#Data Science

Data Science is a multidisciplinary field that extracts insights and knowledge from structured and unstructured data. It combines statistics, programming, and domain expertise to analyze and interpret complex data patterns. Data Scientists help businesses make data-driven decisions through predictive models and visualizations. This field offers exciting career opportunities in AI, analytics, finance, healthcare, and tech industries

Data Science
Training Course Contents:

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Module 1 : Microsoft SQL (TSQL)

Ch 1: SQL SERVER INTRODUCTION

Database Introduction
Types of Databases
Need for & ETL, DWH
BI Implementations
SQL Server Advantages
Version, Editions of MSSQL
Data Analyst Job Roles

Ch 2: SQL SERVER INSTALLATIONS

SQL Server 2019, 2017
SSMS Tools Installation
Database Engine (OLTP)
SCM, Configuration Tools
Instance Types, Uses
Authentication Modes
Collation, File Stream

Ch 3: SQL BASICS – 1

Need for Databases, Tables
Need for SQL Commands
DDL, DML & DQL Statements
Database Creation @ GUI
Data Operations @ GUI
Session ID, SQL Context
DB, Tables, Data @ SQL

Ch 4: SQL BASICS – 2

DDL Variants in MSSQL
DML Variants in MSSQL
INSERT & INSERT INTO
SELECT & SELECT INTO
Basic Operators in SQL
Special Operators in MSSQL
ALTER, ADD, TRUNCATE, DROP

Ch 5: Data Imports, Schemas

Data Imports with Excel
ORDER BY & UNION
UNION ALL For Sorting Data
Creating, Using Schemas
Real-world Banking Database
Table Migrations @ Schemas
2 Part, 3 Part & 4 Part Naming

Ch 6 : Constraints, Index Basics

Need for Constraints, Keys
NULL, NOT NULL, UNIQUE
Primary Key & Foreign Key
RDBMS and ER Models
Identity Property, Default
Clustered Index, Primary Key
Non Clustered Index, Unique

Ch 7: Joins & Views Basics

JOINS: Purpose. Inner Joins
Left / Right / Full Outer Joins
Cross Joins, Query Tuning
Creating & Using Views
DML, SELECT with Views
RLS : WITH CHECK OPTION
System Views & Metadata

Ch 8: Functions(UDF), Data Types

Using Functions in MSSQL
Scalar Value Functions
Inline & Multiline Functions
Date & Time Functions
String, Aggregate Functions
Data Types : Integer, Char, Bit
SQL Variant, Timestamp, Date

Ch 9: Stored Procedures,Models

Stored Procedures & Usage
Creating, Testing Procedures
Encryption, Deferred Names
SPs for Validations, Analysis
System SPs, Recompilation
Normal Forms & Types
Data Models, Self-References

Ch 10: Triggers, Temp Tables

Need for Triggers
DDL & DML Triggers
Using Memory Tables
Data Replication, Automation
Local & Global Temp Tables
Testing & Using Temp Tables
SELECT .. INTO & Bulk Loads

Ch 11: DB Architecture, Locks

Planning VLDBs : Files, Sizing
Filegroups, Extents & Types
Log Files : VLF, Mini LSN
Table Location, Performance
Schemas, Transfer, Synonyms
Transactions Types, Lock Hint
Query Blocking Scenarios

Ch 12 : Cursors & CTEs, Links

Cursors : Realtime Use
Fetch & Access Cursor Rows
CTEs for SELECT, DML
CTEs: Scenarios & Tuning
Linked Servers, Remote Joins
Linked Servers: MSDTC, RPC
Tuning Remote Queries

Ch 13: Merge, Upsert & Rank

Need for Merge in ETL
Incremental Loads with SQL
MERGE and RANK Functions
Window Functions, Partition
Identify, Remove Duplicates

Ch 14: Grouping & Cube

Group By & HAVING
Cube, Rollup & Grouping
Joins with Group By
3 Table, 4 Table Joins
Query Execution Order

Ch 15: Self Joins, Excel Analysis

Self Joins & Self References
UNION, UNION ALL
Sub Queries with Joins
IIF, CASE, EXISTS Statements
Excel Analytics, Pivot Reports

Module 2: Power BI with AI

Ch 1 : Power BI Introduction

Reporting Basics & Types
Interactive,Analytical Reports
Paginated Reports (RDL)
Power BI Eco System
Power BI Tools,Service,Server
Need for Power Query (M)
Need for DAX & Cloud

Ch 2: Power BI Basic Reports

Power BI Desktop Installation
Basic Report Design (PBIX)
Data View, Data Models
Data Points, Aggregations
Focus Mode, Spotlight, Exports
ToolTip, PBIX and PBIT
Visual Interactions & Edits

Ch 3 : Grouping, Hierarchies

Creating Groups in Power BI
Groups : Creation & Usage
Group Edits Options
Bins & Bin Size, Bin Count
Hierarchies: Creation, Use
Drill Down, Drill Up
Conditional Drill Down

Ch 4 : Visual Sync, Filters

Slicer & Single Select
Multi Select Options
Integer, Character Slicers
Visual Sync with Slicers
Filters: Visual, Page, Report
Drill Thru Filters & Usage
Basic, Top & Advanced
Clear Filter Options, Resets

Ch 5 : Bookmarks, Big Data

Bookmarks Creation & Usage
Visual Interactions, Bookmarks
Images : Actions, Bookmarks
Big Data Access with Power BI
Storage Modes: Direct Query
Import & Performance Impact
Formatting & Data Refresh
Summary, Date Time Formats

Ch 6 : Power BI Visualizations

Chart and Bar Visuals
Line and Area Charts
Maps, TreeMaps, HeatMaps
Funnel, Card, Multrow Card
PieCharts & Settings
Waterfall, Sentiment Colors
Scatter Chart, Play Axis
Infographics, Classifications

Ch 7 : Power Query Level 1

Power Query (Mashup)
ETL Transformations in PBI
Power Query Expressions
Table Combine Options
Merge, Union All Options
Table Transformations

Ch 8 : POWER QUERY LEVEL 2

Any Column Transformations
String / Text Transformations
Numeric Analytics & Mashup
Date Time Transformations
Add Column Transformations
Expressions and New Columns

Ch 9 : POWER QUERY LEVEL 3

Parameters in Power Query
Static Parameters, Defaults
Dynamic Dropdowns, Lists
Linking with Table Queries
Column From Examples
Step Edits, Type Conversions

Ch 10 : Power BI Cloud – 1

Power BI Cloud Concepts
Workspace Creation, Usag
Report Publish & Edits
Semantic Models in Realtime
Dashboard Creation, Usage
Clone, Share, Subscribe
Q&A, Lineage, Settings

Ch 11 : Power BI Cloud – 2

Data Gateways, Data Refresh
Data Source Configurations
Data Refresh & Scheduling
Gateway Optimizations
Semantic Model Optimizations
Report Optimizations
Dashboard Optimizations

Ch 12 : Power BI Cloud – 3

Power BI Apps, Shares
App Sections & Options
App Updates, Security
Excel Analytics
Data Explorer Option
Sharing, Subscriptions
Alerts, Metrics, Insights

Ch 13 : Report Server & DAX

Power BI Report Server
Report Database, TempDB
Web Service & Server URL
Paginated Reports (RDL)
Report Builder Tool Usage
DAX : Purpose, Realtime Use

Ch 14: DAX Level 2

DAX Measures Creation, Use
DAX Functions: IIF, ISBLANK
SUM, CALCULATE Functions
DAX Cheat Sheet : Examples
Quick Measures in Power BI
Running Totals, Filters

Ch 15 : DAX Level 3

Star Rating Calculations
Data Models & DAX
Star & Snowflake Schemas
Dimensions, Fact Tables
DAX Expressions & Joins
DAX Variables, Usage

Ch 16 : DAX Level 4

Dynamic Report with DAX
SELECTED MEMEBER
Time Intelligence with DAX
PARALLELPERIOD, DATE
DAX with Big Data
Big Data Analytics

Ch 17 : Realtime Project Phase 1

Project Requirement Spec
Understanding Data, Formats
Report Pattern Design
Report Design & Modelling
Power Query, DAX, Insights
Analytical Reports in Cloud

Ch 18 : Realtime Project Phase 2

Complete Project Solution
Project FAQs, Key Roles
Real-world Considerations
Power BI Admin Concepts
Resume Points, FAQs
PL 300 Exam Guidance

Module 3: Python

Ch 1. Python Introduction

Need for Data Analytics
Python in Data Analysis
History of Python
Python Versions
Python Implementations
Python Installations
Python IDE & Usage
Jupyter Notebooks

Ch 2. Python Basics, Architecture

Python Scripting Options
Basic Operations in Python
Python Scripts, Print()
Single, Multiline Statements
Adding Cells, Saving Notebook
Single, Multi Line Comments
Python : Internal Architecture
Compiler Versus Interpreter

Ch 3. Data Types & Variables

Integer / Int Data Types
Float & String Data Types
Boolean, Binary Types
Sequence Types: List, Tuple
Range, Complex & memview
Retrieving Data Type: type()
Multi Assignments & Casting
Unpack Collection, Outputs

Ch 4. Python Operators

Arithmetic, Assignment Ops
Comparison Operators
Logical, Identity Operators
Member, Bitwise Operators
Operator Precedence
If … Else Statement, Pass
Short Hand If, OR, AND
ELIF and ELSE IF Statements
Expressions, Ternary OPs

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 & For Loops
Break, Continue & Range
__iter__() and __next__()
iter() and Looping Options

Ch 6: Python Collections

Python Collections (Arrays)
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 7: Python Functions

Python Functions & Usage
Function Parameters
Arguments, **kwargs
Default & List Parameters
Python Lambda Functions
Anonymous Functions
Recursive Functions, Usage
Return & Print @ Lamdba

Ch 8: Python Classes & Arrays

Python Classes & Objects
__init__() Function
__str__() Function
Self Parameters & Objects
Python Inheritance & Classes
Parent & Child Classes
__init__() & super() Function
Polymorphism in Python

Ch 9: Python Modules

import Python Modules
Variables in Modules
Built In Modules & dir
datetime module in Python
Date Objections Creation
strftime Method & Usage
imports & datetime.now()
Using Python Constructors

Ch 10: : Python JSON & RegEx

JSON Concepts, Usage
Dictionary & import json
Python Objects into JSON
Formatting & Ordering
json.dumps, print options
Python Regular Expressions
RegEx Module & Function
search() & span() , Strings
Using RegEx with JSON

Ch 11: Python User Inputs & TRY

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

Ch 12: Python File Handling

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

Ch 13: Data Analytics – Pandas

Python Modules & Pandas
Pandas Codebase & Usage
Installation of Pandas
import pandas.DataFrame
Checking Pandas Version
Pandas Series, arrays
Labels : Creation, Use
series(), print()

Ch 14: Data Analytics – DataFrames

Indexes & Named Options
Locate Row and Load Rows
Row Index & Index Lists
Load Files Into a DataFrame
pd.read_csv() Function
pd.options.display.max_rows
df.to_string() Function
tail() & null() Function

Ch 15: Data Analytics – Pandas

Pandas – Cleaning Data
Replace, Transform Columns
Data Discovery & Column Fill
Identify & Remove Duplicates
dropna(), fillna() Functions
Pandas – Data Correlations
Good & Bad Correlation
Data Plotting & matlib Lib

Ch 16: : SQL Server & Python – 1

SQL & Databases
Azure Data Studio Tool
sp_execute_external_script
Input Data & Result Sets
DDL & DML with Python
SQL_out, SQL_in
Variables & Parameters
Versions, Package List
WITH RESULT SETS Options

Ch 17: SQL Server & Python – 2

pandas.Series with SQL DBs
Indexing Methods in Realtime
Convert series to data frame
Output values into data.frame
pymssql package in SQL Server
pip list & Package Manager
Python runtime, Py Package
pymssql.connect & Usage
Cursor Variables & Usage

Ch 18: Power BI with Python

Using Python Script Visual
PyScript Options & Tuning
Settings, Labelling Options
Running and Testing Scripts
Data Validations in Power BI
Power BI: ipynb Scripts
Interactive Reports
Data Formatting with Python
End to End Realtime Projects

SQL SCHOOL

24x7 LIVE Online Server (Lab) with Real-time Databases.
Course includes ONE Real-time Project.

Data Science Training FAQs

What is Data Science Job Role?

A Data Scientist collects, cleans, analyzes, and interprets large amounts of data to extract valuable insights. This role involves working with tools like Python, SQL, Pandas, NumPy, Scikit-learn, and more to build predictive models, perform data visualization, and support data-driven decision-making in organizations.

What are the Job Roles for Data Scince?

Data Scientist
Data Analyst
Machine Learning Engineer
Data Engineer
Business Intelligence (BI) Analyst
AI Engineer and more .. !

What does our Data Science Training course contains?

The course is carefully curated with below modules:
👉🏻Module 1: Microsoft SQL (TSQL)
👉🏻Module 2: Power BI
👉🏻Module 3: Python

Who can join this course?

This course is ideal for:

Fresh graduates or students looking to enter the field of AI/ML/Data
Professionals from any domain wanting to shift into a data-oriented role
Analysts, Developers, and Engineers seeking upskilling in Data Science

No prior coding experience is required. All concepts are taught step-by-step from basics to advanced level.

What training modes are available?

Option 1: LIVE Online Training (100% Interactive, step by step, assignments)

Option 2: Self Paced Videos (100% practical, step by step with concept wise assignments)

You may choose any one of these options, same curriculum!

I (Trainer) shall be available for doubts and clarifications, assignment check and review.

Why should I choose SQL School for Data Science training?

👉🏻 Every session is Practical, Step by Step with Concept wise FAQs !!

👉🏻 100% results with on-time practice. Daily Tasks for every session.

👉🏻 Concept wise tasks be submitted before next class for Job Waiters / Starters.

👉🏻 Concept wise tasks due for submission by Weekends for Working Professionals.

Why Choose SQL School

100% Real-Time and Practical
ISO 9001:2008 Certified
Concept wise FAQs
TWO Real-time Case Studies, One Project
Weekly Mock Interviews
24/7 LIVE Server Access
Realtime Project FAQs
Course Completion Certificate
Placement Assistance
Job Support
Realtime Project Solution
MS Certification Guidance