Business Analytics Course

Business analytics is the practice of using data analysis and statistical methods to derive insights, make informed decisions, and drive strategic planning within an organization. It involves collecting, processing, and analyzing data from various sources to uncover patterns, trends, and relationships that can help improve business performance and competitiveness.

|| What will I learn?

  • Participants will gain a solid understanding of the fundamentals of business analytics, including its role in decision-making, key concepts, and terminology.
  • Develop skills in data-driven decision-making and problem-solving.
  • Participants will master data visualization techniques, including designing effective dashboards and reports, and using visualization tools to communicate insights visually.
  • Understand ethical considerations and best practices in business analytics.

|| What will I learn?

  • Participants will gain a solid understanding of the fundamentals of business analytics, including its role in decision-making, key concepts, and terminology.
  • Develop skills in data-driven decision-making and problem-solving.
  • Participants will master data visualization techniques, including designing effective dashboards and reports, and using visualization tools to communicate insights visually.
  • Understand ethical considerations and best practices in business analytics.

|| Requirements

  • Familiarity with spreadsheet software (e.g., Microsoft Excel) is recommended but not required.
  • Basic understanding of business concepts and terminology.

|| Requirements

  • Familiarity with spreadsheet software (e.g., Microsoft Excel) is recommended but not required.
  • Basic understanding of business concepts and terminology.

    • 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

    • Module-1 Introduction to Power BI:
    • Overview of Power BI tools and their functionalities.
    • Understanding the Power BI ecosystem.


    • Getting Started:
    • Installing Power BI Desktop.
    • Navigating the Power BI interface.
    • Connecting to data sources.


    • Data Transformation:
    • Importing data into Power BI.
    • Cleaning and shaping data using Power Query Editor.
    • Data modeling basics.


    • Visualization Basics:
    • Creating basic visualizations (e.g., bar charts, line charts, pie charts).
    • Applying formatting and customization to visualizations.
    • Adding interactivity with slicers and filters.


    • Exercises:
    • Import Data:
    • Import a dataset from Excel, CSV, or a database.
    • Clean and transform the data using Power Query Editor.


    • Basic Visualizations:
    • Create a bar chart to visualize sales by product category.
    • Create a line chart to show trends in monthly sales.


    • Filters and Slicers:
    • Add slicers to filter data by region, product, or date.
    • Use filters to focus on specific time periods or product segments.


    • Advanced Data Modeling:
    • Relationships in Power BI.
    • DAX (Data Analysis Expressions) fundamentals.
    • Calculated columns and measures.


    • Advanced Visualization Techniques:
    • Custom visuals.
    • Drill-through and drill-down.
    • Hierarchies and grouping.


    • Data Analysis:
    • Using DAX functions for advanced calculations.
    • Time intelligence functions.
    • Statistical analysis in Power BI.


    • Power BI Service:
    • Publishing reports and dashboards.
    • Sharing and collaboration.
    • Power BI mobile app.


    • Exercises:
    • Calculated Columns and Measures:
    • Create a calculated column to calculate profit margin.
    • Write a DAX measure to calculate year-over-year growth in sales.


    • Relationships and Hierarchies:
    • Define relationships between multiple tables.
    • Create hierarchical structures for date or product categories.


    • Advanced Visualizations:
    • Design a custom visual using the custom visuals gallery.
    • Implement drill-through functionality to analyze data at a more granular level.


    • Advanced Data Preparation:
    • Dataflows in Power BI.
    • Advanced data transformation techniques.


    • Power BI Administration:
    • Security and permissions.
    • Managing datasets and workspaces.
    • Performance optimization.


    • Advanced Visualization Design:
    • Design principles for effective visualizations.
    • Interactive and responsive report design.
    • Advanced Analytics:


    • Exercises:
    • Advanced Data Modeling:
    • Implement role-playing dimensions for date tables.
    • Use bidirectional filtering in complex data models.


    • Time Intelligence:
    • Calculate moving averages and cumulative totals using DAX.
    • Implement time-based calculations for year-to-date sales, rolling averages, etc.


    • Power BI Service:
    • Publish a report to the Power BI Service.
    • Share the report with colleagues and set up row-level security.
    • Integrating R and Python scripts.
    • Machine learning in Power BI.

    • 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.


    • 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.

    • 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

    • 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

    • 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

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|| Frequently asked question

Business Analytics involves the use of data analysis and statistical methods to derive actionable insights from business data, enabling organizations to make informed decisions, solve complex problems, and drive business performance and growth.

This course is suitable for individuals interested in leveraging data analytics techniques to drive business insights and improve decision-making processes. It caters to professionals working in various industries, including business analysts, data analysts, managers, consultants, and entrepreneurs.

Prerequisites may include a basic understanding of statistics, mathematics, and business concepts. Familiarity with tools such as Microsoft Excel or programming languages like Python or R is beneficial but not always required for introductory courses.

Most reputable Business Analytics courses offer a certificate of completion, which can validate your skills and be added to your resume or LinkedIn profile. It's essential to verify the accreditation and recognition of the issuing institution or organization.

Some courses offer job placement assistance or career services, including resume building, interview preparation, and networking opportunities with industry professionals. However, this varies depending on the course provider and the course's focus.

Yes, many Business Analytics courses are available online, offering flexibility in terms of timing and location. Online courses often provide video lectures, interactive exercises, and discussion forums to facilitate learning.

Yes, Business Analytics courses typically include hands-on projects, case studies, and practical exercises to apply the techniques learned to real-world business problems and scenarios. This practical experience is essential for developing proficiency and building a portfolio of projects.

After completing the course, you may continue to have access to course materials, online resources, alumni networks, career services, and professional development opportunities to support your continued learning and career growth.

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