What is Python?

Python is a easier, useful platform used for Big Data Analytics, Software Development, Cloud Scripting, Data Science, Machine Learning, Automations and almost everything with Data !

Who can join this Python Analytics Training Course?

Anyone who wants to get into Data Platform, Data Analytics, Data Engineering and Data Science can find this course very very useful. This course includes Basic to Advanced Data Analytics concepts including Data Ingestions, ETL, Data Cleansing, Big Data Loads, Pandas Dataframes, SQL Server TSQL Integrations, Power BI Desktop Integrations, Power BI Cloud Concepts with Python Analytics.


Python Analytics Training (with Pandas Dataframes, SQL. Excel, Power BI)

  PLAN A PLAN B PLAN C
Course includes 1. Python Analytics 1. SQL Server, TSQL
2. Python Analytics
1. SQL Server, TSQL
2. Python Analytics
3. Power BI Analytics
Total No. Of Videos 18 Videos 30 Videos 48 Videos
Python Fundamentals
Python Jupyter Notebooks
Python Data Types, Classes
Python Iterators, Lists
Python Functions, JSON
Python for Big Data Analytics
Python Data Frames, Pandas
Python with SQL Server
Python with TSQL Queries
Python with SQL Aggregations
Python with Power BI
Python with Cloud Service
SQL : Database Basics
TSQL : Database Basics, T-SQL
TSQL : Constraints, Joins, Queries
TSQL : Views, Group By, Self Joins
TSQL : Excel Analytics & Pivot
TSQL : Procedures & Functions
Power BI : Report Design, Visuals
Power BI : M Lang, DAX for ETL
Power BI : Cloud, Apps, Tenant
Power BI : Paginated Reports
Power BI with Python Analytics
DA 100 Certification Exam Guidance
Total Course Fee* INR 12000
USD 150
INR 17000
USD 200
INR 29000
USD 400

Trainer: Mr.Sai Phanindra (18+ Yrs of Exp)


If you are looking for LIVE Online Training, please register for Python Training Videos

TRAINING HIGHLIGHTS

Python Fundamentals Python Data Frames, Pandas
Python Jupyter Notebooks Python with SQL Server
Python Data Types, Classes Python with TSQL Queries
Python Iterators, Lists Python with SQL Aggregations
Python Functions, JSON Python with Power BI
Python for Big Data Analytics Python with Cloud Service

All Training Sessions are step by step, practical and job oriented. Book Your Free Demo Today             

Python Training Course Contents:

Ch 1: DATABASE INTRODUCTION

  • Databases Introduction & Purpose
  • Database Types : OLTP, DWH, OLAP
  • Microsoft SQL Server Advantages, Use
  • SQL Server Components and Usage
  • Microsoft SQL Server - Career Options
  • Developer, DBA, Data Engineer
  • Data Analyst, Data Scientist Careers
  • SQL : Purpose, Real-time Usage Options
  • SQL Versus Microsoft T-SQL [MSSQL]
  • Course Plan, Real-time Project, Resume
  • 24 x 7 Online Lab for Remote DB Access
  • Versions and Editions of SQL Server
  • SQL Server Pre-requisites : S/W, H/W
  • System Configuration Checker Tool

Ch 5: SQL Basics - 3, TSQL INTRO

  • Database Objects : Tables and Schemas
  • Schemas : Group Tables in Database
  • Schemas : Security Management Object
  • Creating Schemas & Batch Concept
  • Using Schemas for Table Creation
  • Data Storage in Tables with Schemas
  • Data Retrieval & Usage with Schemas
  • Table Migrations across Schemas
  • Import and Export Wizard in SSMS
  • Data Imports with Excel File Data
  • Performing Bulk Operations in SSMS
  • Temporary Tables : Real-time Use
  • Local and Global Temporary Tables
  • # and ## Prefix, Scope of Usage

Ch 9: Functions, Procedures Basics

  • Functions with SQL Server, TSQL
  • Scalar, Inline, Table Functions
  • Variables: Declare, Real-time Use
  • Creating, Executing Functions
  • Functions for Computations
  • Functions for Parameterized Joins
  • Procedures: Usage in Real-time
  • Using Parameters in SQL Server
  • Parameterized Joins in TSQL
  • Compilation with Stored Procedures
  • sp_help, sp_helptext, sp_helpindex
  • sp_helpdb, sp_rename, sp_recompile
  • System Views For Metadata Audits
  • DBID, DBName, ObjectID, ObjectName

Ch 2: SQL SERVER INSTALLATION

  • SQL Server & SSMS Installation Plan
  • SQL Server Pre-requisites : S/W, H/W
  • SQL Server 2022 & 2019 Installation
  • Database Engine Feature, OLTP
  • Instances : Types and Properties
  • Default Instance, Named Instances
  • Service and Service Account Use
  • Authentication Modes and Logins
  • Windows Logins and SQL Logins
  • SQL Server Management Studio
  • Server Connections with SSMS Tool
  • Local and Remote Connections
  • System Databases: Master and Model
  • MSDB, TempDB, Resource Databases

Ch 6: Constraints, Index Basics

  • Constraints and Keys - Data Integrity
  • NULL, NOT NULL Property on Tables
  • UNIQUE KEY Constraints: Importance
  • PRIMARY KEY Constraint: Importance
  • FOREIGN KEY Constraint: Importance
  • REFERENCES, CHECK & DEFAULT
  • Candidate Keys and Identity Property
  • Database Diagrams and ER Models
  • Relationships Verification and Links
  • Indexes : Basic Types and Creation
  • Index Sorting and Search Advantages
  • Clustered and NonClustered Indexes
  • Primary Key and Unique Key Indexes
  • Need for Indexes - working with Keys

Ch 10: TRIGGERS & TRANSACTIONS

  • Triggers - Purpose, Real-world Usage
  • FOR/AFTER Triggers - Real time Use
  • INSTEAD OF Triggers - Real time Use
  • INSERTED, DELETED Memory Tables
  • Using Triggers for Data Replication
  • Enable Triggers and Disable Triggers
  • Database Level, Server Level Triggers
  • Transactions : Types, ACID Properties
  • Transaction Types and AutoCommit
  • EXPLICIT & IMPLICIT Transactions
  • COMMIT and ROLLBACK Statements
  • Batch Concept and Go Statement
  • Open Transactions in Real-time
  • Using Conditional Commits, Rollbacks

Ch 3: SSMS Tool, SQL BASICS - 1

  • Creating Databases: Files [MDF, LDF]
  • Creating Tables in User Interface
  • Data Insertion & Report in User Interface
  • SQL : Purpose and Real-time Usage
  • SQL Versus T-SQL : Basic Differences
  • DDL, DML, SELECT, DCL and TCL
  • Creating SSMS Sessions : SPID
  • Create, Connect Databases using SQL
  • Creating Tables with INT, CHAR
  • Data Storage, Inserts - Basic Level
  • Table Data Verifications with Select
  • SELECT Statement for Table Retrieval
  • Identify Databases and Tables
  • Identify Sessions and Session ID

Ch 7: Joins Basics, TSQL Queries

  • JOINS - Table Comparisons Queries
  • INNER JOINS For Matching Data
  • OUTER JOINS For (non) Match Data
  • Join Queries with "ON" Conditions
  • Left Outer Joins - Example Queries
  • Right Outer Joins - Example Queries
  • FULL Outer Joins: Realtime Scenarios
  • CROSS JOIN and CROSS APPLY
  • One-way, Two way Data Comparisons
  • Using Table Aliases & Column Aliases
  • Optimizing Join Queries with Indexes
  • Choosing Correct Comparison Columns
  • Joining Unrelated Tables in TSQL
  • Self References, Self Joins in TSQL

Ch 11:  Normal Forms, Cursors

  • First Normal Form and Atomicity
  • Third Normal Form and MVD Property
  • Boycee-Codd Normal Form : BNCF
  • Fourth Normal Form : Advantages
  • Self Reference Keys and 4 NF Usage
  • 1:1, 1:M, M:1, M:M Relationship Types
  • Computed Columns, Variant Type
  • Linked Servers, Remote Joins in TSQL
  • 2 Part, 3 Part, 4 Part Naming Styles
  • Remote Joins Queries and Aliases
  • Cursors - Basics, Data Operations
  • Cursors - Life Cycle & Declaration
  • Cursors Types, FETCH Operations
  • Cursors - Deallocate, Real-world Use

Ch 4: SQL BASICS - 2

  • Creating Tables: VARCHAR, FLOAT
  • Single Row Inserts, Multi Row Inserts
  • Rules for Data Insertion Statements
  • SELECT with WHERE Conditions
  • AND and OR Operators Usage
  • IN Operator and NOT IN Operator
  • Between, Not Between Operators
  • LIKE and NOT LIKE Operators
  • ORDER BY, TOP & OFFSET
  • Basic Sub Queries with SELECT
  • UPDATE Statement & Conditions
  • DELETE & TRUNCATE Statements
  • ALTER, ADD COLUMN Statements
  • DROP Statements: Table, Database

Ch 8: Group By, Views & Excel

  • GROUP BY: Importance, Realtime Use
  • GROUP BY Queries and Aggregations
  • Group By Queries with Having Clause
  • Group By Queries with Where Clause
  • Using WHERE and HAVING in T-SQL
  • Group By with Joins in TSQL
  • Query Execution Order & Aliases
  • Joins with Sub Queries, Formatting
  • Database Objects: Overview & Usage
  • Views: Types, Usage in Real-time
  • Creating, Executing & Verifying Views
  • Storing Queries in Database Views
  • Excel Analytics - Joins & Views
  • Excel Office Data Connection Reports

Ch 12: TSQL Merge, Cursors

  • IIF() Function with SELECT Query
  • WHEN..THEN..ELSE
  • WHEN MATCHED, NOT MATCHED
  • Incremental Loads, Upsert Statement
  • Stored Procedures: Merge Statement
  • UNION and UNION ALL Operator
  • Window Functions: Rank, Dense Rank
  • Row_Number, PartitionBy in TSQL
  • Duplicate Row Identification, Deletion
  • Grouping, Cube, Rollup, Lag, Lead
  • Data Types: Numerical, Date, Time
  • Data Types: Characters, Real, Float
  • Date & Time Functions, DateAdd
  • String Functions, Concat, SubString
Case Study 1: Database Design with Tables,
Constraints, Keys & Relations
Case Study 2: Joins with Group By,
Sub Queries, Views, Excel Analytics

Part 1: Python Fundamentals

Part 2: Python For Data Analytics - 1

Part 3: Python For Data Analytics - 2

Ch 1: Data Analytics Intro & Python

  • Data and Databases : Introductions
  • Data Analytics Job Role
  • Python : Introduction & Advantages
  • Python : Career Options, Scope
  • Python for Big Data Analytics
  • Why Python? Usage Options
  • Python Installation : Multi OS
  • Anaconda Software Installation
  • Jupyter Interface for Python
  • Python Activities with Jupyter
  • Notebooks : Python Web Interface
  • Notebooks and Cells: Introduction

Ch 7: Python Functions

  • Python Functions : Realtime Use
  • Function : Creation, Execution Call
  • Function Parameters, Arguments
  • Arguments Number, Arg keyword
  • Arbitrary Keyword & **kwargss
  • Default & List Value Parameters
  • Python Lambda Functions
  • Using Lamdba Options in Python
  • Anonymous Functions in Python
  • Arguments, Expressions
  • Recursive Functions, Usage
  • Return & Print with Lamdba

Ch 13: Data Analytics - Pandas

  • Python Modules & Pandas
  • Why Use Pandas?
  • Pandas Codebase & Usage
  • Installation of Pandas
  • import & pandas.DataFrame
  • Checking Pandas Version
  • Pandas Series
  • one-dimensional array
  • Labels : Creation, Use
  • series(), print()
  • Pandas DataFrames
  • Dataframes & Series

Ch 2: Basic Operations with Python

  • Python Interface : Creating Notebook
  • Adding Cells, Saving Notebook
  • Executing Basic Cells; Result Window
  • Single Line & Multi Line Comments
  • Save, Open / Clone Notebooks
  • Indentation Options with Python
  • Python : Internal Architecture
  • Code Editor, Source Files
  • Compiler, Byte Code, Virtual Machine
  • Program Execution : Py, PYC and PVM
  • Python Libraries / Modules
  • Compiler Versus Interpreter

Ch 8: Python Classes & Arrays

  • Python Classes & Objects
  • Python Classes : Usage
  • __init__() Function
  • __str__() Function
  • Self Parameters & Usage
  • Object Properties Options
  • Python Inheritance & Classes
  • Adding Parent & Child Classes
  • Add __init__() Function
  • Using super() Function
  • Add Properties, Methods
  • Polymorphism in Python

Ch 14: Data Analytics - DataFrames

  • Pandas DataFrames in Python
  • DataFrame() & Realtime Usage
  • Indexes & Named Options
  • Locate Row and Load Rows
  • Row Index & Index Lists
  • Load Files Into a DataFrame
  • Pandas Read CSV
  • pd.read_csv() Function
  • pd.options.display.max_rows
  • df.to_string() Function
  • Dictionary as JSON
  • tail() & null() Function

Ch 3: Data Types & Variables

  • Integer / Int Data Types
  • Float & String Data Types
  • Boolean Data Types, Binary Types
  • Sequence Types: List, Tuple
  • Range, Complex & memoryview
  • Retrieving Data Type: type()
  • Python Variables: Naming
  • Camel / Pascal / Snake Case
  • Multi Assignments & Casting
  • Multi Word Variables, PRINT
  • Multiple Variables and Vales
  • Unpack Collection, Outputs

Ch 9: Python Modules

  • Python Modules : Creation
  • Import Python Modules
  • Using Variables in Modules
  • Naming, Renaming Module
  • Built In Modules & dir
  • Using Modules, Properties
  • datetime module in Python
  • Date Objections, strftime Method
  • import datetime, datetime.now()
  • Using Python Constructors
  • Conditional Columns, Expressions
  • Disable / Enable Data Loads

Ch 15: Data Analytics - Pandas

  • Pandas - Cleaning Data
  • Removing Rows, Data Cleansing
  • Replace, Transform Columns
  • Data Discovery & Column Fill
  • Identify & Remove Duplicates
  • dropna(), fillna() Functions
  • Pandas - Data Correlations
  • Data Relations and Validations
  • Good & Bad Correlation
  • Perfect Correlation Scenarios
  • Python Data Plotting Options
  • matlib Module & Plotting

Ch 4: Python Operators, Conditions

  • Python Operators : Arithmetic
  • Assignment, Compare Operators
  • Logical, Identity Operators
  • Member & Bitwise Operators
  • Operator Precedence Options
  • Python Operator Expressions
  • Python If ... Else Statement
  • Short Hand If Statement, Use
  • OR, AND and NOT Statements
  • Pass Statement with Python
  • ELIF and ELSE IF Statements
  • Ternary Operators in Python

Ch 10: Python JSON & RegEx

  • Python JSON Concepts, Usage
  • Python Dictionary & import json
  • Convert from Python to JSON
  • Python Objects into JSON strings
  • Result Formatting & Ordering
  • json.dumps, print options
  • Python Regular Expressions
  • RegEx Module in Python
  • RegEx Functions : findall
  • search() Function & split
  • span() function & Usage
  • Using RegEx with JSON

Ch 16: SQL Server with Python - 1

  • Installing SQL Server DB Engine
  • Install Machine Learning Services
  • SQL Server Management Studio
  • Install Azure Data Studio Tool
  • sp_execute_external_script
  • Input Data & Result Sets
  • DDL & DML with Python
  • SQL_out, SQL_in with Python
  • Variables & Parameters in Python
  • Python Version, Package List
  • Script Parameters & Usage
  • WITH RESULT SETS Options

Ch 5: Python Loops, Iterations

  • Python Loop & Realtime Use
  • Python While Loop Statement
  • Break and Continue Statement
  • Using Print with While()
  • Iterations & Conditions
  • Exit Conditions : Cautions
  • Python For Loop Statement
  • Break, Continue & Range
  • Python Iterators : Creation
  • __iter__() and __next__()
  • Iterator vs Iterable
  • iter() and Looping Options

Ch 11: Python User Inputs & TRY

  • Python Try Except
  • Python Exception Handling
  • NameError Resolution
  • Python Finally Block, Usage
  • Raise an exception method
  • TypeError, Scripting in Python
  • Python User Inputs
  • Python String Formatting
  • Multiple Values & String
  • Python Index Numbers
  • Named Indexes, Usage
  • input() & raw_input()

Ch 17: SQL Server with Python - 2

  • Using pandas.Series with SQL Server
  • Indexing Methods and Realtime Use
  • Convert series to data frame
  • DataFrames with SQL Server
  • Output values into data.frame
  • Output Datasets and Usage
  • pymssql package in SQL Server
  • pip list & Package Manager
  • Python runtime, Py Package Index
  • pymssql.connect & Usage
  • Query Execution & Result
  • Cursor Variables & Usage

Ch 6: Python Collections

  • Python Collections (Arrays)
  • Python Collection Data Types
  • List, Tuple, Set, Dictionary
  • List Items, Ordered & Length
  • list() Constructor, print()
  • Python Tuples, Tuple Items
  • tuple() Constructor, Usage
  • Python Sets : Syntax Rules
  • Duplicates, Types, Ordered
  • Python Dictionaries: Usage
  • Changeable, Ordered Data
  • Dictionary Construct, type()

Ch 12: Python File Handling

  • File Handling
  • r, a, w, x modes
  • t, b Operations
  • File Activities
  • Read Only Parts
  • Loop, Close Files
  • Python File Write
  • Appending, Overwriting
  • Create a New File
  • import os, path.exists
  • f.open, f.write
  • f.read, f.close

Ch 18: Power BI with Python

  • Installing Power BI Desktop
  • Using Python Script Visual
  • PyScript Options & Tuning
  • Settings, Labelling Options
  • Running and Testing Scripts
  • Data Validations in Power BI
  • Power BI Cloud : ipynb Scripts
  • Python in Desktop Vs Cloud
  • Interactive Reports with Python
  • Power Query Options (M Lang)
  • Data Formatting with Python
  • Integrate SQL, Power BI, Python

Power BI : Realtime Project (Sales - Retail)

Phase 1 : Basic Report Design

  • Project Requirement Analysis
  • Requirement Gathering, FSA
  • Report Design with Excel
  • Basic Data Modelling
  • Infographics, Histograms
  • Analytics and Formating

Phase 2 : SME Level

  • Report Design with SQL DB
  • SQL Database : Joins, Views
  • Dual Storage Mode, SQL Queries
  • Data Modeling, Power Query
  • Dynamic Connections, Azure DB
  • Parameters and M Lang Scripts

Phase 3: Deployments (Cloud, Server)

  • DAX Requriements, Analysis
  • Cloud and Report Server
  • Custom Visualizations
  • 3party Visuals & REST API *
  • Project FAQs and Solutions
  • One - One Resume, Mock Interview
Part 1: Power BI Report Design
Part 2: Power Query, Cloud (Service)
Part 3: DAX & Report Server

Ch 1: POWER BI INTRODUCTION

  • Power BI : Introduction to Analytics
  • Power BI Tools Suite, Advantages
  • Power BI : Career Options, Plan
  • Power BI Developer Job Role
  • Microsoft Data Analyst Job Role
  • Big Data Analyst Job Role
  • Power BI Data Analyst (PL 300)
  • Data Engineer*, Power BI (DP 500 *)
  • Artificial Intelligence (AI) Visuals
  • AI Enabled Power BI Features
  • Course - Lab Plan with Design Tools
  • Need for Power Query & DAX
  • Power BI Licensing Types
  • Power BI Cloud - Advantages
  • Power BI Report Server Advantages

Ch 7: POWER QUERY LEVEL 1

  • Power Query M Language Purpose
  • Power Query Architecture and ETL
  • Data Types, Literals and Values
  • Power Query Transformation Types
  • Table & Column Transformations
  • Text & Number Transformations
  • Date, Time and Structured Data
  • let, source, in statements @ M Lang
  • Get Data, Table Creations and Edit
  • ETL Operations with Power Query
  • Merge Transformations in Power BI
  • Join Kinds: Inner, Outer & Apply
  • Union All Transformation & Appends
  • Power Query Editor, Step Edits
  • Close & Apply Options. Report Design

Ch 13: DAX Functions - Level 1

  • DAX : Importance in Real-time
  • DAX Data Types, Syntax Rules
  • DAX Measures and Columns
  • ROW Context and Filter Context
  • Operators, Special Characters
  • DAX Functions, Vertipaq Engine
  • DAX Cheat Sheet : Expressions
  • Data Analytics with DAX
  • DAX Measures : Expressions
  • ISBLANK, IF, IN, SUM
  • SUMX, AVG, AVERAGEX
  • Data Models: Fact, Dimensions
  • Detecting Relations for DAX
  • Star & Snowflake Schemas
  • Data Modeling Options in DAX

Ch 2: Basic Report Design

  • Power BI Eco System: Architecture
  • Data Sources & Types in Real-world
  • Report Types: Interactive, Paginated
  • Analytical Reports & Mobile Reports
  • Data Sources : File, Database, Web
  • Visualizations : Report Shapes
  • Power BI Design Tools, Requirements
  • Power BI Desktop Tool : Installation
  • Desktop Interface: Overview, Canvas
  • Get Data, Data View, Report View
  • In-Memory Xvelocity Database
  • Basic Visuals: Table, Tree Map
  • Data Labels, Legend, Category
  • Local Store: PBIX & PBIT Files
  • Data Points and Tooltips

Ch 8: POWER QUERY LEVEL 2

  • Query Duplicate, Query Reference
  • Group By and Advanced Options
  • Aggregations with Power Query
  • Transpose, Header Promotion
  • Reverse Rows and Row Count
  • Data Type Changes & Detection
  • Replace Columns: Text, NonText
  • Advanced Query Edit Options
  • Replace Nulls: Fill Up, Fill Down
  • Pivot, Unpivot Transformations
  • Move Column and Split Column
  • Date & Time Transformations
  • Derive Year, Quarter, Month, Day
  • Add Column : Query Expressions
  • Query Step Inserts and Step Edits

Ch 14: DAX Functions - Level 2

  • Quick Measures in Power BI
  • Average and Filtered Average
  • Running Totals, EARLIER( )
  • RELATED, COUNTROWS
  • CALCULATE Function Conditions
  • ALL Members Scope & IN
  • Account and Time Calculations
  • Star Rating, DAX Expressions
  • Data Modeling Options in DAX
  • 1:1, 1:M and M:1 Relations
  • Working with Facts & Measures
  • Modeling : Missing Relations
  • Relationships & Importance
  • Modeling : Relation Management
  • Modeling with Multiple Keys

Ch 3: Visual Interaction, Visual Sync

  • Visual Interaction with Data Points
  • Disabling / Enabling Interactions
  • Edit Interactions: Format Options
  • Spotlight and Focus Mode
  • Report Export to CSV, PDF
  • Tooltip Options and Usage
  • Working with Pages in PBI
  • Rename, Duplicate, Hide Pages
  • Slicer Visual : Real-time Usage
  • Orientation, Selection Properties
  • Slicer Settings : Tiles & Slider
  • Single & Multi Select, Header
  • Number, Text, Show Summary
  • Date Slicer and Value Selections
  • Slicer List, Dropdowns & Clear

Ch 9: POWER QUERY LEVEL 3

  • Big Data Loads : Parameter Queries
  • Creating Parameters in Power Query
  • Parameter Data Types, Default Lists
  • Static & Dynamic Lists: List Queries
  • Convert Tables to Lists, Use Cases
  • Linking Parameters to Queries
  • Testing Parameters with Canvas
  • Multi-Valued Parameter Lists
  • Creating Lists in Power Query
  • Converting Lists to Table Data
  • Invoke Function, Type Conversions
  • Function Query & Parameter List
  • Columns From Examples, Indexes
  • Conditional Columns, Expressions
  • Disable / Enable Data Loads

Ch 15: DAX Functions - Level 3

  • DAX : Variables and Expressions
  • Dynamic Expressions, RETURN
  • Current Value, Previous Value
  • SELECTED VALUE, Joins
  • FORMAT Function with DAX
  • RELATED, Joins in DAX
  • DAX Expressions with SQL DB
  • Time Intelligence Functions
  • Date Dimension : Generation
  • CALENDAR(), DATESYTD()
  • TOTALYTD, TOTALQTD
  • TODAY, DATE, DAY with DAX
  • SELECTEDVALUE, FORMAT
  • Date, Time and Text Functions

Ch 4: Grouping & Hierarchies

  • Grouping : Visuals with Pdf Sources
  • List Grouping and Binning Options
  • Grouping Static / Fixed Data Values
  • Grouping Dynamic / Changing Data
  • Bin Size and Bin Limits (Max, Min)
  • Bin Count and Grouping Options
  • Group with Bins & Clustering
  • Group, Layer with Selection Pane
  • Creating Hierarchies in Power BI
  • Independent, Dependant Drill-Down
  • Drill-Down with Interactive Reports
  • Conditional Drilldowns, Data Points
  • Drill Up Buttons and Operations
  • Expand & Show Next Level
  • Dynamic Data Drills Limitations

Ch 10: POWER BI CLOUD - 1

  • Power BI Cloud Components
  • App Workspaces, Report Publish
  • Reports & Related Datasets Cloud
  • Creating New Reports in Cloud
  • Report Publish, Report Uploads
  • Report Edits and New Reports
  • Report Actions: Downloads
  • Dataset Usage Options in Cloud
  • Dashboards Creation and Usage
  • Pining Visuals and Report Pages
  • Visual Pin Actions in Dashboards
  • Dashboard & LIVE Interactions
  • Media Tiles: Images, Custom Links
  • Q & A Option with Dashboards
  • Pin with Q & A; Standard Visuals

Ch 16: DAX Functions - Level 4

  • RLS: Row Level Security
  • Data Models in Power BI Desktop
  • DAX Roles Creation and Testing
  • DAX Expressions & Operators
  • PBIX Uploads: Power BI Cloud
  • Dataset Security with DAX Roles
  • Entity Sets and Slicing in DAX
  • Dataflows with Power BI
  • Analytical Reports - DAX Usage
  • Creating Data Models with DAX
  • Datasets in Excel and Dashboards
  • Using Excel Analyzer in Power BI
  • Power BI Data Source in Excel
  • Connection Strings and Refresh
  • Analytical Reports - Limitations

Ch 5: Filters & Bookmarks

  • Filters : Types and Usage in Real-time
  • Visual Filter, Page Filter, Report Filter
  • Basic, Advanced and TOP N Filters
  • Category and Summary Level Filters
  • Data / Drill Options, DrillThru Filters
  • Keep All Filters" Options in DrillThru
  • CrossReport Filters, Include, Exclude
  • Drill-thru Filters, Page Navigations
  • Bookmarks : Report Navigations
  • Buttons, Images with Actions
  • Selection Pane, Actions, Text URLs
  • Show Data and See Records
  • Custom Tooltips, Table Visual
  • Table Vs Matrix : Drill-downs
  • Styles, Cell Properties, Databars
  • Conditional Formatting, Divergent

Ch 11: POWER BI CLOUD - 2

  • Report Actions : Share, Subscribe
  • Report Actions : Lineage, Embed
  • Report Actions : Export Options
  • Report Actions : Public User Access
  • Dashboard Actions : Share, Subscribe
  • Dashboard Actions : Themes, Lineage
  • Dashboard Actions : Share, Subscribe
  • Favorite, Insights, Embed Code
  • Gateways Configuration, PBI Service
  • Gateway Types, Cloud Connections
  • Gateway Cluster, Add Data Sources
  • Data Refresh : Manual, Scheduled
  • Power Query Parameters, Gateways
  • DataFlows, Power Query in Cloud
  • Lineage, Share, Subscribe, Insights
  • Performance Inspector& Gateways

Ch 17: Power BI Report Server

  • Power BI Report Server Config
  • SQL Server Instance Verifications
  • Report Server DB, Temp Database
  • WebService & WebPortal URL
  • Uploading Interactive Reports
  • End User Report Share (pdf)
  • Power BI Desktop RS Tool
  • Interactive Reports: Report Server
  • Mobile Reports : Design Options
  • Mobile Reports : Grids, Elements
  • Mobile Reports : Uploads, Edits
  • Paginated Reports : Deployments
  • Paginated Vs Interactive Reports
  • Paginated Vs Analytical Reports
  • Paginated Vs Mobile Reports
  • Power BI Report Server Vs Cloud

Ch 6: Big Data Access, Visuals

  • OLTP Databases, Big Data Sources
  • Azure Database Access, Reports
  • Import, Direct Query & Dual Mode
  • Data Modeling: Do Not Summarize
  • Data Modeling: Currency, Relations
  • Power BI Archtiecture, Eco System
  • Power BI Interface for Reports
  • Stacked Chart, Clustered Chart
  • Line Chart, Area Chart, Bar Chart
  • 100% Stacked Bar & Column Chart
  • Map Visuals: Tree, Filled, Bubble
  • Small Multiples, Legends, Axis
  • Cards, Funnel, Table, Matrix
  • Scatter Chart : Play Axis, Labels
  • Waterfall Chart, Multi Row Cards

Ch 12: POWER BI CLOUD - 3

  • Workbooks : Excel Online & Pins
  • Power BI Apps: Creation & Usage
  • Power BI Segments, Content
  • Navigation Screens, Audience
  • App Publish, Verification & Edits
  • Export, Share & Subscribe
  • List View & Lineage View Options
  • Power BI Scorecards: Realtime Use
  • Paginated Reports - Design & Usage
  • Power BI Report Builder Tool
  • Microsoft Report Builder Tool
  • Report Builder : Datasets, Charts
  • Report Builder : Bar Charts, Fields
  • Report Builder : Creating RDL Files
  • Paginated Reports : Deployments

Ch 18: Power BI Admin & AI

  • Power BI Cloud Management
  • Power BI Admin : Alerts
  • Workspace Management, Users
  • Security: Report, Dataset Levels
  • Security: Dataset, App Levels
  • Security: Workspace Options
  • PBI Performance Inspector
  • Power BI & Artificial Intelligence
  • Power BI & CoPilot Add-Ins
  • AI Visuals & Big Data Analytics
  • Smart Narrative and Q & A
  • Infographics, Icons and Labels
  • Key Influencer Visual in Power BI
  • Metrics Visual, Performance
  • Paginated Reports Visual

Power BI : Realtime Project (Sales - Retail)

Phase 1 : Basic Report Design

  • Project Requirement Analysis
  • Requirement Gathering, FSA
  • Report Design with Excel
  • Basic Data Modelling
  • Infographics, Histograms
  • Analytics and Formating

Phase 2 : SME Level

  • Report Design with SQL DB
  • SQL Database : Joins, Views
  • Dual Storage Mode, SQL Queries
  • Data Modeling, Power Query
  • Dynamic Connections, Azure DB
  • Parameters and M Lang Scripts

Phase 3: Deployments (Cloud, Server)

  • DAX Requriements, Analysis
  • Cloud and Report Server
  • Custom Visualizations
  • 3party Visuals & REST API *
  • Project FAQs and Solutions
  • One - One Resume, Mock Interview
 

Who can benefit from this Power BI Online Training course?

Complete Real-time and Practical Power BI Training with Real-time Scenarios. Power BI is a cloud-based, elegant end-to-end business analytics tool that enables anyone to visualize, analyze, forecast any type of data with greater speed, efficiency, and understanding. It connects users to a broad range of data through easy-to-use dashboards, interactive reports and compelling visualizations for your day to day corproate business data needs!

Job-Oriented Real-time Training @ SQL School Training Institute - Trainer :  Mr.Sai Phanindra (18+ Yrs of Exper)

SQL Server T-SQL, Azure SQL, Azure DBA, Azure BI, Azure Data Engineer, Power BI Training

 
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