordinal Regression - Execution Part 2

Lecture 114 Ordinal Regression - Predicted Values

Lecture 115 Ordinal Regression - Confusion Matrix

Lecture 116 Ordinal Regression - Physical Activity Data

Lecture 117 Ordinal Regression - Execution Part 3

Lecture 118 Ordinal Regression - Predicted Values Part 2

Lecture 119 Ordinal Regression - Confusion Matrix Part 2

Section 11: KNN: k-nearest neighbors (Classification/Supervised Learning)

Lecture 120 KNN - Download the Data

Lecture 121 KNN - Sorting Hat Intution Part 1

Lecture 122 KNN - Sorting Hat Intuition Part 2

Lecture 123 KNN - Prostate Cancer Data

Lecture 124 KNN - Prostate Cancer Scatter Plot

Lecture 125 KNN - Prostate Cancer Applying the Logic - Part 1

Lecture 126 KNN - Prostate Cancer Applying the Logic - Part 2

Lecture 127 KNN - Breast Cancer Data

Lecture 128 KNN - Breast Cancer Data - Applying the Logic Part 1

Lecture 129 KNN - Breast Cancer Data - Applying the Logic Part 2

Lecture 130 KNN - Breast Cancer Data - Applying the Logic Part 3

Lecture 131 KNN - Breast Cancer Data - Simple Excel Formula

Section 12: K-Means (Clustering/Unsupervised Learning)

Lecture 132 K-Means - Download the Data

Lecture 133 First Impressions - The Intuition

Lecture 134 KMeans - Logic of Centroid

Lecture 135 KMeans - Elbow Method

Lecture 136 KMeans - Iris Dataset

Lecture 137 KMeans - Iris Data - Part 1

Lecture 138 KMeans - Iris Data - Part 2

Lecture 139 KMeans - Iris Data - Part 3

Lecture 140 KMeans - Maternal Health Data

Lecture 141 KMeans - Maternal Health Analysis - Part 1

Lecture 142 KMeans - Maternal Health Analysis - Part 2

Lecture 143 KMeans - Note of Caution

Lecture 144 Quick Ordinal Regression on Maternal Health Data

Section 13: Basic Time Series Forecasting

Lecture 145 Time Series - Download the Data

Lecture 146 Twister - The Intuition

Lecture 147 Classic Time Series Components

Lecture 148 TFS Example 01 - Using Seasonality

Lecture 149 TFS Example 02 - Using Seasonality + Trend Part 01

Lecture 150 TFS Example 02 - Using Seasonality + Trend Part 02

Lecture 151 TFS Example 02 - Using Seasonality + Trend Part 03

Lecture 152 TFS Example 03 - DE-Seasonalize Part 01

Lecture 153 TFS Example 03 - DE-Seasonalize Part 02

Lecture 154 TFS Example 03 - DE-Seasonalize Part 03

Section 14: ARIMA - Time Series Forecasting

Lecture 155 ARIMA - Download the Data

Lecture 156 ARIMA Intuition - Part 1: Summer of 92

Lecture 157 ARIMA Intuition - Part 1A: Summer of 92 (Article)

Lecture 158 ARIMA Basics - Part 2

Lecture 159 Simple Moving Average Basic Example

Lecture 160 Simple Moving Average Basic Example

Lecture 161 Simple Moving Average Covid Part 1

Lecture 162 Simple Moving Average Covid Part 2

Lecture 163 Simple Moving Average Covid Part 3

Lecture 164 ARIMA Understanding ACF PACF

Lecture 165 ARIMA Understanding ACF PACF (External Article)

Lecture 166 ARIMA Pharma Sales Calculate ACF & PACF

Lecture 167 ARIMA Pharma Sales Calculate Stationarity

Lecture 168 ARIMA Pharma Sales Apply ARIMA

Lecture 169 ARIMA Pharma Sales 2nd Example

Lecture 170 ARIMA Enrollment Data Part - 1

Lecture 171 ARIMA Enrollment Data Part - 2

Lecture 172 ARIMA Enrollment Data Part - 3 Simple Moving Average

Lecture 173 ARIMA Enrollment Data Part - 4 ARIMA (ACF PACF)

Lecture 174 ARIMA Enrollment Data Part - 5 Differencing

Lecture 175 ARIMA Enrollment Data Part - 6 Applying ARIMA

Section 15: Bonus Section

Lecture 176 Bonus Session - About Me

Beginners curious about create basic Analytics using Healthcare Data,Health IT Professionals,Healthcare/Hospital Management Professionals,Professionals from a Non-Technical background who want to understand basics of Analytics