Health Care Analytics Course in Vadodara

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The Healthcare Data Analytics course is designed to equip professionals with the skills and knowledge necessary to analyze and interpret healthcare data effectively. This specialized program covers essential topics such as healthcare data management, electronic health records (EHR) analysis, clinical data analysis, and healthcare outcomes assessment using advanced analytical tools and techniques. Students will learn to utilize programming languages like Python, along with healthcare-specific tools and software such as SAS and SQL, to extract, clean, and analyze healthcare data.

|| Health Care Data Analytics Course in Vadodara 

The Health Care Analytics certification course in Vadodara offers an in-depth understanding of essential analytics methods and tools used in the healthcare sector, providing participants with a solid foundation for careers in medical data analysis, informatics, and patient outcome improvement. Through hands-on projects and case studies, this Health Care Analytics training in Vadodara emphasizes practical applications, enabling participants to tackle real-world challenges in the healthcare industry. The curriculum covers data visualization, statistical analysis, predictive modeling, and healthcare data ethics, preparing individuals to effectively use analytics for evidence-based decision-making in clinical and administrative settings.


By enrolling in Health Care Analytics classes in Vadodara, participants gain the expertise needed to improve patient care quality, optimize healthcare delivery processes, and identify trends in public health. The course also focuses on healthcare policy and compliance, ensuring that data usage adheres to privacy regulations and ethical standards. Graduates of this Health Care Analytics course in Vadodara are well-equipped to pursue rewarding careers in healthcare analytics, administration, consulting, and clinical research, contributing to advancements in healthcare outcomes and public health initiatives.


This comprehensive Health Care Analytics certification course in Vadodara offers participants the opportunity to master advanced techniques such as clinical data management, big data in healthcare, and healthcare risk management. The program is led by experienced faculty specializing in analytics and healthcare administration, ensuring that students receive high-quality education. By completing this Health Care Analytics training in Vadodara, participants will be prepared to address critical challenges in healthcare delivery, enhance operational efficiency, and drive innovation in the healthcare industry.

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|| What will I learn?

  • Understand the role and importance of healthcare analytics in healthcare delivery and management.
  • The course will foster communication and collaboration skills, enabling participants to effectively communicate healthcare insights and findings to stakeholders, collaborate with healthcare teams, and influence decision-making processes.
  • Students will be introduced to prescriptive analytics concepts and techniques, enabling them to optimize healthcare resource allocation, design interventions to improve patient outcomes, and make data-driven decisions to achieve healthcare objectives.
  • Explore tools and technologies used in healthcare analytics, including electronic health record (EHR) systems and data visualization tools.
  • Learning data management techniques for collecting, cleaning, and storing healthcare data securely while ensuring compliance with regulatory standards like HIPAA.
  • Mastering statistical analysis methods to interpret healthcare data, including descriptive statistics, inferential statistics, and hypothesis testing.
  • Designing interactive dashboards to present data trends, patient outcomes, and healthcare metrics to various stakeholders.
  • Creating impactful visualizations to communicate complex data insights effectively using tools like Tableau, Power BI, or specialized healthcare analytics software.

|| What will I learn?

  • Understand the role and importance of healthcare analytics in healthcare delivery and management.
  • The course will foster communication and collaboration skills, enabling participants to effectively communicate healthcare insights and findings to stakeholders, collaborate with healthcare teams, and influence decision-making processes.
  • Students will be introduced to prescriptive analytics concepts and techniques, enabling them to optimize healthcare resource allocation, design interventions to improve patient outcomes, and make data-driven decisions to achieve healthcare objectives.
  • Explore tools and technologies used in healthcare analytics, including electronic health record (EHR) systems and data visualization tools.
  • Learning data management techniques for collecting, cleaning, and storing healthcare data securely while ensuring compliance with regulatory standards like HIPAA.
  • Mastering statistical analysis methods to interpret healthcare data, including descriptive statistics, inferential statistics, and hypothesis testing.
  • Designing interactive dashboards to present data trends, patient outcomes, and healthcare metrics to various stakeholders.
  • Creating impactful visualizations to communicate complex data insights effectively using tools like Tableau, Power BI, or specialized healthcare analytics software.

|| Requirements

  • Familiarity with spreadsheet software (e.g., Microsoft Excel) is recommended
  • Basic understanding of healthcare concepts and terminology.

|| Requirements

  • Familiarity with spreadsheet software (e.g., Microsoft Excel) is recommended
  • Basic understanding of healthcare concepts and terminology.

|| Key Features of a Comprehensive Healthcare Analytics Training Program

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    A comprehensive healthcare analytics course equips students with the skills and knowledge to leverage data for improving patient outcomes, operational efficiency, and cost management in healthcare settings. The course covers fundamental concepts such as the structure and functions of healthcare systems, the types and sources of healthcare data, and essential data management practices including data governance, quality, and security. Students learn analytical techniques ranging from descriptive and inferential statistics to advanced predictive and prescriptive analytics. Practical modules include health informatics, data visualization using tools like Tableau and Power BI, and the application of machine learning and AI in healthcare. Ethical and legal considerations are emphasized to ensure compliance with regulations such as HIPAA and to address issues of bias and fairness in data analysis. The course also involves hands-on projects and case studies, allowing students to apply theoretical knowledge to real-world problems, such as predicting patient readmissions, optimizing emergency department operations, managing chronic diseases, and improving revenue cycle management. By the end of the course, students are prepared to use data-driven insights to enhance healthcare delivery and decision-making processes.


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    • Microsoft Excel fundamentals.
    • Entering and editing texts and formulae.
    • Working with basic Excel functions.
    • Modifying an Excel worksheet.
    • Formatting data in an excel worksheet.
    • Inserting images and shapes into an Excel worksheet.
    • Creating Basic charts in Excel.
    • Printing an Excel worksheet.
    • Working with an Excel template.
    • Working with an excel list.
    • Excel list function.
    • Excel data validation.
    • Importing and exporting data.
    • Excel pivot tables.
    • Working with excels
    • Pivot tools.
    • Working with large sets of Excel data.
    • Conditional function.


    • Lookup functions.
    • Text based functions
    • Auditing and Excel worksheet.
    • Protecting Excel worksheets and workbooks.
    • Mastering Excel "What if?" Tools?
    • Automating Repetitive Tasks in Excel with Macros.
    • Macro Recorder Tool.
    • Excel VBA Concepts.
    • Ranges and Worksheet in VBA 
    • IF condition 
    • Loops in VBA 
    • Debugging in VBA 
    • Messaging in VBA
    • Preparing and Cleaning Up Data with VBA.
    • VBA to Automate Excel Formulas.
    • Preparing Weekly Report.
    • Working with Excel VBA User Forms.
    • Importing Data from Text Files.

    • 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.

    • 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 Basic Building
    • Python Keywords and identifiers
    • Comments, indentation, statements
    • Variables and data types in Python
    • Standard Input and Output
    • Operators
    • Control flow: if else elif
    • Control flow: while loop
    • Control flow: for loop
    • Control flow: break & continue


    • Python Data Structures
    • Strings
    • Lists, Lists comprehension
    • Tuples
    • Sets
    • Dictionary, Dictionary Comprehension


    • Python Functions
    • Python Builtin Functions.
    • Python Userdefined Functions.
    • Python Recursion Functions.
    • Python Lambda Functions.
    • Python Exception Handling, 
    • Logging And Debugging


    • Exception Handling 
    • Custom Exception Handling
    • Logging With Python
    • Debugging With Python


    • Python OOPS
    • Python Objects And Classes
    • Python Constructors
    • Python Inheritance
    • Abstraction In Python
    • Polymorphism in Python
    • Encapsulation in Python


    • File Handling
    • Create 
    • Read
    • Write
    • Append

    • Introduction to NumPy
    • NumPy Array
    • Creating NumPy Array
    • Array Attributes, 
    • Array Methods
    • Array Indexing, 
    • Slicing Arrays
    • Array Operation
    • Iteration through Arrays


    • Introduction to Pandas
    • Pandas Series
    • Creating Pandas Series
    • Accessing Series Elements
    • Filtering a Series
    • Arithmetic Operations
    • Series Ranking and Sorting
    • Checking Null Values
    • Concatenate a Series


    • Data Frame Manipulation
    • Pandas Dataframe 
    • Introduction Dataframe Creation
    • Reading Data from Various Files
    • Understanding Data
    • Accessing Data Frame Elements using Indexing
    • Dataframe Sorting
    • Ranking in Dataframe
    • Dataframe Concatenation
    • Dataframe Joins, 
    • Dataframe Merge
    • Reshaping Dataframe
    • Pivot Tables, 
    • Cross Tables
    • Dataframe Operations


    • Checking Duplicates
    • Dropping Rows and Columns
    • Replacing Values
    • Grouping Dataframe
    • Missing Value Analysis & Treatment
    • Visualization using Matplotlib
    • Plot Styles & Settings
    • Line Plot, 
    • Multiline Plot
    • Matplotlib Subplots
    • Histogram, Boxplot
    • Pie Chart ,Scatter Plot
    • Visualization using Seaborn
    • Strip Plot ,Distribution Plot
    • Joint Plot, 
    • Violin Plot, 
    • Swarm Plot
    • Pair Plot,
    • Count Plot
    • Heatmap
    • Visualization using Plotly
    • Boxplot
    • Bubble Chart
    • Violin Plot
    • 3D Visualization


    • EDA and Feature Engineering
    • Introduction of EDA
    • Dataframe Analysis using Groupby
    • Advanced Data Explorations

    • Introduction to SAS:
    • Overview of SAS software and its components
    • Understanding the SAS programming environment (SAS Studio, SAS Enterprise Guide)
    • Basics of SAS syntax and structure
    • Data Step Programming:
    • Reading and importing data into SAS datasets
    • Working with SAS datasets (creating, modifying, appending, merging)
    • Data manipulation techniques (sorting, subsetting, conditional statements)
    • SAS Functions and Formats:
    • Overview of SAS functions (numeric, character, date and time)
    • Working with SAS formats (DATE, DOLLAR, PERCENT, etc.)
    • Customizing formats and informats
    • Data Cleaning and Validation:
    • Identifying and handling missing values
    • Data validation techniques (range checks, consistency checks)
    • Handling errors and invalid data
    • Basic Statistical Analysis:
    • Descriptive statistics (mean, median, mode, standard deviation, etc.)
    • Frequency analysis (PROC FREQ)
    • Summary statistics (PROC MEANS)
    • SAS Procedures (PROCs):
    • Introduction to SAS procedures and their use
    • Common PROCs for data analysis (PROC PRINT, PROC SORT, PROC SUMMARY)
    • Generating reports with PROC REPORT and PROC TABULATE
    • Introduction to SAS Macro Language:
    • Overview of SAS macro language and macro variables
    • Creating and using macros
    • Automating repetitive tasks with macros
    • Data Visualization with SAS:
    • Creating basic graphs and charts (PROC GPLOT, PROC GCHART)
    • Customizing graphs (titles, labels, legends)
    • Introduction to ODS (Output Delivery System) for generating reports
    • Advanced Data Manipulation Techniques:
    • Combining datasets (SET, MERGE)
    • Working with character data (functions, concatenation)
    • Advanced data manipulation techniques (arrays, do-loops)
    • Debugging and Error Handling:
    • Identifying and fixing common programming errors
    • Using SAS log and output to debug programs
    • Error handling techniques
    • Introduction to SQL in SAS:
    • Basics of SQL (SELECT, FROM, WHERE)
    • Using SQL to manipulate and query SAS datasets
    • Combining SAS and SQL for data analysis
    • Real-world Case Studies and Projects:
    • Applying SAS programming skills to solve real-world data analysis problems
    • Hands-on projects to reinforce learning and practical skills

    • Advanced Data Manipulation Techniques:
    • Advanced conditional processing (IF-THEN/ELSE statements, WHERE statements)
    • Subsetting data with advanced techniques (DO loops, arrays, SQL)
    • Advanced sorting and merging techniques (complex sorting, one-to-many merging)
    • SAS Macro Language Mastery:
    • Macro variables and macro functions
    • Advanced macro programming techniques (macro loops, macro arrays)
    • Macro debugging and error handling
    • Advanced Statistical Analysis:
    • Advanced statistical procedures (PROC UNIVARIATE, PROC REG, PROC LOGISTIC)
    • Multivariate analysis techniques (factor analysis, cluster analysis)
    • Time series analysis (PROC TIMESERIES, PROC ARIMA)
    • Efficiency and Performance Optimization:
    • Improving program efficiency (techniques for reducing memory usage and CPU time)
    • Performance optimization tips and tricks (indexing, compression)
    • Performance monitoring and benchmarking
    • Advanced Reporting and Output Delivery:
    • Customizing and enhancing reports (advanced formatting, conditional highlighting)
    • Creating interactive reports (ODS HTML, ODS PDF, ODS EXCEL)
    • Output delivery customization with ODS destinations
    • Advanced SQL in SAS:
    • Advanced SQL techniques (subqueries, joins, set operators)
    • Performance optimization in SQL queries
    • Combining SAS and SQL for advanced data manipulation and analysis
    • Data Quality and Validation:
    • Data validation techniques for advanced data structures
    • Handling complex data quality issues (data profiling, error detection)
    • Advanced data cleansing and transformation techniques
    • SAS Integration with External Systems:
    • Integrating SAS with databases (PROC SQL, LIBNAME)
    • Accessing and processing external data sources (Excel, CSV, XML)
    • Using SAS with other programming languages (Python, R)
    • Advanced Topics in SAS Programming:
    • Handling large datasets (SAS data set options, compression techniques)
    • Parallel processing with SAS Grid Computing
    • Integrating SAS with cloud platforms (SAS Viya)
    • Real-world Case Studies and Projects:
    • Applying advanced SAS programming skills to solve complex real-world problems
    • Hands-on projects and exercises to reinforce learning and practical skills

    • Introduction to Data Analytics and SAS:
    • Overview of data analytics concepts and methodologies
    • Introduction to SAS software and its capabilities for data analysis
    • Understanding the SAS programming environment (SAS Studio, SAS Enterprise Guide)
    • Data Preparation and Management:
    • Data import/export techniques (reading and writing data from/to external sources)
    • Data cleaning and preprocessing (handling missing values, outliers, and inconsistencies)
    • Data transformation techniques (variable creation, recoding, and restructuring)
    • Exploratory Data Analysis (EDA):
    • Descriptive statistics (summary statistics, frequency distributions, measures of central tendency and dispersion)
    • Data visualization techniques (scatter plots, histograms, box plots, heatmaps)
    • Exploring relationships between variables (correlation analysis, cross-tabulations)
    • Statistical Analysis with SAS:
    • Hypothesis testing (t-tests, ANOVA, chi-square tests)
    • Regression analysis (simple linear regression, multiple regression)
    • Non-parametric methods (Wilcoxon signed-rank test, Kruskal-Wallis test)


    • Predictive Modeling:
    • Introduction to predictive analytics concepts
    • Building predictive models using SAS (logistic regression, decision trees, neural networks)
    • Model assessment and validation techniques (ROC curves, confusion matrices, cross-validation)
    • Time Series Analysis:
    • Understanding time series data and its characteristics
    • Time series forecasting techniques (moving averages, exponential smoothing, ARIMA models)
    • Analyzing seasonality and trends in time series data
    • Advanced Analytics Techniques:
    • Cluster analysis (k-means clustering, hierarchical clustering)
    • Association rule mining (market basket analysis)
    • Text mining and sentiment analysis with SAS Text Miner
    • Big Data Analytics with SAS:
    • Introduction to SAS Viya and SAS Cloud Analytic Services (CAS)
    • Working with large-scale datasets using SAS Viya
    • Distributed computing and parallel processing with SAS Viya
    • Data Visualization and Reporting:
    • Creating interactive dashboards and reports with SAS Visual Analytics
    • Customizing visualizations and reports for effective communication of insights
    • Best practices for data visualization and storytelling with data
    • Case Studies and Practical Applications:
    • Applying SAS analytics skills to real-world datasets and scenarios
    • Hands-on projects and exercises to reinforce learning and practical skills
    • Ethical and Legal Considerations:
    • Ethical issues in data analytics (privacy, bias, transparency)
    • Legal regulations and compliance (GDPR, CCPA, HIPAA)

    • Introduction to Clinical Trials:
    • Overview of the clinical trial process (phases, design, regulations)
    • Role of SAS in clinical research and pharmaceutical industries
    • Understanding the importance of data integrity and regulatory compliance
    • Data Management in Clinical Trials:
    • Data collection methods (Case Report Forms, electronic data capture)
    • Data standards (CDISC standards - SDTM, ADaM)
    • Data cleaning and validation techniques specific to clinical data
    • SAS Programming for Clinical Trials:
    • Introduction to SAS programming in the context of clinical trials
    • SAS datasets specific to clinical trials (SDTM, ADaM)
    • Creating and managing clinical trial datasets using SAS
    • Clinical Data Analysis:
    • Descriptive statistics for clinical data (summary statistics, frequency tables)
    • Statistical analysis for clinical trials (t-tests, ANOVA, survival analysis)
    • Handling time-to-event data (Kaplan-Meier curves, Cox proportional hazards model)
    • Safety and Efficacy Analysis:
    • Adverse event analysis (AE/SAE reporting)
    • Efficacy endpoint analysis (response rates, time-to-event endpoints)
    • Safety and efficacy tables, listings, and figures
    • Clinical Trial Reporting:
    • Generating clinical study reports (CSRs) using SAS
    • Creation of analysis datasets and outputs for regulatory submissions
    • Automation of reporting processes using SAS macros and procedures
    • Advanced Topics in Clinical Data Analysis:
    • Advanced statistical techniques for clinical trials (mixed models, non-parametric methods)
    • Handling missing data in clinical trials
    • Meta-analysis of clinical trial data
    • Regulatory Compliance and Good Clinical Practice (GCP):
    • Understanding regulatory requirements (FDA, EMA, ICH guidelines)
    • Ensuring compliance with GCP principles
    • Documentation and audit trails in clinical trials
    • CDISC Standards Implementation:
    • Overview of CDISC standards (SDTM, ADaM, CDASH)
    • Converting raw data to SDTM and ADaM datasets using SAS
    • Validation and quality control of CDISC datasets
    • Case Studies and Hands-on Projects:
    • Applying SAS skills to analyze and report on real-world clinical trial data
    • Hands-on exercises and projects to reinforce learning and practical skills
    • Industry Trends and Emerging Technologies:
    • Emerging trends in clinical research and data analysis
    • Integration of SAS with other technologies (R, Python, cloud computing)

    Power BI and Tableau are both leading business intelligence (BI) tools used extensively in data analytics, each offering distinct features and capabilities tailored to different user needs.


    Power BI, developed by Microsoft, is known for its integration with the Microsoft ecosystem, particularly Excel and Azure services. It excels in data connectivity and integration, allowing users to easily connect to various data sources, clean and transform data using Power Query, and create interactive visualizations and reports. Power BI's strength lies in its user-friendly interface and seamless integration with other Microsoft products, making it a preferred choice for organizations already invested in Microsoft technologies.


    Tableau, on the other hand, is celebrated for its powerful data visualization capabilities and ease of use. Tableau enables users to create visually appealing and interactive dashboards with simple drag-and-drop functionality. It supports a wide range of data sources and provides robust analytics features, including advanced statistical analysis, predictive modeling, and geographic mapping. Tableau's intuitive interface and strong emphasis on visual storytelling make it popular among analysts and data professionals who prioritize data visualization and storytelling.

    • Introduction to Power BI Desktop
    • Overview of Power BI
    • Key Features and Benefits
    • Comparison with other BI tools


    • Getting Started with Power BI Desktop
    • Installation and Setup
    • Tour of the Interface
    • Navigating Power BI Ribbon and Panes


    • Connecting to Data Sources
    • Importing Data from Excel
    • Connecting to Databases (SQL Server, MySQL, etc.)
    • Using Web and Text Data Sources


    • Transforming and Cleaning Data
    • Understanding Power Query Editor
    • Data Cleaning and Shaping
    • Merging and Appending Queries


    • Data Modeling in Power BI
    • Introduction to Data Modeling
    • Creating Relationships between Tables
    • Defining Calculated Columns and Measures


    • Creating Visualizations
    • Types of Visualizations (Bar charts, Line charts, Pie charts, etc.)
    • Formatting and Customizing Visuals
    • Using Interactive Filters and Slicers


    • Advanced Visualizations and Techniques
    • Hierarchies and Drill-downs
    • Using Custom Visuals
    • Applying Themes and Templates


    • Working with Maps and Geographic Data
    • Mapping Data Points
    • Using Shapefiles and Custom Maps
    • Geocoding and Location Analytics


    • Creating Dashboards
    • Designing Effective Dashboards
    • Using Tiles and Q&A Features
    • Sharing Dashboards


    • Data Analysis Expressions (DAX)
    • Introduction to DAX
    • Writing DAX Formulas
    • Calculating Totals, Ratios, and Percentages


    • Advanced Data Modeling with DAX
    • Understanding CALCULATE and FILTER Functions
    • Time Intelligence Functions (DATESYTD, SAMEPERIODLASTYEAR, etc.)
    • Implementing Row-level Security


    • Power BI Service Integration
    • Publishing Reports to Power BI Service
    • Setting up Scheduled Data Refresh
    • Sharing and Collaborating on Reports


    • Data Insights and AI Features
    • Introduction to AI Insights in Power BI
    • Using Quick Insights and AI Visuals
    • Integrating Azure AI Services


    • MS Power BI Desktop Exercises
    • Importing and Transforming Data
    • Task: Import sales data from Excel, clean and transform data using Power Query.
    • Outcome: Create a clean dataset ready for analysis.


    • Creating Basic Visualizations
    • Task: Build a bar chart and a line chart to visualize sales trends.
    • Outcome: Understand basic visualization types and formatting options.


    • Creating Advanced Visualizations
    • Task: Create a slicer-based dashboard page with interactive visuals.
    • Outcome: Learn how to use slicers, filters, and drill-down capabilities.


    • Implementing DAX Calculations
    • Task: Write DAX formulas to calculate year-to-date sales and growth percentages.
    • Outcome: Gain proficiency in using DAX for calculations and analysis.


    • Publishing and Sharing Reports
    • Task: Publish a completed sales dashboard to Power BI Service, set up scheduled refresh.
    • Outcome: Understand the workflow of publishing and sharing reports.

    • Introduction to Power BI Server
    • Overview of Power BI Ecosystem
    • Key Features and Capabilities
    • Understanding Power BI Server vs. Power BI Online


    • Installation and Configuration
    • System Requirements and Installation Steps
    • Configuring Power BI Server
    • Integration with Active Directory


    • Power BI Server Architecture
    • Components Overview (Gateway, Data Sources, Reports)
    • Understanding Data Gateways
    • Security and Permissions


    • Data Sources and Connectivity
    • Connecting to Various Data Sources
    • Live vs. DirectQuery vs. Import
    • Refreshing Data


    • Creating Reports and Dashboards
    • Using Power BI Desktop for Report Authoring
    • Building Interactive Visualizations
    • Designing Effective Dashboards


    • Publishing and Managing Reports
    • Publishing Reports from Power BI Desktop to Power BI Server
    • Organizing Content in Workspaces
    • Version Control and Sharing Reports


    • Data Security and Governance
    • Implementing Row-level Security
    • Applying Security Policies
    • Data Encryption and Compliance


    • Advanced Analytics and AI Integration
    • Introduction to AI Features in Power BI
    • Using Custom Visuals and R/Python Scripts
    • Integrating Azure AI Services


    • Performance Optimization
    • Optimizing Query Performance
    • Improving Report Rendering Speed
    • Monitoring and Troubleshooting


    • Customizing and Extending Power BI
    • Creating and Using Custom Themes
    • Developing Custom Visuals
    • Using Power BI APIs for Automation


    • Practical Exercises
    • Exercise 1: Setting up Power BI Server
    • Exercise 2: Creating and Publishing Reports
    • Exercise 3: Implementing Security Measures
    • Exercise 4: Performance Optimization Tasks
    • Exercise 5: Customizing Reports and Dashboards


    • Case Studies and Real-world Applications
    • Industry-specific Use Cases
    • Success Stories and Best Practices


    • MS Power BI Server Exercise
    • Setting up Power BI Server
    • Install Power BI Server on a local machine or VM.
    • Configure basic settings and connect to a sample database.


    • Creating and Publishing Reports
    • Design a sales dashboard using Power BI Desktop.
    • Publish the dashboard to Power BI Server and configure data refresh.


    • Implementing Security Measures
    • Set up row-level security based on user roles.
    • Configure encryption settings and access policies.


    • Performance Optimization Tasks
    • Identify slow-performing reports and optimize queries.
    • Monitor resource usage and apply performance tuning techniques.


    • Customizing Reports and Dashboards
    • Customize the appearance of reports using custom themes.
    • Create a custom visual using Power BI SDK and integrate it into a dashboard.

    • Introduction to Tableau Desktop:
    • Overview of Tableau Desktop and its features.
    • Understanding the Tableau interface and terminology.


    • Connecting to Data:
    • Importing data into Tableau from various sources (Excel, CSV, databases, etc.).
    • Understanding data source connection options and considerations.


    • Basic Visualization:
    • Creating basic visualizations such as bar charts, line charts, scatter plots, and maps.
    • Applying formatting and customization to visualizations.


    • Working with Data:
    • Data organization and structuring.
    • Filtering and sorting data.
    • Grouping and aggregating data.


    • Advanced Visualization Techniques:
    • Creating more complex visualizations such as dual-axis charts, treemaps, and heatmaps.
    • Implementing reference lines, bands, and distributions.


    • Calculations and Expressions:
    • Introduction to Tableau Calculated Fields.
    • Writing basic calculations (e.g., arithmetic calculations, string calculations, date calculations).


    • Dashboard Creation:
    • Building dashboards to combine multiple visualizations into a single view.
    • Implementing interactivity with dashboard actions and filters.


    • Data Blending and Joins:
    • Working with multiple data sources and blending data.
    • Understanding different types of joins and their implications.


    • Advanced Data Analysis:
    • Implementing advanced calculations using Tableau Calculated Fields and Parameters.
    • Utilizing Level of Detail (LOD) expressions for complex analysis.


    • Geospatial Analysis:
    • Mapping geographic data in Tableau.
    • Creating custom geocoding and using spatial files for analysis.


    • Performance Optimization:
    • Optimizing workbook performance for large datasets.
    • Understanding Tableau data extracts and incremental refreshes.


    • Advanced Dashboard Techniques:
    • Designing interactive and responsive dashboards.
    • Incorporating storytelling and guided analytics into dashboards.


    • Tableau Desktop Exercises
    • Data Connection and Basic Visualizations:
    • Import a dataset (e.g., CSV, Excel) into Tableau Desktop.
    • Create a bar chart to visualize sales by product category.
    • Create a line chart to show trends in monthly sales.
    • Add filters to interactively explore the data.


    • Geographic Visualization:
    • Use a geographic dataset (e.g., countries, states) to create a map visualization.
    • Color code the map based on a measure such as sales or population.
    • Drill down from country-level to state-level data using hierarchical filters.


    • Advanced Visualizations:
    • Create a dual-axis chart to compare two measures on the same axis.
    • Build a treemap to visualize hierarchical data such as sales by product category and subcategory.
    • Design a dashboard to display multiple visualizations together.


    • Calculations and Expressions:
    • Create a calculated field to calculate profit margin (profit divided by sales).
    • Use a LOD (Level of Detail) expression to calculate the total sales regardless of filters applied.
    • Implement a parameter to dynamically change the view (e.g., switch between different metrics).


    • Advanced Analytics:
    • Implement forecasting to predict future sales trends.
    • Use clustering algorithms to segment customers based on their purchasing behavior.
    • Apply trend lines and statistical models to analyze data patterns.


    • Dashboard Design and Interactivity:
    • Design a dynamic dashboard with interactivity (e.g., use of parameters, dashboard actions).
    • Incorporate user input controls like dropdowns and sliders to filter data dynamically.
    • Implement URL actions to link Tableau visualizations to external web pages or documents.


    • Sales Performance Analysis:
    • Analyze sales performance by region, product, and time period.
    • Identify top-performing products and regions.
    • Visualize sales trends and seasonality.


    • Customer Segmentation:
    • Segment customers based on demographics, purchasing behavior, or lifetime value.
    • Identify key characteristics of each segment and tailor marketing strategies accordingly.


    • Profitability Analysis:
    • Analyze profitability by product line, customer segment, or sales channel.
    • Identify low-margin products or unprofitable customer segments and recommend actions to improve profitability.

    • Introduction to Tableau Server:
    • Overview of Tableau Server
    • Introduction to Tableau Server architecture and components.
    • Understanding the role of Tableau Server in the Tableau ecosystem.


    • Installation and Configuration:
    • Installation prerequisites and best practices.
    • Step-by-step installation and configuration of Tableau Server.


    • User Management:
    • User authentication options (local authentication, Active Directory, SAML).
    • Managing users, groups, and permissions.


    • Content Management:
    • Publishing workbooks and data sources to Tableau Server.
    • Managing projects and content permissions.
    • Versioning and revision history.


    • Tableau Server Administration:
    • Server Administration Tasks:
    • Monitoring server status and performance.
    • Configuring server settings and resource management.
    • Backup and restore procedures.


    • Data Source Management:
    • Connecting to data sources and configuring data connections.
    • Managing data source permissions and connections.


    • Security and Governance:
    • Implementing security best practices.
    • Enforcing data governance policies.
    • Auditing and logging user activities.


    • High Availability and Scalability:
    • Configuring high availability and load balancing.
    • Scaling Tableau Server for increased capacity.


    • Advanced Topics:
    • Customization and Integration:
    • Customizing Tableau Server interface and branding.
    • Integrating Tableau Server with other applications and services.


    • Automation and Scripting:
    • Automating server tasks using Tableau Server REST API.
    • Scripting common administrative tasks for efficiency.


    • Disaster Recovery and Failover:
    • Planning and implementing disaster recovery strategies.
    • Configuring failover and redundancy options.


    • Tableau Server Exercises
    • Setting Up Tableau Server:
    • Installation and Configuration:
    • Install Tableau Server on a virtual machine or server environment.
    • Configure server settings, including authentication method (local, Active Directory, SAML).


    • Adding Users and Groups:
    • Add users to Tableau Server and assign them to appropriate groups.
    • Configure permissions to control access to projects, workbooks, and data sources.
    • Publishing Content to Tableau Server


    • Publishing Workbooks:
    • Publish a workbook from Tableau Desktop to Tableau Server.
    • Set permissions for the published workbook to control who can view and interact with it.


    • Publishing Data Sources:
    • Publish a data source to Tableau Server.
    • Configure data source permissions and refresh schedules.
    • Managing Content on Tableau Server:


    • Managing Projects:
    • Create new projects on Tableau Server to organize content.
    • Move workbooks and data sources between projects.


    • Content Permissions:
    • Modify permissions for existing content on Tableau Server.
    • Assign permissions to specific users or groups for projects, workbooks, and data sources.


    • Collaboration and Interactivity:
    • Creating and Managing Comments:
    • Add comments to workbooks and views on Tableau Server.
    • Reply to comments and manage comment threads.


    • Subscriptions and Alerts:
    • Set up email subscriptions to receive scheduled updates of workbook views.
    • Configure alerts to be notified when certain data thresholds are met.


    • Monitoring and Administration:
    • Server Status and Performance Monitoring:
    • Monitor server status, including CPU usage, memory usage, and disk space.
    • Identify performance bottlenecks and optimize server resources.


    • Backup and Restore:
    • Perform a backup of Tableau Server data and configuration.
    • Practice restoring Tableau Server from a backup in a test environment.


    • Security and Governance:
    • Security Best Practices:
    • Review and implement security best practices for Tableau Server.
    • Ensure compliance with data governance policies and regulations.


    • Auditing and Logging:
    • Review audit logs to track user activity on Tableau Server.
    • Analyze logs to identify security incidents or compliance issues.


    • Scaling and High Availability:
    • Scaling Tableau Server:
    • Add additional nodes to scale Tableau Server for increased capacity.
    • Configure load balancing to distribute traffic across multiple nodes.


    • High Availability Configuration:
    • Configure Tableau Server for high availability to ensure uptime and reliability.
    • Test failover and disaster recovery procedures to ensure continuity of service.

    • 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


    • Set Analysis in Qlik View
    • Date functions
    • What If analysis
    • Calculated Dimensions
    • Conditional Objects
    • Securing your document and document tuning
    • Cross tables
    • Bookmarks
    • Chart-level and script-level functions
    • Security measures and access points in QlikView
    • Integrating visualizations with dashboards

    • 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

    AWS (Amazon Web Services) and Azure (Microsoft Azure) are two of the leading cloud computing platforms offering robust data analytics services, each with its own strengths and capabilities tailored to diverse business needs.


    AWS provides a comprehensive suite of data analytics services under its Amazon Web Services umbrella. Key services include Amazon Redshift for data warehousing, Amazon EMR (Elastic MapReduce) for big data processing using Apache Hadoop and Spark, and Amazon Athena for querying data stored in Amazon S3 using standard SQL. AWS also offers analytics services like Amazon QuickSight for business intelligence and visualization, AWS Glue for ETL (Extract, Transform, Load) tasks, and AWS Data Pipeline for orchestrating data workflows. AWS's ecosystem is extensive, with a broad range of integrations and support for various programming languages and frameworks, making it a preferred choice for organizations seeking flexibility and scalability in their data analytics solutions.


    Azure, Microsoft's cloud platform, provides a robust set of data analytics services integrated with its suite of tools and services. Azure Synapse Analytics (formerly SQL Data Warehouse) offers enterprise-level data warehousing capabilities, supporting both relational and big data analytics. Azure HDInsight provides managed Apache Hadoop, Spark, HBase, and Storm clusters for big data processing. Azure Data Lake Store and Azure Databricks further enhance data storage and analytics capabilities, while services like Azure Machine Learning enable advanced predictive analytics and machine learning model development. Azure also includes Power BI for business intelligence and visualization, tightly integrating with other Microsoft products like Excel and SharePoint. Azure's strength lies in its seamless integration with Microsoft's enterprise ecosystem, making it an attractive option for organizations already using Microsoft technologies.

    • S3 Basics
    • Storage Classes 
    • Data Management
    • security & Access Control 
    • Cost Optimization
    • Monitoring & Logging 
    • Use Cases 
    • Data Replications and Disaster recovery
    • Course Overview 
    • Introducing our Hands-On Case Study
    • Collection Section 
    • Introduction Kinesis Data Streams Overview 
    • Hot shard 
    • Kinesis Producers
    • Kinesis Consumers 
    • Kinesis Enhanced Fan Out 
    • Kinesis Scaling
    • Kinesis - Handling Duplicate Records part 1 
    • Kinesis - Handling Duplicate Records part 2 
    • Kinesis Security 
    • Kinesis Data Firehose
    • CloudWatch Subscription Filters with Kinesis 
    • Kinesis Data Streams vs SQS 
    • IoT Overview 
    • IoT Components Deep Dive
    • Database Migration Service (DMS)
    • Direct Connect 
    • S3 Overview 
    • S3 Hands On 
    • S3 Security Bucket Policy
    • S3 Security Bucket Policy Hands On 
    • S3 Website Overview 
    • S3 Website Hands On
    • S3 Overview 
    • S3 Versioning Hands On 
    • S3 Server Access Logging
    • S3 Server Access Logging Hands On 
    • S3 Replication Overview
    • S3 Replication Hands On
    • S3 Storage Classes Overview 
    • S3 Storage Classes Hands On 
    • S3 Glacier Vault Lock & S3 Object Lock 
    • S3 Encryption
    • Shared Responsibility Model for S3 


    • DynamoDB Overview 
    • DynamoDB RCU & WCU
    • DynamoDB Partitions 
    • dynamodb api 
    • DynamoDB Indexes LSI & GSI
    • DynamoDB DAX 
    • DynamoDB Streams 
    • DynamoDB TTL 
    • DynamoDB Security
    • DynamoDB Storing Large Objects 
    • Lambda Overview 
    • Lambda Hands On
    • Why Cloud & Big Data on Cloud 
    • What is Virtual Machine 
    • On-Premise vs Cloud Setup
    • Major Vendors of Hadoop Distribution 
    • Hdfs vs S3 
    • Important Instances in AWS
    • Spark Basics 
    • Why spark is difficult 
    • Overview of EMR part 1 
    • Overview of EMR part 2 
    • What is EMR
    • Tez vs mapreduce 
    • Launching an emr cluster 
    • connecting to your cluster
    • Create a tunnel for web ui 
    • Use Hue to interact with EMR
    • Part 1 analyze movie ratings with hive on emr 
    • Part 2 analyze movie ratings with hive on emr
    • Transient vs Long Running Cluster Running 
    • Copy File From S3 to Local Zeppelin Notebook
    • How to Create a 
    • VM S3 & EBS 
    • Public ip Vs Private Ip
    • Aws Command Line Interface 
    • AWS Glue
    • Introduction to Amazon Redshift 
    • Redshift Master Slave Architecture 
    • Redshift demo
    • redshift specturm 
    • Redshift Distribution Styles
    • Redshift Fault Tolerance 
    • Redshift Sort Keys

    • Getting started with Azure
    • Creating Microsoft Azure account 
    • Understanding regions and availability zones in Azure
    • Getting started with Azure virtual machines 
    • Creating your first virtual machine in azure
    • Connecting to the Azure virtual machine and running commands 
    • Understanding Azure VM-key concepts
    • Simplifying installing software on the Azure virtual machine 
    • Increasing availability for azure VM
    • Virtual machine scale sets 
    • Exploring scaling and load balancing 
    • Static IP, monitoring and reducing costs
    • Designing a good solution with Azure VM 
    • Exploring Azure virtual machine scenarios
    • Azure Web Service Plan 
    • Azure Storage 
    • What is Data Factory
    • data factory in azure ecosystem 
    • Provision Azure data factory instance
    • data factory components 
    • data factory pipeline and activities
    • data factory linked service and datasets 
    • data factory integration runtime 
    • data factory triggers
    • data factory copy data activity demo 
    • copy data activity using author demo
    • secure input and output property 


    • user properties 
    • Data factory parameters
    • data flow concept 
    • mapping data flow
    • Wrangling data flow 
    • Monitoring
    • metrics and diagnostic settings 
    • why warehouse in cloud?
    • Traditional vs modern warehouse architecture 
    • what is synapse analytics service
    • demo create dedicated sql pool 
    • demo connect sql pool with ssms
    • demo create azure synapse analytics workspace 
    • Demo explore synapse studio v2
    • demo create dedicated sql pool and spark pool from inside synapse studio
    • demo analyse data using dedicated sql pool
    • analyse data using apache spark notebook
    • demo analyse data using serverless sql
    • demo data factory copy tool from synapse integrate tab
    • demo monitor synapse analytics studio
    • azure synapse a game-changer
    • azure synapse benefits

    • 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 to Health Care Analytics:
    • Overview of Health Care Systems:
    • Structure and functions of health care systems
    • Key stakeholders: patients, providers, payers, regulators


    • Importance of Analytics in Health Care:
    • Improving patient care
    • Enhancing operational efficiency
    • Reducing costs


    • Data in Health Care:
    • Types of Health Care Data:
    • Clinical data: Electronic Health Records (EHRs), laboratory results, imaging
    • Administrative data: billing, claims, patient demographics
    • Operational data: staffing, scheduling, supply chain


    • Data Sources and Collection Methods:
    • Primary vs. secondary data
    • Data collection tools and techniques


    • Data Management:
    • Data Governance:
    • Data quality and integrity
    • Data privacy and security (HIPAA compliance)


    • Data Warehousing:
    • Building and managing data warehouses
    • Extract, Transform, Load (ETL) processes


    • Analytical Techniques:
    • Descriptive Analytics:
    • Summarizing and describing historical data
    • Common metrics and KPIs in health care


    • Predictive Analytics:
    • Forecasting future trends and outcomes
    • Predictive modelling techniques (regression, classification)


    • Prescriptive Analytics:
    • Recommending actions based on data
    • Optimization and simulation models


    • Statistical Methods for Health Care Analytics:
    • Basic Statistical Concepts:
    • Descriptive statistics (mean, median, mode, variance)
    • Probability distributions


    • Inferential Statistics:
    • Hypothesis testing
    • Confidence intervals
    • p-values and significance testing


    • Health Care Data Visualization:
    • Principles of Effective Data Visualization:
    • Choosing the right type of chart or graph
    • Design principles for clarity and impact


    • Tools and Software:
    • Tableau, Power BI, R, Python (matplotlib, seaborn)
    • Creating dashboards and interactive visualizations


    • Health Care Informatics:
    • Clinical Informatics:
    • Integration of informatics in clinical settings
    • Use of EHRs and clinical decision support systems


    • Public Health Informatics:
    • Surveillance and reporting
    • Population health management


    • Machine Learning and AI in Health Care:
    • Introduction to Machine Learning:
    • Supervised vs. unsupervised learning
    • Key algorithms (decision trees, random forests, neural networks)


    • Applications in Health Care:
    • Diagnosis and treatment recommendations
    • Predictive maintenance for medical equipment
    • Natural Language Processing (NLP) for clinical documentation


    • Case Studies and Applications:
    • Clinical Applications:
    • Predictive models for patient readmissions
    • Analyzing clinical trial data


    • Operational Applications:
    • Workflow optimization
    • Resource allocation and scheduling


    • Financial Applications:
    • Revenue cycle management
    • Cost-benefit analysis


    • Ethical and Legal Considerations:
    • Ethical Issues in Health Care Analytics:
    • Bias and fairness in algorithms
    • Informed consent and patient autonomy


    • Legal and Regulatory Compliance:
    • HIPAA and other health care regulations
    • Data protection laws


    • Capstone Project:
    • Project Design and Implementation:
    • Identifying a real-world problem
    • Collecting and analysing data
    • Presenting findings and recommendations

Get in touch

|| Tool to Master

healthcare analytics tools to master , health care analyst learning tools , PYthon ,Tensorflow , Pytorch ,Google collab , VS Code ,SQL ,Numpy ,Seaborn Excel ,DOCKER

Certificate

|| Skill to Master

health care analytics skills to master ,data storytelling ,automation and scalability ,api integration ,data mining and exploration ,industry specific knowledge

Certificate

|| Significant Scope after Healthcare Analytics Course

  • Growing Healthcare Sector
  • India’s healthcare sector is one of the fastest-growing industries, driven by increasing demand for quality healthcare services, rising health awareness, and a growing middle class. The need for efficient, data-driven decision-making in hospitals, clinics, and other healthcare facilities creates substantial opportunities for healthcare analytics.
  • Digital Health Initiatives
  • The Indian government is actively promoting digital health initiatives, such as the National Digital Health Mission (NDHM), which aims to create a digital health ecosystem. This includes electronic health records (EHRs), telemedicine, and health information systems, all of which require robust analytics for effective implementation and management.
  • Insurance and Cost Management
  • With the expansion of health insurance coverage in India, there is an increased focus on cost management, fraud detection, and optimizing the claims process. Healthcare analytics helps insurers in risk assessment, pricing strategies, and identifying patterns in claims data to reduce costs.
  • Public Health and Policy Making
  • Analytics plays a crucial role in public health by aiding in disease surveillance, outbreak prediction, and health policy formulation. Government agencies and NGOs rely on data analytics to design and implement effective public health interventions and measure their impact.
  • Pharmaceutical and Clinical Research
  • India is a hub for pharmaceutical manufacturing and clinical research. Analytics is essential for drug development, clinical trials, and pharmacovigilance. It helps in analyzing clinical trial data, monitoring adverse drug reactions, and ensuring regulatory compliance.
  • Operational Efficiency in Healthcare Facilities
  • Hospitals and healthcare providers are increasingly using analytics to optimize their operations. This includes improving patient flow, reducing wait times, managing resources effectively, and enhancing the overall patient experience.
  • Personalized Medicine and Patient Care
  • With advancements in genomics and personalized medicine, healthcare analytics is becoming crucial in tailoring treatments to individual patients based on their genetic makeup, lifestyle, and other factors. This leads to more effective and targeted healthcare interventions.
  • Emergence of Health Tech Startups
  • India is witnessing a surge in health tech startups focusing on innovative solutions like telemedicine, remote monitoring, and AI-driven diagnostics. These startups rely heavily on data analytics to develop, refine, and market their products and services.
  • Educational and Training Programs
  • There is a growing number of educational institutions offering specialized courses in healthcare analytics, creating a skilled workforce ready to meet the demands of the industry. Continuous learning and development in this field are fostering innovation and expertise.
placement report placement report

|| Healthcare Analytics Course offers variety of Career Options and Job Opportunities

  • Data Analyst/Healthcare Analyst: Work within hospitals and clinics to analyze patient data, optimize clinical workflows, and improve patient outcomes.
  • Clinical Data Manager: Manage and maintain clinical databases, ensuring data quality and compliance with healthcare regulations.
  • Quality Improvement Analyst: Use data to identify areas for improvement in patient care and operational efficiency.
  • Clinical Research Analyst: Analyze data from clinical trials to assess the efficacy and safety of new drugs.
  • Pharmacovigilance Analyst: Monitor and analyze adverse drug reactions and ensure drug safety.
  • Market Research Analyst: Conduct market analysis to support strategic decision-making in drug development and marketing.
  • Actuarial Analyst: Use statistical models to predict healthcare costs and risks, and to develop pricing strategies for insurance products.
  • Claims Analyst: Analyze claims data to detect fraud, optimize claims processing, and manage healthcare costs.
  • Health Policy Analyst: Evaluate the impact of health policies and insurance plans on healthcare delivery and costs.
  • Public Health Analyst: Analyze population health data to inform public health strategies and policies.
  • Epidemiologist: Study the patterns and causes of diseases in populations to develop strategies for prevention and control.
  • Health Informatics Specialist: Develop and implement health information systems to improve data collection and analysis in public health.
  • Healthcare Consultant: Provide data-driven insights and recommendations to healthcare organizations on how to improve efficiency, reduce costs, and enhance patient care.
  • Strategy Consultant: Advise healthcare providers and companies on strategic initiatives based on data analysis and market trends.
  • Data Scientist: Develop advanced analytics and machine learning models to solve complex healthcare problems.
  • Product Manager: Oversee the development of healthcare analytics products and platforms, ensuring they meet market needs and regulatory requirements.
  • Health Tech Innovator: Lead or join startups focused on innovative healthcare solutions using data analytics and AI.
  • Academic Researcher: Conduct research in healthcare analytics, publish findings, and contribute to the academic body of knowledge.
  • Professor/Lecturer: Teach healthcare analytics courses and mentor students in universities and educational institutions.

|| Job Roles & Salary

healthcare analytics job roles , data analyst , healthcare analyst ,market research analyst ,healthcare consultant ,claims analyst ,product manager

Certificate

|| Healthcare Analytics holds Prominent Position in Indian Job Market

The field of healthcare analytics in India is burgeoning with a wealth of placement opportunities, driven by the increasing digitization of healthcare services and the growing emphasis on data-driven decision-making. Graduates and professionals equipped with healthcare analytics skills are in high demand across various sectors including hospitals, pharmaceutical companies, healthcare startups, and public health organizations. Leading hospitals and healthcare providers such as Apollo Hospitals, Fortis Healthcare, and Max Healthcare are actively seeking analysts to optimize patient care, manage hospital operations, and enhance clinical outcomes. Pharmaceutical giants like Dr. Reddy's Laboratories and Cipla utilize healthcare analytics to streamline drug development and improve market strategies.

 

Additionally, the rise of healthcare technology startups, such as Practo, 1mg, and HealthifyMe, offers dynamic roles in areas like patient data analysis, predictive modeling, and personalized healthcare solutions. Public health organizations and government initiatives are also significant employers, leveraging analytics to improve population health management and policy-making. Furthermore, global healthcare consultancies like Deloitte, PwC, and Accenture have a strong presence in India and frequently recruit healthcare analytics professionals to support their clients with strategic insights and data-driven solutions. With the continuous advancement of healthcare technologies and the growing importance of analytics in making informed healthcare decisions, the placement opportunities in this field are expansive and promising in India.

|| Empowering Your Career Transition From Learning To Leading

User Image
Rajvi Suthar

Rajvi Suthar, excelling as a Data Analyst at Tata Consultancy Services (TCS), leverages unique tools such as Python for scripting, R for statistical analysis, and Alteryx for data blending. Her adept use of these cutting-edge tools contributes to efficient and advanced data analysis solutions.

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Sarthak Gupta

Sarthak Gupta, demonstrating mastery as a Business Data Analyst at Accenture, leverages unique tools such as Power BI for visual analytics, Python for data scripting, and Alteryx for data blending. His adept use of these cutting-edge tools contributes to efficient and advanced business data analysis solutions.

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Megha Bhatt

Megha Bhatt, demonstrating prowess as a ML Engineer at Cognizant, leverages unique tools such as Alteryx for advanced data blending, Google BigQuery for large-scale data analytics. Her adept use of these cutting-edge tools contributes to innovative and efficient data analysis.

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Rishabhjit Saini

Rishabhjit Saini, demonstrating mastery as a Senior Data Processing professional at NielsenIQ, leverages unique tools such as Talend for data integration, Apache Spark for big data processing, and Trifacta for advanced data wrangling. His adept use of these cutting-edge tools contributes to efficient and innovative data handling.

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Darshna Dave

Darshna Dave, excelling as a Data Engineer at Deepak Foundation post-IT institute, showcases expertise in unique tools such as KNIME for data analytics workflows, Apache Superset for interactive data visualization, and RapidMiner for advanced predictive analytics.

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Shubham Ambike

Shubham Ambike, excelling as a Digital MIS Executive at Alois post-IT institute, showcases expertise in tools like Microsoft Excel, Power BI, and Google Analytics. His adept use of these tools contributes to efficient data management and analysis. Congratulations on his placement.

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Mehul Sirohi

Mehul Sirohi, excelling as a Data Associate at Numerator post-IT institute, skillfully employs unique tools such as Alteryx for data blending, Jupyter Notebooks for interactive data analysis, and Looker for intuitive data visualization. His mastery of these advanced tools contributes to Numerator's data processing success.

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|| Companies using Healthcare Analytics

Tata Consultancy Services (TCS)

  • Overview: TCS provides a range of healthcare solutions, including analytics services.
  • Applications: TCS uses analytics for predictive healthcare, improving clinical outcomes, optimizing hospital operations, and managing health information systems.

Wipro

  • Overview: Wipro offers healthcare analytics solutions as part of its broader IT services.
  • Applications: Their analytics services help in clinical data analysis, revenue cycle management, patient management systems, and fraud detection in insurance claims.

Infosys

  • Overview: Infosys delivers healthcare analytics services to help healthcare providers, payers, and life sciences companies.
  • Applications: Solutions include patient data management, predictive analytics for patient care, and operational analytics for efficiency improvement.

Cognizant

  • Overview: Cognizant provides a comprehensive suite of healthcare analytics services.
  • Applications: They focus on clinical data analytics, operational efficiency, financial analytics, and population health management.

Optum (a part of UnitedHealth Group)

  • Overview: Optum is a health services and innovation company using analytics to improve healthcare delivery.
  • Applications: Their analytics services cover areas like clinical outcomes, patient management, claims analytics, and health informatics.

Manthan Systems

  • Overview: Manthan provides advanced analytics solutions, including for the healthcare sector.
  • Applications: Their solutions include patient analytics, clinical data insights, and operational analytics to enhance healthcare delivery.

HealthifyMe

  • Overview: HealthifyMe is a digital health and wellness startup using analytics to offer personalized health and fitness plans.
  • Applications: They use data analytics to track user health metrics, provide diet and fitness recommendations, and monitor progress.

Practo

  • Overview: Practo is a digital healthcare platform that connects patients with healthcare providers.
  • Applications: Practo uses analytics to enhance its telemedicine services, improve appointment scheduling, and provide insights into patient behavior and preferences.

Niramai

  • Overview: Niramai is a health tech startup focused on early breast cancer detection using AI and analytics.
  • Applications: They use machine learning and thermal imaging analytics to detect abnormalities in breast tissue.

MedGenome

  • Overview: MedGenome is a genomics-based diagnostics and research company.
  • Applications: They utilize analytics to interpret genetic data for disease diagnosis, personalized medicine, and research.

THB (Technology | Healthcare | Big Data Analytics)

  • Overview: THB offers data analytics solutions tailored for healthcare providers and pharma companies.
  • Applications: Their services include clinical analytics, patient engagement, and market insights for pharmaceutical companies.

Qure.ai

  • Overview: Qure.ai develops AI-powered healthcare solutions.
  • Applications: They use analytics for medical imaging, providing diagnostic assistance in radiology through AI and machine learning.

Innovaccer

  • Overview: Innovaccer offers a healthcare data platform that integrates and analyzes healthcare data.
  • Applications: Their platform supports population health management, care coordination, and operational analytics.

|| Top Hiring Companies

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Certificate

|| Healthcare Analytics has significant placement opportunities in India

Here are some specific placement opportunities and roles available for healthcare analytics professionals:

Hospitals and Healthcare Providers
  • Healthcare Data Analyst: Analyze patient data to improve clinical outcomes, optimize workflows, and enhance patient satisfaction.
  • Clinical Data Manager: Oversee the collection, storage, and analysis of clinical data, ensuring data quality and compliance.
  • Quality Improvement Analyst: Use data to identify areas for improvement in healthcare delivery and patient care processes.
Pharmaceutical and Biotech Companies
  • Clinical Research Analyst: Analyze data from clinical trials to evaluate the efficacy and safety of new drugs.
  • Pharmacovigilance Specialist: Monitor and analyze adverse drug reactions to ensure drug safety.
  • Market Research Analyst: Conduct market analysis to support strategic decision-making in drug development and marketing.
Health Insurance Companies
  • Actuarial Analyst: Use statistical models to predict healthcare costs, assess risks, and develop pricing strategies for insurance products.
  • Claims Analyst: Analyze claims data to detect fraud, optimize claims processing, and manage healthcare costs.
  • Health Policy Analyst: Evaluate the impact of health policies and insurance plans on healthcare delivery and costs.
Consulting Firms
  • Healthcare Consultant: Provide data-driven insights and recommendations to healthcare organizations on improving efficiency, reducing costs, and enhancing patient care.
  • Strategy Consultant: Advise healthcare providers and companies on strategic initiatives based on data analysis and market trends.
Technology Companies and Startups
  • Data Scientist: Develop advanced analytics and machine learning models to solve complex healthcare problems.
  • Product Manager: Oversee the development of healthcare analytics products and platforms, ensuring they meet market needs and regulatory requirements.
  • Health Tech Innovator: Lead or join startups focused on innovative healthcare solutions using data analytics and AI.
Government and Public Health Organizations
  • Public Health Analyst: Analyze population health data to inform public health strategies and policies.
  • Epidemiologist: Study the patterns and causes of diseases in populations to develop strategies for prevention and control.
  • Health Informatics Specialist: Develop and implement health information systems to improve data collection and analysis in public health.
Research and Academia
  • Academic Researcher: Conduct research in healthcare analytics, publish findings, and contribute to the academic body of knowledge.
  • Professor/Lecturer: Teach healthcare analytics courses and mentor students in universities and educational institutions.Here are some specific placement opportunities and roles available for healthcare analytics professionals:
Hospitals and Healthcare Providers
  • Healthcare Data Analyst: Analyze patient data to improve clinical outcomes, optimize workflows, and enhance patient satisfaction.
  • Clinical Data Manager: Oversee the collection, storage, and analysis of clinical data, ensuring data quality and compliance.
  • Quality Improvement Analyst: Use data to identify areas for improvement in healthcare delivery and patient care processes.
Pharmaceutical and Biotech Companies
  • Clinical Research Analyst: Analyze data from clinical trials to evaluate the efficacy and safety of new drugs.
  • Pharmacovigilance Specialist: Monitor and analyze adverse drug reactions to ensure drug safety.
  • Market Research Analyst: Conduct market analysis to support strategic decision-making in drug development and marketing.
Health Insurance Companies
  • Actuarial Analyst: Use statistical models to predict healthcare costs, assess risks, and develop pricing strategies for insurance products.
  • Claims Analyst: Analyze claims data to detect fraud, optimize claims processing, and manage healthcare costs.
  • Health Policy Analyst: Evaluate the impact of health policies and insurance plans on healthcare delivery and costs.
Consulting Firms
  • Healthcare Consultant: Provide data-driven insights and recommendations to healthcare organizations on improving efficiency, reducing costs, and enhancing patient care.
  • Strategy Consultant: Advise healthcare providers and companies on strategic initiatives based on data analysis and market trends.
Technology Companies and Startups
  • Data Scientist: Develop advanced analytics and machine learning models to solve complex healthcare problems.
  • Product Manager: Oversee the development of healthcare analytics products and platforms, ensuring they meet market needs and regulatory requirements.
  • Health Tech Innovator: Lead or join startups focused on innovative healthcare solutions using data analytics and AI.
Government and Public Health Organizations
  • Public Health Analyst: Analyze population health data to inform public health strategies and policies.
  • Epidemiologist: Study the patterns and causes of diseases in populations to develop strategies for prevention and control.
  • Health Informatics Specialist: Develop and implement health information systems to improve data collection and analysis in public health.
Research and Academia
  • Academic Researcher: Conduct research in healthcare analytics, publish findings, and contribute to the academic body of knowledge.
  • Professor/Lecturer: Teach healthcare analytics courses and mentor students in universities and educational institutions.

|| Case Studies 

CASE STUDIES HEALTHCARE ANALYTICS (1).webp

Here are a few notable case studies that highlight the impact of healthcare data analytics:

 

Predicting Patient Readmissions:


Background: Parkland Health & Hospital System in Dallas, Texas, aimed to reduce the rate of patient readmissions within 30 days, a common metric for hospital quality.


Approach: The hospital utilized predictive analytics to identify patients at high risk of readmission. They collected and analyzed data from electronic health records (EHRs), including demographic information, clinical history, and social determinants of health.


Outcome: By implementing a predictive model, Parkland was able to identify high-risk patients and intervene with targeted care plans. This approach led to a significant reduction in readmission rates, improving patient outcomes and reducing healthcare costs.


Optimizing Emergency Department (ED) Operations:


Background: NewYork-Presbyterian Hospital sought to address overcrowding in its emergency department (ED), which can lead to longer wait times and reduced quality of care.


Approach: The hospital employed data analytics to analyze patient flow and ED operations. They used historical data to predict patient arrivals and optimize staffing levels and resource allocation.


Outcome: The predictive models helped the hospital manage patient flow more effectively, reducing wait times and improving the overall efficiency of the ED. This led to better patient satisfaction and more timely care delivery.


Enhancing Disease Surveillance:


Background: The CDC needed to enhance its ability to detect and respond to infectious disease outbreaks quickly.


Approach: The CDC implemented an advanced analytics platform that integrated data from multiple sources, including hospitals, laboratories, and social media. Machine learning algorithms were used to identify patterns and predict potential outbreaks.


Outcome: The analytics platform improved the CDC’s ability to detect outbreaks early and respond effectively. This proactive approach helped mitigate the spread of diseases and informed public health interventions.


Improving Chronic Disease Management:


Background: Kaiser Permanente aimed to improve the management of chronic diseases, such as diabetes and hypertension, among its patient population.


Approach: The healthcare organization used data analytics to identify patients with poorly controlled chronic conditions. They analyzed data from EHRs, including lab results, medication adherence, and lifestyle factors, to create personalized care plans.


Outcome: By leveraging data analytics, Kaiser Permanente was able to provide more targeted interventions and support to patients with chronic diseases. This resulted in better disease control, improved patient outcomes, and reduced healthcare costs.


Reducing Medication Errors:


Background: Medication errors are a significant concern in pediatric care, and Cincinnati Children’s Hospital aimed to reduce these errors.


Approach: The hospital implemented a clinical decision support system (CDSS) that utilized data analytics to provide real-time alerts and recommendations to healthcare providers. The system analyzed patient data, including allergies, current medications, and lab results, to prevent potential medication errors.


Outcome: The implementation of the CDSS led to a significant reduction in medication errors, enhancing patient safety and improving the quality of care provided to pediatric patients.


Personalizing Cancer Treatment:


Background: Memorial Sloan Kettering Cancer Center sought to personalize cancer treatment based on genetic profiles.


Approach: The center used advanced analytics and machine learning to analyze genomic data from cancer patients. By identifying specific genetic mutations, they were able to tailor treatment plans to the individual characteristics of each patient’s cancer.


Outcome: The personalized approach to cancer treatment resulted in more effective therapies, improved patient outcomes, and a higher rate of remission. This case study demonstrates the power of data analytics in advancing precision medicine.

|| Get Certified, Get Ahead

Three easy steps will unlock your Health Care Analytics Certification:

 

  • Finish the online / offline course of Health Care Analytics Course and the Assignment
  • Take on and successfully complete a number of industry-based Projects
  • Pass the Health Care Analytics certification exam

 

The certificate for this Health Care Analytics course will be sent to you through our learning management system, where you can also download it. Add  a link to your certificate to your CV or LinkedIn profile.

 

 

Certificate

|| Frequently asked question

Data analytics in healthcare helps doctors predict patients' medical interventions and treatments based on their age, medical history, and possible scenarios and outcomes of medication.

Data Analytics always helps companies to get an insight into how to develop the business. There are several types of tools you will require to interpret the data. Companies use data analytics tools to understand customer behavior and increase productivity.

BIT offers a wide range of programs catering to various interests and career paths. These may include academic courses, vocational training, professional development, and more. Please visit our website – www.bitbaroda.com or contact our admissions office at M.9328994901 for a complete list of programs.

For any questions or assistance regarding the enrolment process, admissions requirements, or program details, please don't hesitate to reach out to our friendly admissions team. Please visit our website – www.bitbaroda.com or contact our admissions office at M.9328994901 for a complete list of programs or Visit Our Centers – Sayajigunj, Waghodia Road, Manjalpur in Vadodara, Anand, Nadiad, Ahmedabad

BIT prides itself on providing high-quality education, personalized attention, and hands-on learning experiences. Our dedicated faculty, state-of-the-art facilities, industry partnerships, and commitment to student success make us a preferred choice for students seeking a rewarding educational journey.

BIT committed to supporting students throughout their academic journey. We offer a range of support services, including academic advising, tutoring, career counselling, and wellness resources. Our goal is to ensure that every student has the tools and support they need to succeed.

Healthcare Analytics involves the use of data analysis tools and techniques to improve healthcare delivery, patient outcomes, operational efficiency, and decision-making within healthcare organizations.

Healthcare professionals, data analysts, IT specialists, healthcare administrators, and anyone interested in using data to improve healthcare outcomes can benefit from these courses. They are relevant across various roles in healthcare organizations and public health sectors.

Students typically gain skills in data analysis, data management, statistical modeling, machine learning, data visualization, healthcare operations understanding, and ethical considerations in data use.

Yes, our institutions offer online Healthcare Analytics courses, making them accessible to students worldwide. These courses may be part of certificate programs, continuing education, or degree programs in healthcare management, informatics, or data science.

Commonly used tools include Microsoft Power BI, Tableau, SAS Healthcare Analytics, Python for data analysis and machine learning, R programming, and SQL for database management.

Benefits include acquiring in-demand skills for healthcare data analysis and management, improving career prospects in healthcare and data science fields, contributing to improved patient outcomes and healthcare efficiency, and staying abreast of technological advancements in healthcare.

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