Banking Data Analytics Course in Dahod

• 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 is renowned for its versatility and ease of use, making it a popular choice among data analysts and scientists. It boasts a rich ecosystem of libraries and frameworks, such as NumPy, Pandas, and Scikit-learn, which are robust for data manipulation, statistical analysis, and machine learning. Python's syntax is straightforward and readable, making it accessible for those new to programming or transitioning from other languages. Its flexibility extends beyond data analysis to web development, automation, and scripting, making it a versatile tool in various industries.

On the other hand, R is specifically designed for statistical computing and data analysis. It excels in handling and manipulating data frames, making it particularly strong for statistical modeling, data visualization (with packages like ggplot2), and advanced analytics. R's extensive collection of statistical packages and libraries, coupled with its strong community support in academia and research, make it a preferred choice for statisticians and analysts who require sophisticated data analysis capabilities.

Choosing between Python and R often depends on specific project requirements and personal preferences. Python is favored for its general-purpose nature, broader application across different domains, and seamless integration with other technologies. Meanwhile, R remains the go-to language for statistical analysis and research-driven projects where data visualization and advanced statistical modeling are paramount.

Ultimately, both Python and R are valuable tools in the data analyst's toolkit. Many professionals choose to learn both languages to leverage their respective strengths depending on the task at hand, ensuring they have the flexibility to tackle a wide range of data analytics challenges effectively.

• 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 R
• What is R?
• Installing R
• R environment

• Understanding R data structure 
• Variables , Scalars
• Vectors, Matrices, List
• Data frames, functions, Factors

• Importing data
• Reading Tabular Data files
• Loading and storing data with a clipboard
• Accessing database, Writing data to file
• Writing text & output from analyses to file

• Manipulating Data
• Selecting rows/observations
• Rounding Number
• Merging data
• Relabeling the column names
• Data sorting
• Data aggregation

• Using functions in R
• Commonly used Mathematical Functions
• Commonly used Summary Functions
• Commonly used String Functions
• User-defined functions
• local and global variable
• Working with dates

• Looping
• While loop ,
• If loop

• Charts and Plots
• Box plot, Histogram, 
• Pie graph ,Line chart
• Scatterplot, Developing graphs

• Introduction to R Programming
• Overview of R and RStudio IDE
• Basic syntax, data types, and variables in R

• Data Import and Manipulation
• Importing data from various sources (e.g., CSV files, Excel spreadsheets, databases)
• Cleaning and preprocessing data using dplyr and tidyr packages

• Exploratory Data Analysis (EDA)
• Summarizing and visualizing data distributions, correlations, and patterns
• Identifying outliers, missing values, and data inconsistencies

• Data Visualization with ggplot2
• Creating static and interactive plots: scatter plots, histograms, bar charts, etc.
• Customizing plot aesthetics and themes for effective storytelling

• Statistical Analysis with R
• Descriptive statistics: mean, median, standard deviation, etc.
• Inferential statistics: hypothesis testing, confidence intervals, p-values

• Advanced Data Analytics Techniques
• Predictive modeling: linear regression, logistic regression, decision trees
• Cluster analysis: k-means clustering, hierarchical clustering
• Time series analysis: forecasting, seasonality, trend detection

• Reporting and Deployment
• Generating dynamic reports and presentations using RMarkdown
• Building interactive web applications with Shiny for data visualization and analysis

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

• Set Analysis in Qlik Sense
• 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 QlikSense
• 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
• Background:
• Overview of the bank: A large retail bank with a presence in multiple countries, serving millions of customers through various banking products and services.
• Current challenges: Increasing competition, maintaining customer satisfaction and loyalty, and rising incidents of financial fraud.
• Objective:
• Improve customer insights to enhance personalized services.
• Strengthen fraud detection mechanisms using data analytics.

• Data Collection and Preparation
• Data Sources:
• Transactional data: Deposits, withdrawals, transfers, payments, and loan activities.
• Customer data: Demographics, account information, transaction history, credit scores, and customer service interactions.
• External data: Market trends, economic indicators, and social media sentiment.
• Data Cleaning:
• Handling missing values: Imputation techniques such as mean, median, mode, and advanced methods like KNN imputation.
• Outliers and duplicates: Detection using statistical methods and removal or correction.
• Data consistency: Ensuring uniform formats for dates, currencies, and other standard fields.
• Data Integration:
• Combining data: Using ETL (Extract, Transform, Load) processes to merge data from various sources.
• Unified dataset creation: Ensuring a comprehensive dataset that includes all relevant features for analysis.

• Exploratory Data Analysis (EDA)
• Descriptive Statistics:
• Summary statistics (mean, median, mode, standard deviation) for key variables.
• Distribution analysis to understand the spread and central tendency of data.
• Visualization:
• Histograms: To visualize the distribution of transactional amounts.
• Scatter plots: To identify relationships between variables such as age and spending patterns.
• Heatmaps: To visualize correlation between different variables.
• Time series analysis: To identify trends and seasonal patterns in transaction data.

• Customer Segmentation
• Objective:
• Group customers based on similar characteristics and behaviors to tailor marketing and service strategies.
• Methodology:
• Clustering algorithms:
• K-means clustering for partitioning customers into distinct groups.
• Hierarchical clustering for creating nested clusters of customers.
• Feature selection:
• Using spending habits, product usage, transaction frequency, account balance, and demographic information as key features.
• Results:
• Identification of distinct customer segments such as high-net-worth individuals, frequent transactors, and low-balance customers.
• Insights into the needs, preferences, and potential product interests of each segment.

• Predictive Analytics for Personalized Services
• Objective:
• Predict customer needs and behaviors to offer personalized banking services and products.
• Methodology:
• Predictive modeling:
• Using machine learning algorithms like logistic regression, decision trees, and random forests.
• Feature engineering:
• Creating new features from existing data such as transaction frequency, average transaction amount, and recency of transactions.
• Model training and validation:
• Splitting data into training and test sets.
• Evaluating model performance using metrics such as accuracy, precision, recall, and F1-score.
• Results:
• Prediction of customer churn, product preferences, and likelihood of loan default.
• Personalized marketing campaigns and product recommendations based on predictive insights.

• Fraud Detection
• Objective:
• Enhance the bank's ability to detect and prevent fraudulent transactions.
• Methodology:
• Anomaly detection:
• Using unsupervised learning algorithms such as isolation forests and autoencoders to detect anomalies in transaction data.
• Supervised learning:
• Building classification models using labeled data to identify fraudulent transactions.
• Real-time analytics:
• Implementing real-time monitoring systems to flag suspicious activities as they occur.
• Results:
• Reduction in false positives and improved accuracy in fraud detection.
• Faster response times to fraudulent activities, minimizing financial losses.

• Implementation and Results
• Implementation:
• Integrating the analytics solutions into the bank's existing IT infrastructure.
• Training staff on the new tools and processes.
• Continuous monitoring and refinement of models to ensure accuracy and relevance.
• Results:
• Enhanced customer satisfaction and loyalty through personalized services.
• Significant reduction in fraudulent activities and associated losses.
• Improved decision-making capabilities for marketing and risk management teams.