Full Stack Analytics Course

Embark on an illuminating journey into the realm of data analytics with our comprehensive Full Stack Analytics Course. Whether you're a data enthusiast eager to explore the world of analytics or a seasoned professional looking to advance your skills, our curriculum is meticulously crafted to cater to learners of all levels. Enroll now and unlocked the limitless opportunities in the firld of Full stack Analytics

|| What will I learn?

  • Students should be able to collect data from various sources, including databases, APIs, and web scraping, and clean it to prepare it for analysis.
  • Students will learn how to apply statistical and machine learning techniques to analyze data and uncover patterns, trends, and relationships.
  • Visualizing data effectively is key to communicating insights to stakeholders. Students will learn how to create meaningful visualizations using tools like Matplotlib, Seaborn, Tableau, or Power BI to effectively convey their findings.
  • Students should be proficient in applying machine learning algorithms to build predictive models and make data-driven decisions.
  • In a full stack analytics course, students may also learn how to deploy analytical models into production environments and integrate them with existing systems and applications.

|| What will I learn?

  • Students should be able to collect data from various sources, including databases, APIs, and web scraping, and clean it to prepare it for analysis.
  • Students will learn how to apply statistical and machine learning techniques to analyze data and uncover patterns, trends, and relationships.
  • Visualizing data effectively is key to communicating insights to stakeholders. Students will learn how to create meaningful visualizations using tools like Matplotlib, Seaborn, Tableau, or Power BI to effectively convey their findings.
  • Students should be proficient in applying machine learning algorithms to build predictive models and make data-driven decisions.
  • In a full stack analytics course, students may also learn how to deploy analytical models into production environments and integrate them with existing systems and applications.

|| Requirements

  • While there are no strict prerequisites, participants are expected to have a basic understanding of programming concepts and some familiarity with data analysis tools and techniques.
  • Knowledge of Python or R programming languages is beneficial but not required.

|| Requirements

  • While there are no strict prerequisites, participants are expected to have a basic understanding of programming concepts and some familiarity with data analysis tools and techniques.
  • Knowledge of Python or R programming languages is beneficial but not required.

    • SQL Fundamentals
    • Various types of databases
    • Introduction to Structured Query Language
    • Distinction between client server and file server databases
    • Understanding SQL Server Management Studio
    • SQL Table basics
    • Data types and functions
    • Transaction-SQL
    • Authentication for Windows
    • Data control language
    • The identification of the keywords in T-SQL, such as Drop Table
    • Database Normalization
    • Entity Relationship Model
    • SQL Operators
    • Working with SQL
    • Join
    • Tables
    • Variables
    • Advanced concepts of SQL tables
    • SQL functions
    • Operators & queries
    • Table creation
    • Data retrieval from tables
    • Combining rows from tables using inner, outer, cross, and self joins
    • Deploying operators such as ‘intersect,’ ‘except,’ ‘union,’
    • Temporary table creation
    • Set operator rules
    • Table variables•
    • Deep Dive into SQL Functions
    • Working with Subqueries
    • SQL Views, Functions, and Stored Procedures
    • Deep Dive into User-defined Functions


    • SQL Optimization and Performance
    • SQL Server Management Studio
    • Using pivot in MS Excel and MS SQL Server
    • Differentiating between Char, Varchar, and NVarchar
    • XL path, indexes and their creation
    • Records grouping, advantages, searching, sorting, modifying data
    • Clustered indexes creation
    • Use of indexes to cover queries
    • Common table expressions
    • Index guidelines
    • Managing Data with Transact-SQL
    • Querying Data with Advanced Transact-SQL Components
    • Programming Databases Using Transact-SQL
    • Creating database programmability objects by using T-SQL
    • Implementing error handling and transactions
    • Implementing transaction control in conjunction with error handling in stored procedures
    • Implementing data types and NULL
    • Designing and Implementing Database Objects
    • Implementing Programmability Objects
    • Managing Database Concurrency
    • Optimizing Database Objects
    • Advanced SQL
    • Correlated Subquery, Grouping Sets, Rollup, Cube
    • Implementing Correlated Subqueries
    • Using EXISTS with a Correlated subquery
    • Using Union Query
    • Using Grouping Set Query
    • Using Rollup
    • Using CUBE to generate four grouping sets
    • Perform a partial CUBE

     

    • SQL Fundamentals 
    • Various types of databases 
    • Introduction to Structured Query Language 
    • Distinction between client server and file server databases 
    • Understanding SQL Server Management Studio
    • SQL Table basics 
    • Data types and functions 
    • Transaction-SQL 
    • Authentication for Windows 
    • Data control language
    • The identification of the keywords in T-SQL, such as Drop Table
    • Database Normalization 
    • Entity Relationship Model 
    • SQL Operators 
    • Working with SQL 
    • Join 
    • Tables 
    • Variables 
    • Advanced concepts of SQL tables 
    • SQL functions 
    • Operators & queries 
    • Table creation 
    • Data retrieval from tables 
    • Combining rows from tables using inner, outer, cross, and self joins 
    • Deploying operators such as ‘intersect,’ ‘except,’ ‘union,’ 
    • Temporary table creation 
    • Set operator rules 
    • Table variables
    • Deep Dive into SQL Functions 
    • Working with Subqueries 
    • SQL Views, Functions, and Stored Procedures 


    • Deep Dive into User-defined Functions 
    • SQL Optimization and Performance
    • SQL Server Management Studio 
    • Using pivot in MS Excel and MS SQL Server 
    • Differentiating between Char, Varchar, and NVarchar 
    • XL path, indexes and their creation 
    • Records grouping, advantages, searching, sorting, modifying data
    • Clustered indexes creation 
    • Use of indexes to cover queries 
    • Common table expressions 
    • Index guidelines
    • Managing Data with Transact-SQL 
    • Querying Data with Advanced Transact-SQL Components 
    • Programming Databases Using Transact-SQL
    • Creating database programmability objects by using T-SQL 
    • Implementing error handling and transactions
    • Implementing transaction control in conjunction with error handling in stored procedures 
    • Implementing data types and NULL
    • Designing and Implementing Database Objects
    • Implementing Programmability Objects
    • Managing Database Concurrency 
    • Optimizing Database Objects 
    • Advanced SQL 
    • Correlated Subquery, Grouping Sets, Rollup, Cube
    • Implementing Correlated Subqueries 
    • Using EXISTS with a Correlated subquery 
    • Using Union Query 
    • Using Grouping Set Query 
    • Using Rollup 
    • Using CUBE to generate four grouping sets 
    • Perform a partial CUBE


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

    • 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 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

    • 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.

    • 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

    • Introduction to Alteryx
    • Download and Install Alteryx
    • User Interface of Alteryx
    • Get Data from Excel
    • Get Data from CSV
    • Append All CSV files
    • Browse Tool
    • Output Tool - Update Existing Data
    • Directory Tool
    • Directory Tool - Specific Files
    • Text Input Tool
    • Date and Time Tool
    • Auto Field Tool
    • Data Cleansing Tool
    • Filter Tool (Text Example)
    • Filter Tool (Number Example)
    • Filter Tool ( Date Example)
    • Formula Tool ( Basic Example )
    • Formula Tool - (Multiple Examples)
    • Generate Rows Tool
    • Imputation Tool
    • Multi-Field Binning Tool
    • Multi-Field Formula
    • Multi Row Formula
    • Random % Sample Tool
    • Sample Tool
    • Record Id Tool
    • Select Tool
    • Sort


    • Create Sample Tool
    • Tile Tool
    • Unique Tool
    • Append Fields Tool
    • Find And Replace Tool
    • Fuzzy Match Tool
    • Join Tool
    • Join Multiple Tool
    • Union Tool
    • Regex Tool
    • Text To Columns
    • Cross Tab Tool
    • Transpose Tool
    • Running Total Tool
    • Summarize Tool
    • Table Tool
    • Interactive Chart Tool
    • Join Table And Chart
    • Add Annotation
    • Report Text Tool
    • Report Header Tool
    • Report Footer Tool
    • Report Layout Tool
    • Comment Tool
    • Explorer Tool
    • Container Tool

    • 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 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

This course is ideal for anyone interested in pursuing a career in analytics, data science, or related fields. It caters to beginners with little to no experience in analytics as well as professionals looking to enhance their skills or transition into analytics roles.

Prerequisites may vary, but typically include basic knowledge of mathematics and statistics, familiarity with programming concepts, and a willingness to learn. Some courses may require specific software or tool prerequisites, which will be mentioned in the course description.

Most reputable Full Stack Analytics Courses offer a certificate of completion that can be shared on your resume or LinkedIn profile. However, it's essential to check the accreditation and recognition of the issuing institution before enrolling.

Some courses may offer job placement assistance or career services, including resume building, interview preparation, and networking opportunities. However, this varies depending on the course provider

Yes, Full Stack Analytics Courses typically include hands-on projects and case studies to apply the skills learned in real-world scenarios. This practical experience is crucial for developing proficiency and building a portfolio to showcase to potential employers.

Completing a Full Stack Analytics Course can open doors to various career opportunities, including roles such as data analyst, business analyst, data scientist, analytics consultant, and more. Industries such as finance, healthcare, e-commerce, marketing, and technology actively seek professionals with analytics skills.

Completing a Full Stack Analytics Course can open doors to various career opportunities, including roles such as data analyst, business analyst, data scientist, analytics consultant, and more. Industries such as finance, healthcare, e-commerce, marketing, and technology actively seek professionals with analytics skills.

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