Health Care Analytics Course

• SQL Fundamentals
• Various types of databases
• Introduction to Structured Query Language
• Distinction between client server and file server databases
• Understanding SQL Server Management Studio
• SQL Table basics
• Data types and functions
• Transaction-SQL
• Authentication for Windows
• Data control language
• The identification of the keywords in T-SQL, such as Drop Table
• Database Normalization
• Entity Relationship Model
• SQL Operators
• Working with SQL
• Join
• Tables
• Variables
• Advanced concepts of SQL tables
• SQL functions
• Operators & queries
• Table creation
• Data retrieval from tables
• Combining rows from tables using inner, outer, cross, and self joins
• Deploying operators such as ‘intersect,’ ‘except,’ ‘union,’
• Temporary table creation
• Set operator rules
• Table variables•
• Deep Dive into SQL Functions
• Working with Subqueries
• SQL Views, Functions, and Stored Procedures
• Deep Dive into User-defined Functions
• SQL Optimization and Performance
• SQL Server Management Studio
• Using pivot in MS Excel and MS SQL Server
• Differentiating between Char, Varchar, and NVarchar
• XL path, indexes and their creation
• Records grouping, advantages, searching, sorting, modifying data
• Clustered indexes creation
• Use of indexes to cover queries
• Common table expressions
• Index guidelines
• Managing Data with Transact-SQL
• Querying Data with Advanced Transact-SQL Components
• Programming Databases Using Transact-SQL
• Creating database programmability objects by using T-SQL
• Implementing error handling and transactions
• Implementing transaction control in conjunction with error handling in stored procedures
• Implementing data types and NULL
• Designing and Implementing Database Objects
• Implementing Programmability Objects
• Managing Database Concurrency
• Optimizing Database Objects
• Advanced SQL
• Correlated Subquery, Grouping Sets, Rollup, Cube
• Implementing Correlated Subqueries
• Using EXISTS with a Correlated subquery
• Using Union Query
• Using Grouping Set Query
• Using Rollup
• Using CUBE to generate four grouping sets
• Perform a partial CUBE
• •  

Basic Math

• Linear Algebra
• Probability
• Calculus
• Develop a comprehensive understanding of coordinate geometry and linear algebra.
• Build a strong foundation in calculus, including limits, derivatives, and integrals.

• What is Keras?
• How to Install Keras?
• Why to Use Keras?
• Different Models of Keras
• Preprocessing Methods
• What are the Layers in Keras?

• Descriptive Statistics
  • Sampling Techniques
  • Measure of Central Tendency
  • Measure of Dispersion
  • Skewness and Kurtosis
  • Random Variables
  • Bassells Correction Method
  • Percentiles and Quartiles
  • Five Number Summary
  • Gaussian Distribution
  • Lognormal Distribution
  • Binomial Distribution
  • Bernoulli Distribution
• Inferential Statistics
  • Standard Normal Distribution 
  • ZTest
  • TTest
  • ChiSquare Test
  • ANOVA / FTest
  • Introduction to Hypothesis Testing
  • Null Hypothesis
  • Alternet Hypothesis
• Probability Theory
  • What is Probability?
  • Events and Types of Events
  • Sets in Probability
  • Probability Basics using Python
  • Conditional Probability
  • Expectation and Variance

• Python Programming
• Python for Analytics
• R Programming
• R for Anlalytics

• Introduction
• Roles
• Snowflake Pricing
• Resource Monitor – Track Compute Consumption
• Micro-Partitioning in Snowflake
• Clustering in Snowflake
• Query History & Caching
• Load Data from AWS – CSV / JASON / PARQUET & Stages
• Snow pipe – Continuous Data Ingestion Service
• Different Type of Tables
• Time Travel – Work with History of Objects & Fail Safe
• Task in Snowflake – Scheduling Service
• Snowflake Stream – Change Data Capture (CDC)
• Zero-Copy Cloning
• Snowflake SQL – DDL
• Snowflake SQL – DML & DQL
• Snowflake SQL – Sub Queries & Case Statement
• Snowflake SQL – SET Operators
• Snowflake SQL – Working with ROW NUMBER
• Snowflake SQL – Functions & Transactions
• Procedures
• User defined function
• Types of Views

• Intro to Qlik View
• Installation of Qlik view
• Data Modelling in Qlik View
• Circular reference
• Link Tables to your model
• Joins in Qlik view
• ETL in Qlik View
• Handling Null Values
• Visualizations in Qlik View
• Pivot Table in Qlik View
• KPI Development in Qlik View

• Introduction to Alteryx
• Download and Install Alteryx
• User Interface of Alteryx
• Get Data from Excel
• Get Data from CSV
• Append All CSV files
• Browse Tool
• Output Tool – Update Existing Data
• Directory Tool
• Directory Tool – Specific Files
• Text Input Tool
• Date and Time Tool
• Auto Field Tool
• Data Cleansing Tool
• Filter Tool (Text Example)
• Filter Tool (Number Example)
• Filter Tool ( Date Example)
• Formula Tool ( Basic Example )
• Formula Tool – (Multiple Examples)
• Generate Rows Tool
• Imputation Tool
• Multi-Field Binning Tool
• Multi-Field Formula
• Multi Row Formula
• Random % Sample Tool
• Sample Tool
• Record Id Tool
• Select Tool
• Sort
• Create Sample Tool
• Tile Tool
• Unique Tool
• Append Fields Tool
• Find And Replace Tool
• Fuzzy Match Tool
• Join Tool
• Join Multiple Tool
• Union Tool
• Regex Tool
• Text To Columns
• Cross Tab Tool
• Transpose Tool
• Running Total Tool
• Summarize Tool
• Table Tool
• Interactive Chart Tool
• Join Table And Chart
• Add Annotation
• Report Text Tool
• Report Header Tool
• Report Footer Tool
• Report Layout Tool
• Comment Tool
• Explorer Tool
• Container Tool

• Cloud AWS
• Cloud Azure

• Introduction to GIT
• Version Control System
• Introduction and Installation of Git
• History of Git
• Git Features
• Introduction to GitHub
• Git Repository
• Git Features
• Bare Repositories in Git
• Git Ignore
• Readme.md File
• GitHub Readme File
• GitHub Labels
• Difference between CVS and GitHub
• Git – SubGit
• Git Environment Setup
• Using Git on CLI
• How to Setup a Repository
• Working with Git Repositories
• Using GitHub with SSH
• Working on Git with GUI
• Difference Between Git and GitHub
• Working on Git Bash
• States of a File in Git Working Directory
• Use of Submodules in GitHub
• How to Write Good Commit Messages on GitHub?
• Deleting a Local GitHub Repository
• Git Workflow Etiquettes
• Git Packfiles
• Git Garbage Collection
• Git Flow vs GitHub Flow
• Git – Difference Between HEAD, Working Tree and Index
• Git Ignore

• Introduction of Scum and Agile
• How to differentiate between Waterfall and Agile
• Agile Framework
• Agile Manifesto
• Agile Principles
• Top Agile Methodologies
• Scrum terminology and roles
• Managing tasks and events within a Sprint
• Scrum Framework
• Introduction to Scrum Framework
• Three pillars of Scrum Framework
• Values of Scrum
• When to use Scrum
• Cross-Functional, Self-Organizing Teams
• Scrum Team philosophy
• Developers
• Product Owner
• Scrum Master
• Scrum Events and Planning
• Scrum Events
• Understanding Sprint
• Sprint Planning
• Daily Scrum Meeting
• Sprint Review Meeting
• Sprint Retrospective
• Scrum Planning with backlog
• Product Backlog
• Refining Backlog
• Backlog items Estimation
• Planning Poker
• T-Shirt Sizing
• Defining Product Goals
• User Stories and INVEST
• Sprint Backlog
• Definition of Done
• Product Increment
• Definition of Done

• Objective and Scope
  • Objective: To improve the effectiveness of marketing campaigns by analyzing customer behavior data.
  • Scope: Focus on a retail company’s email marketing campaigns over the past year.
• Background Information
  • Business Context: A retail company wants to increase sales through targeted email marketing campaigns.
  • Data Sources: Customer transaction data, email campaign engagement metrics (open rates, click-through rates), demographic information.
• Data Collection and Preparation
  • Data Collection: Gather transactional data from the company’s CRM system and email campaign data from marketing tools.
  • Data Cleaning: Handle missing values, remove duplicates, and ensure data consistency.
  • Data Transformation: Merge datasets, perform segmentation based on customer demographics and purchase history.
• Data Analysis and Exploration
  • Descriptive Analytics: Analyze email open rates, click-through rates, and conversion rates over time.
  • Exploratory Data Analysis (EDA): Identify trends in customer behavior and preferences.
  • Customer Segmentation: Use clustering algorithms to group customers based on their purchasing patterns and demographics.
• Modeling and Analysis
  • Predictive Analytics: Build a predictive model to forecast customer response to different types of email campaigns.
  • Campaign Optimization: Use A/B testing to compare the effectiveness of different campaign strategies.
  • ROI Calculation: Estimate the return on investment (ROI) for each campaign based on sales uplift.
• Interpretation of Results
  • Business Insights: Discover that customers in certain demographics respond better to personalized product recommendations.
  • Impact Analysis: Show that targeted campaigns led to a 15% increase in sales compared to generic promotions.
• Case Study Structure
  • Introduction: Overview of the retail company’s marketing challenges and objectives.
  • Methodology: Detailed explanation of data collection, analysis techniques, and modeling approach.
  • Results: Presentation of findings including visualizations (charts, graphs) and statistical analysis.
  • Discussion: Interpretation of results, implications for marketing strategy, and actionable recommendations.
  • Conclusion: Summary of key findings and the impact of data-driven insights on business outcomes.
• Review and Validation
  • Peer Review: Obtain feedback from marketing experts and data analysts within the company.
  • Validation: Validate findings by comparing with historical performance and conducting sensitivity analyses.
• Presentation and Documentation
  • Presentation: Prepare a compelling slide deck with key findings, visualizations, and actionable insights.
  • Documentation: Document methodologies, assumptions, and data sources for transparency and reproducibility.
• Dissemination
  • Publishing: Share the case study internally with stakeholders and externally through industry conferences or publications.
  • Feedback: Gather feedback from stakeholders to refine strategies and improve future campaigns.