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Course Highlights

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 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.

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Trainer: Mr. Guna Shekeran

Training Modes:

LIVE OnlineSelf Paced Videos

Training Highlights:

  • 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

Python Training
Course Contents:

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Module 1 : Python [Applicable for Python Plans A, B, C]

Part 1: Python Fundamentals

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 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 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 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 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 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()

Part 2: Python For Data Analytics – 1

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 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 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 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 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 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

Part 3: Python For Data Analytics – 2

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 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 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 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 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 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

Module 2 : SQL Server Basics, Queries, Stored Procedures and Database Development

Ch 1: SQL SERVER INTRODUCTION

  • Data, Databases and RDBMS Software
  • Database Types : OLTP, DWH, OLAP
  • Microsoft SQL Server Advantages, Use
  • Versions and Editions of SQL Server
  • SQL : Purpose, Real-time Usage Options
  • SQL Versus Microsoft T-SQL [MSSQL]
  • Microsoft SQL Server – Career Options
  • SQL Server Components and Usage
  • Database Engine Component and OLTP
  • BI Components, Data Science Components
  • ETL, MSBI and Power BI Components
  • Course Plan, Concepts, Resume, Project
  • 24 x 7 Online Lab for Remote DB Access
  • Software Installation Pre-Requisites

Ch 2: SQL SERVER INSTALLATIONS

  • System Configuration Checker Tool
  • Versions and Editions of SQL Server
  • SQL Server and SSMS Installation Plan
  • SQL Server Pre-requisites : S/W, H/W
  • SQL Server 2016 / 2017 Installation
  • SQL Server 2019 Installation
  • Instance Name and Server Features
  • Instances : Types and Properties
  • Default Instance, Named Instances
  • Port Numbers, Instance Differences
  • Service and Service Account Use
  • Authentication Modes and Logins
  • Windows Logins and SQL Logins
  • FileStream and Collation Properties
  • DBEngine and Replication Components

Ch 3: SSMS Tool, SQL BASICS – 1

  • SQL Server Management Studio
  • Local and Remote Connections
  • System Databases: Master and Model
  • MSDB, TempDB, Resource Databases
  • Creating Databases : Files [MDF, LDF]
  • Creating Tables in User Interface
  • Data Insertion & Storage. Limitations
  • SQL : Purpose and Real-time Usage
  • SQL Versus T-SQL : Basic Differences
  • DDL, DML, SELECT, DCL and TCL
  • Creating Tables using SQL Scripts
  • Data Storage, Inserts – Basic Level
  • Table Data Verifications with Select
  • SELECT Statement for Table Retrieval

Ch 4: SQL BASICS – 2

  • Creating Databases & Tables in SSMS
  • Single Row Inserts, Multi Row Inserts
  • Rules for Data Insertion Statements
  • SELECT Statement @ Data Retrieval
  • SELECT with WHERE Conditions
  • Batch Concept and Go Statement
  • AND and OR Operators Usage
  • IN Operator and NOT IN Operator
  • Between, Not Between Operators
  • LIKE and NOT LIKE Operators
  • UPDATE Statement & Conditions
  • DELETE & TRUNCATE Statements
  • Logged and Non-Logged Operations
  • ADD, ALTER and DROP Columns
  • ALTER & DROP Table Statements

Ch 5: SQL Basics – 3, T-SQL 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 Retreival and 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
  • Session Level, Connection Level Use

Ch 6 : CONSTRAINTS,INDEXES 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 and DEFAULT
  • Candidate Keys and Identity Property
  • Database Diagrams and ER Models
  • Relationships Verification and Links
  • Indexes : Basic Types and Creation
  • Index Sort Options, Search Advantages
  • Clustered and NonClustered Indexes
  • Primary Key and Unique Key Indexes
  • Need for Indexes – working with Keys

Real-time Case Study – 1 (Sales & Retail)

Objective : DB Design, Table Design, Relations
Involves Purchases, Products, Customers
and Time Data with Various Data Types.

Ch 7: JOINS, T-SQL Queries : Level 1

  • JOINS – Table Comparisons Queries
  • INNER JOINS For Matching Data
  • OUTER JOINS For (non) Match Data
  • Left Outer Joins with Example Queries
  • Right Outer Joins with Example Queries
  • FULL Outer Joins – Realtime Scenarios
  • Join Queries with “ON” Conditions
  • Join Unrelated Tables in SQL Server
  • NULL, IS NULL Operators in Joins
  • CROSS JOIN and CROSS APPLY
  • CROSS JOIN Versus CROSS APPLY
  • One-way & Two Way Data Comparisons
  • Important Join Queries in T-SQL
  • Join Options: Merge, Loop, Hash

Ch 8: Group By, T-SQL Queries Level 2

  • GROUP BY Queries and Aggregations
  • Group By Queries with Having Clause
  • Group By Queries with Where Clause
  • Using WHERE and HAVING in T-SQL
  • Rollup : Usage and T-SQL Queries
  • Cube : Usage and T-SQL Queries
  • UNION and UNION ALL Operator
  • EXISTS Operator, Query Conditions
  • Sub Queries and Alternatives to Joins
  • Using Joins with Group By Queries
  • Using Joins with Nested Sub Queries
  • Sub Queries with Joins and Group By
  • Using UNION and UNION ALL in Queries
  • Nested Sub Queries with Group By, Joins
  • Comparing WHERE, HAVING Conditions

Ch 9: JOINS, T-SQL QUERIES Level 3

  • GetDate, Year, Month, Day Functions
  • Date & Time Styles, Data Formatting
  • DateAdd and DateDiff Functions
  • Cast and, Convert Functions in Queries
  • String Functions: SubString, Relicate
  • Len, Upper, Lower, Left and Right
  • LTrim, RTrim, CharIndex Functions
  • MERGE Statement – Comparing Tables
  • WHEN MATCHED and NOT MATCHED
  • Incremental Load with MERGE Statement
  • IIF() Function for Value Compares
  • CASE Statement : WHEN, ELSE, END
  • ROW_NUMBER() and RANK() Queries
  • Dense Rank and Partition By Queries

Ch 10: View, SPs, Function Basics

  • Views : Types, Usage in Real-time
  • System Predefined Views and Audits
  • Listing Databases, Tables, Schemas
  • Functions : Types, Usage in Real-time
  • Scalar, Inline and Multi-Line Functions
  • System Predefined Functions, Audits
  • DBId, DBName, ObjectID, ObjectName
  • Variables & Parameters in SQL Server
  • Procedures : Types, Usage in Real-time
  • User & System Predefined Procedures
  • Parameters and Dynamic SQL Queries
  • Sp_help, Sp_helpdb and sp_helptext
  • sp_pkeys, sp_rename and sp_help
  • Important System Objects and Metadata
  • When to use Which Database Objects

Ch 11: 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
  • Auditting Triggers and Real World Use
  • Transactions : Types, ACID Properties
  • Transaction Types and AutoCommit
  • EXPLICIT & IMPLICIT Transactions
  • COMMIT and ROLLBACK Statements
  • Open Transaction Scenarios & Cause
  • Query Blocking Scenarios @ Real-time
  • NOLOCK and READPAST Lock Hints

Ch 12 : ER MODELS, NORMAL FORMS

  • Normal Forms for Entity Relationships
  • First Normal Form and Atomocity
  • Second Normal Form, Candidate Keys
  • 3rd Normal Form Multi Value Dependancy
  • Boycee-Codd Normal Form : BNCF
  • Fourth Normal Form Realtime Advantages
  • 1:1, 1:M, M:1, M:M Relationship Types
  • Joins with Group By Queries
  • Joins with Sub Queries, Formating
  • Office Data Connections, Excel Reports
  • Excel Pivot Reports and Reports
  • SQL Queries (Auto Generated) in BI Tools
  • FETCH OFFSET, NEXT ROWS, Order By
  • Data Refresh (Manual and Automated)

Real-time Case Study – 2 (Sales & Retail)

Objective : Query Writing, Excel Integration
Excel Pivot Tables, Pivot Charts,
Data Formatting, ODC Connections & Labelling

Ch 13: STORED PROCEDURES Level 2

  • Table Valued Parameters (TVP)
  • ReadOnly Parameters, Stored Procedures
  • Output Parameters, Stored Procedures
  • User Data Types and Real-time Use
  • Dynamic Data Insertions with SPs
  • Table Cloning, Inserts @ Table Variables
  • SQL Injection Attacks – Precautions
  • CTE : Common Table Expressions
  • Real-time Scenarios with CTEs – Usage
  • ROW_NUMBER() with CTE Queries
  • Using CTEs for Avoiding Self Joins
  • Using CTEs for Avoiding Sub Queries
  • Recursive CTEs and ANCHOR Element
  • Termination Checks in Recursive CTEs

Ch 14: STORED PROCEDURES – Level 3

  • DML Triggers and DDL Triggers
  • FOR and INSTEAD OF Triggers
  • Magic Tables : Inserted, Deleted
  • Views on Tables – SCHEMABINDING
  • ENCRYPTION and CHECK OPTION
  • Cascaded Views, Encrypted Views
  • Updatable Views, Joins with Triggers
  • Stored Procedures @ Triggers, Views
  • Cursors – Benefits, Cursors in SProcs
  • ForwardOnly, Scroll & Local Cursors
  • Static, Dynamic & Global Cursors
  • Keyset Cursors and @@FetchStatus
  • Nesting of Stored Procedures – Dynamic
  • Data Formatting and WHILE Loops
  • Using Temporary Tables for Formatting

Ch 15: XML,BLOB,FUNCTIONS Level 2

  • Functions : Types, Real-world Usage
  • Scalar Value Returning Functions
  • Inline Table Value Functions
  • Multi-Line Table Value Functions
  • WHILE Loops and Iterations in T-SQL
  • Table Variables Usage in T-SQL
  • Data Type Conversions with Functions
  • Composite Keys , Computed Columns
  • Self Referencing Keys, Self Joins
  • Adding Keys to Existing Tables
  • XML AUTO, XML RAW and XML PATH
  • BULK INSERT, BULKCOLUMN, JSON
  • OPENROWSET, PIVOT and UNPIVOT
  • JSON Files – Data Import into SQL DB

Ch 16: Server, DB Architecture

  • Server Architecture and Protocols
  • Database Engine and Query Processor
  • Parser, Optimizer, SQL & DB Manager
  • Storage Engine Components, SQL OS
  • File Manager and Database Files
  • Transaction Services, Buffer Manager
  • Lock Manager, IO Manager, MDAC
  • CLR, WAL, Lazy Writer, Checkpoint
  • Database Architecture – Data Files
  • Database Architecture – Log Files
  • Primary (mdf), Secondary Files (ndf)
  • Filegroups Usage, ReadOnly Filegroups
  • Database Files : Size and Location
  • Pages, Extents. Uniform, Mixed Extents
  • Transaction Log File [LDF], LSN, VLF

Ch 17 – 20: REAL-TIME PROJECT (BANKING)

Includes 2500 Lines of Code (COMPLETELY SOLVED).

Phase 1: DATABASE DESIGN

  • Understanding Project Requirements
  • End to End Project Work Flow
  • Naming Conventions in Real-time
  • Primary (mdf) and Secondary (ndf) Files
  • Table Schemas : Creation and Use
  • Implementing Normal Forms (OLTP)
  • Computed Columns and Data Types
  • SQL_Variant, Bit, sysname Data Types
  • Email and Phone Number Validations
  • Data Types Conversions, Validations

Phase 2: QUERY DESIGN

  • Joining Tables for Reports
  • Views with JOIN Options
  • Implementing Indexed Views
  • Using PIVOT Tables in Queries
  • Dynamic Conditions in Queries
  • Parameterized Queries in T-SQL

Phase 3: PROGRAMMING

  • Event Handling , Error Handling
  • Stored Procedures with Transactions
  • Error Handling, Event Handling Options
  • Transaction Nesting, Save Points
  • Stored Procedures with Tables
  • Stored Procedures with Views
  • Stored Procedures with Functions
  • Automating DML with Triggers
  • Project Deployments, Project FAQ

   Project Solution Explanation

   Resume Points from the Project

   Interview FAQs from Project

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

Plan A

1.Python

Plan B

1. SQL Server TSQL
2. Python
Total Duration4 Weeks7 Weeks
Python Introduction✔✔
Python Data Types✔✔
Variables, Expressions✔✔
If.. Else, While, For✔✔
Loops & Interactions✔✔
Python Classes, Objects✔✔
Python Modules, File IO✔✔
Python Dataframes✔✔
Pandas & Big Data✔✔
NumpPy & Statistics✔✔
Python IoT, Data Analytics✔✔
Python Programming✔✔
Python Controls✔✔
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✖✖
Total Course Fee ( Payable in Installments)*INR 9000USD 100INR 14000USD 150
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SQL Server & TSQL Schedules

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16 AM - 7 AMJan 28thMr. Sai Phanindra Register
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37 PM - 8 PMJan 6thMr. Sai PhanindraRegister
48 PM - 9 PMJan 20thMr. Sai PhanindraRegister

Python Training Schedules

S NoTime (IST, Mon - Fri)Start DateRegister
17 AM -8 AMJan 6thRegister
26 PM - 7 PMDec 23rdRegister
38:30 PM - 9:30 PMDec 23rdRegister

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Technical FAQs

Who is SQL School? How far you have been in the training services ?

SQL School is a registered training institute, established in February 2008 at Hyderabad, India. We offer Real-time trainings and projects including Job Support exclusively on Microsoft SQL Server, T-SQL, SQL Server DBA and MSBI (SSIS, SSAS, SSRS) Courses. All our training services are completely practical and real-time.CREDITS of SQL School Training Center

  • We are Microsoft Partner. ID# 4338151
  • ISO Certified Training Center
  • Completely dedicated to Microsoft SQL Server
  • All trainings delivered by our Certified Trainers only
  • One of the few institutes consistently delivering the trainings for more than 19+ Years online as inhouse
  • Real-time projects in
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    • Retail Sales
    • Telecom
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What is the Training Registration & Confirmation Process?

Upon submitting demo registration form and attending LIVE demo session, we need to receive your email confirmation on joining for the training. Only then, payment details would be sent and slot would be allocated subject to availability of seats. We have the required tools for ensuring interactivity and quality of our services.

Please Note: Slot Confirmation Subject to Availability Of Seats.

Will you provide the Software required for the Training and Practice?

Yes, during the free demo session itself.

How am I assured quality of the services?

We have been providing the Trainings – Online, Video and Classroom for the last 19+ years – effectively and efficiently for more than 100000 (1 lakh) students and professionals across USA, India, UK, Australia and other countries. We are dedicated to offer realtime and practical project oriented trainings exclusively on SQL Server and related technologies. We do provide 24×7 Lab and Assistance with Job Support – even after the course! To make sure you are gaining confidence on our trainings, participans are requested to attend for a free LIVE demo based on the schedules posted @ Register. Alternatively, participants may request for video demo by mailing us to contact@sqlschool.com Registration process to take place once you are happy with the demo session. Further, payments accepted in installments (via Paypal / Online Banking) to ensure trusted services from SQL School™

YES, We use Enterprise Edition Evaluation Editions (Full Version with complete feature support valid for SIX months) for our trainings. Software and Installation Guidance would be provided for T-SQL, SQL DBA and MSBI / DW courses.

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