#Data Science Engineer

A Data Science Engineer specializes in transforming raw data into meaningful insights using statistics, machine learning, and advanced analytics. They design, build, and deploy data-driven models and pipelines that power intelligent applications, making data science a critical capability for decision-making, automation, and AI-driven solutions across industries.

✅ Python Programming & Data Analysis
✅ SQL for Analytics & Data Modeling
✅ Data Cleaning & Feature Engineering
✅ Machine Learning Algorithms
✅ Supervised & Unsupervised Learning
✅ Big Data with Spark & PySpark
✅ Azure Databricks & Cloud Data
✅ ML Ops & Model Deployment
✅ Power BI Analytics & AI Insights
✅ End-to-End Real-Time Project

Module 1: SQL Server TSQL (MS SQL) Queries

Ch 1: Data Engineer Job Roles

Introduction to Data Engineer
Data Science Engineer Job Roles
Data and Databases Concepts

Ch 2: Database Intro & Installations

Database Types (OLTP, DWH, ..)
DBMS: Basics
SQL Server 2025 Installations
SSMS Tool Installation
Server Connections, Authentications

Ch 3: SQL Basics V1 (Commands)

Creating Databases (GUI)
Creating Tables, Columns (GUI)
SQL Basics (DDL, DML, etc..)
Creating Databases, Tables
Data Inserts (GUI, SQL)
Basic SELECT Queries

Ch 4: SQL Basics V2 (Commands, Operators)

DDL: Create, Alter, Drop, Add, modify, etc..
DML: Insert, Update, Delete, select into, etc..
DQL: Fetch, Insert… Select, etc..
SQL Operations: LIKE, BETWEEN, IN, etc..
Special Operators

Ch 5: Data Types

Integer Data Types
Character, MAX Data Types
Decimal & Money Data Types
Boolean & Binary Data Types
Date and Time Data Types
SQL_Variant Type, Variables

Ch 6: Excel Data Imports

Data Imports with Excel
SQL Native Client
Order By: Asc, Desc
Order By with WHERE
TOP & OFFSET
UNION, UNION ALL

Ch 7: Schemas & Batches

Schemas: Creation, Usage
Schemas & Table Grouping
Real-world Banking Database
2 Part, 3 Part & 4 Part Naming
Batch Concept & “Go” Command

Ch 8: Constraints, Keys & RDBMS – Level 1

Null, Not Null Constraints
Unique Key Constraint
Primary Key Constraint
Foreign Key & References
Default Constraint & Usage
DB Diagrams & ER Models

Ch 9: Normal Forms & RDBMS – Level 2

Normal Forms: 1 NF, 2 NF
3 NF, BCNF and 4 NF
Adding Keys to Tables
Cascading Keys
Self Referencing Keys
Database Diagrams

Ch 10: Joins & Queries

Joins: Table Comparisons
Inner Joins & Matching Data
Outer Joins: LEFT, RIGHT
Full Outer Joins & Aliases
Cross Join & Table Combination
Joining more than 2 tables

Ch 11: Views & RLS

Views: Realtime Usage
Storing SELECT in Views
DML, SELECT with Views
RLS: Row Level Security
WITH CHECK OPTION
Important System Views

Ch 12: Stored Procedures

Stored Procedures: Realtime Use
Parameters Concept with SPs
Procedures with SELECT
System Stored Procedures
Metadata Access with SPs
SP Recompilations

Ch 13: User Defined Functions

Using Functions in MSSQL
Scalar Functions in Real-world
Inline & Multiline Functions
Parameterized Queries
Date & Time Functions
String Functions & Queries
Aggregated Functions & Usage

Ch 14: Triggers & Automations

Need for Triggers in Real-world
DDL & DML Triggers
For / After Triggers
Instead Of Triggers
Memory Tables with Triggers
Disabling DMLs & Triggers

Ch 15: Transactions & ACID

Transaction Concepts in OLTP
Auto Commit Transaction
Explicit Transactions
COMMIT, ROLLBACK
Checkpoint & Logging
Lock Hints & Query Blocking
READPAST, LOCKHINT

Ch 16: CTEs & Tuning

Common Table Expression
Creating and Using CTEs
CTEs, In-Memory Processing
Using CTEs for DML Operations
Using CTEs for Tuning
CTEs: Duplicate Row Deletion

Ch 17: Indexes Basics, Tuning

Indexes & Tuning
Clustered Index, Primary Key
Non Clustered Index & Unique
Creating Indexes Manually
Composite Keys, Query Optimizer
Composite Indexes & Usage

Ch 18: Group By Queries

Group By, Distinct Keywords
GROUP BY, HAVING
Cube( ) and Rollup( )
Sub Totals & Grand Totals
Grouping( ) & Usage
Group By with UNION
Group By with UNION ALL

Ch 19: Joins with Group By

Joins with Group By
3 Table, 4 Table Joins
Join Queries with Aliases
Join Queries & WHERE, Group By
Joins with Sub Queries
Query Execution Order

Ch 20: Sub Queries

Sub Queries Concept
Sub Queries & Aggregations
Joins with Sub Queries
Sub Queries with Aliases
Sub Queries, Joins, Where
Correlated Queries

Ch 21: Cursors & Fetch

Cursors: Realtime Usage
Local & Global Cursors
Scroll & Forward Only Cursors
Static & Dynamic Cursors
Fetch, Absolute Cursors

Ch 22: Window Functions, CASE

IIF Function and Usage
CASE Statement Usage
Window Functions (Rank)
Row_Number( )
Rank( ), DenseRank( )
Partition By & Order By

Ch 23: Merge(Upsert) & CASE, IIF

Merge Statement
Upsert Operations with Merge
Matched and Not Matched
IIF & CASE Statements
Merge Statement inside SPs
Merge with OLTP & DWH

Module 2: Azure Data Engineer (ADF, Synapse)

Part 1 (ADF, Synapse)

Ch 1: ETL, DWH Introduction

Data Warehouse (DWH)
Cloud Concepts: IaaS, PaaS
SaaS & Azure Cloud Concepts
Azure Resources & Groups
Storage, ETL, IoT Resources

Ch 2: Azure Intro, Azure SQL

Azure SQL Server, SQL DB
Azure SQL Database (OLTP)
Azure SQL Pool (DWH)
Connections from SSMS Tool
Connections from ADS Tool
Pause / Resume SQL Pool
Source Data Configurations

Ch 3: Azure Synapse (DWH)

Synapse Pool Architecture
Control Node, Compute Node
DMS & Partitioned Tables
Creating Tables with TSQL
Distributions: RR, Hash, Repl
Big Data Loads with TQL
Important DMFs & DMVs

Ch 4: Azure Data Factory (ADF)

Need for ADF & Pipelines
Data Orchestration with IR
Integration Runtime Engine
Linked Services, Datasets
Pipelines: Copy Data Activity
Data Flow Activity with IR

Ch 5: Azure SQL DB Loads

ADF: Author
Azure SQL Database Reads
Azure SQL Pool Writes
Synapse Analytics with IR
Pipeline Design, Validation
Pipeline Runs, Monitoring

Ch 6: BLOB Data Loads

Azure Storage Account
Azure BLOB Containers
BLOB Storage in ADF
Synapse Analytics with IR
ADF Pipeline Edits
Pipeline Runs, Monitoring

Ch 7: Pipeline Settings

ADF Pipeline Settings
Staging : Advantages
Reliable Logging
Best Effort Logging
DIU & DOCP with IR
Compressions, Health Check

Ch 8: File Incremental Loads

File Incremental Loads
Storage Account, Data Lake
Binary Copy, Schema Drift
Staging Concept in ADF
Initial, Incremental Loads
Schema & Data Changes

Ch 9: Table Incremental Loads

Implement SCD with ADF
Self Hosted IR: Realtime Use
On-premise Data: Incr Loads
Copy Method: Upsert, Keys
Staging & ADF Optimizations
Pipeline Runs, Activity IDs

Ch 10: ADF Data Flow – 1

Data Flow Transformations
Spark Clusters for Debugging
Optimized Clusters, Preview
Conditional Split, SELECT
Sort, Union Transformations
Pipelines with Data Flow

Ch 11: ADF Data Flow – 2

Working with Multiple Tables
Join Transform, Broadcast
Row Filters, Column Filters
Surrogate Keys, Derived Cols
ETL Loads Dates, Sink Options
Aggregated Data Loads

Ch 12: ADF Data Flow – 3

Pivot Transformation
Group By & Pivot Keys
Column Pattern, Deduplicate
Lookup, Cached Lookup
Tuning Transformations
Tuning Data Flow, Spark

Ch 13: ADF Data Flow – 4

Lookup Transformation
Cache Lookup
Inline Datasets
Data Validations
Lookup Versus Joins
Broadcast Options

Ch 14: ADF Metrics, Alerts

Azure Insights
Azure Metrics for ADF
Azure Metrics for Synapse
CPU, Memory Metrics
Alerts and Notifications
Action Groups, Tuning Options

Ch 15: ADF Parameters, Security

Linked Service Parameters
Creating Logins
Users and ETL Permissions
Parameterize Logins
Parameterize Users
Dynamic Linked Services

Ch 16: Parameters, SCD & ETL

ADF Templates in Realtime
Implementing ADF SCD
Table Incremental Loads
Control Tables, Watermarks
Pipeline Parameters, SPs
Dynamic Data Sets, SCD

Ch 17: CDC with ADF

Using CDC in ADF
Control Tables (CT): Upserts
Handling Inserts, Updates
Change Tracking (CT) Tables
SCD Type 1 & Type 2
ADF, Synapse: Limitations

Ch 18: Synapse Analytics – 1

Azure Synapse Analytics
Dedicated SQL Pools
TSQL: Stored Procedures
Synapse Pipelines, Tuning
SP Activity in Pipelines, Jobs
Comparing ADF & Synapse

Ch 19: Synapse Analytics – 2

Serverless Pools in Synapse
TSQL Scripts with Serverless
ADLS Data Imports with SPs
Synapse Analytics
Synapse Optimizations
Synapse Security & Logins

Ch 20: Synapse Analytics – 3

Synapse Notebooks
Synapse Analytics with Pools
Staging, Aggregations
Pipelines with Notebooks
Pipelines Vs Notebooks
Scheduling Notebooks

Ch 21: CI CD with GitHub

Creating Github Account
GIT: Main, Branches
Connecting with ADF
Version Changes
Builds and Deployments
CI-CD Integrations

Part 2 (Azure IoT, ADLS, ASA Jobs)

Ch 22: Azure Intro & Storage

Storage, ETL, IoT Resources
Azure Storage Components
Azure Storage Account, HNS
Azure Data Lake Storage
Storage Explorer Config

Ch 23: Azure Storage Operations

BLOB Storage: Containers
File & Folder Uploads, Edits
Azure Tables: Row Key
Partition Key, Timestamp
Use Cases of Azure Tables

Ch 24: Azure Storage Security

Access Keys & Admin Access
SAS Keys Generation, Ips
Azure AD Users, Groups
IAM & RBAC with Entra Users
ACLs and ADLS Security

Ch 25: Azure SQL DB Migrations

On-Premise SQL DB bacpac
Azure SQL Deployment
Azure Storage from SSMS
Azure SQL DB Migration
Migration Verifications
Testing Migrations in SQL

Ch 26: Azure Tables & Files

Azure Tables
Entities and Properties
Storage Service Operations
Storage Browser Operations
OData Queries & Filters
Azure BLOB Vs Azure Tables

Ch 27: Azure Stream Analytics

Azure IoT Hubs & Devices
APIs with Connection Strings
Azure Steam Analytic Jobs
Inputs, Outputs, SAQL Query
LIVE Feed: JSON, AVRO Files
Watermark & LIVE Streams

Ch 28: Azure Event Hubs

Azure Event Hubs
Namespaces & Instances
Instance Configurations
Access Policies & Security
Event Based Data Ingestions
IoT Hubs Versus Event Hubs

Ch 29: Azure Key Vaults

Azure Encryptions at REST
SMK & CMK Encryptions
Azure Key Vaults & Keys
Key Access Policies
Rest, Transit Encryptions
Realtime Considerations

Ch 30: Access Tiers & Blob Types

Azure Access Tiers
Hot, Cold, Cool & Archive
Block & Page Blobs
Append Blobs
Storage Snapshots
Version Checks @ Snapshots

Ch 31: Azure Metrics & Alerts

Azure Encryptions @ REST
Azure Key Vaults & Keys
SMK & CMK Encryptions
Azure Metrics: Ingress
Egress, E2E Latency Issues
Performance Tuning Options

Ch 32: Storage Optimizations

BLOB Types & Content Types
Hot, Cool, Cold, Archive Types
Creating, Using Access Policies
Immutable Storage, Rotation
Containerization, Indexing
Replication: LRS, ZRS, RA-GRS

Ch 33: Azure Pricing, Functions

Azure Logic Apps: Usage
Log Apps Usage in ETL
Snapshots, Azure Functions
Azure Functions Realtime Use
ETL & DWH with Functions
Azure Resource Pricing

Part 3 (Databricks, AutoLoader, DLT, PySpark)

Ch 34: Databricks Intro & Architecture

Databricks Introduction
Databricks Features
Key Components, Architecture
Control Pange, Compute Pane
Azure Databricks Resource
Databricks Workspace

Ch 35: Spark Cluster Architecture

Spark Clusters: Types, Policies
Driver Node: Purpose, Compute
Worker Node: Purpose, Compute
Cluster Manager, Cluster Types
Resilent Distributed Datasets
DAG, Scaling, Photon Acceleration

Ch 36: DBFS Operations

DBFS Concepts: File Store, Tables
DBFS File Uploads, Infer Schema
Header Row Promotion
Create Table using UI
HIVE Metastore Catalog
Spark Database & Tables

Ch 37: Notebooks Intro

ETL & ELT Process
Workspace Options: Notebooks
Notebooks: SQL, Python, Scala
When to use which Notebooks?
Notebook Exports, Imports
Cloning and Markdown Cells

Ch 38: Spark SQL Notebooks

Creating Spark Databases
Connecting to Spark Databases
Creating Spark tables
Data Inserts & DML Operations
DDL Operations on Spark Tables
SQL Notebook: Limitations

Ch 39: Medallion Architecture

Medallion Architecture: Scaling
Raw Data with Medallion
Transformations (ETL)
Bronze Layer: Raw Data
Silver Layer with Temp Views
Gold Layer with Spark Tables

Ch 40: Python Intro, Variables

Python Introduction
Python Usage in ETL, DDL, DML
Spark Environment for Python
PySpark: Python inside Spark
Python Variables
Python Print Statement

Ch 41: Python Data Types

Python Data Types
String, Numeric Types
Date Data Type
List, Tuple & Sets
Data frames: Purpose
Data frames as Spreadsheets

Ch 42: PySpark Transformations

Custom DataFrames
Single List, Mixed List Options
Concat Function & Index Options
Removing Empty Rows
Replacing Null Values
Merge, Joins, Join Kind

Ch 43: Medallion with DBFS

DBFS File Source & DataFrames
Temp View For Medallion
Dataframe Loads to TempView
Data Filters in Temp View
Data Aggregations in Temp View
Creating Parquet Tables

Ch 44: Medallion with Azure SQL DB

Azure SQL DB Connections
Azure SQL Server & DB Names
Connection String & URL Format
Dataframes @ spark.read.jdbc()
Aggregated / Incremental Tfns
Data Loads into Spark Database

Ch 45: Delta Tables (PySpark)

Delta Tables Concept
Creating, Using Delta Tables
DML Operations in Delta Tables
Upsert: Incremental Loads
Delta Tables in HIVE Metastore
MERGE INTO Statement (Spark)

Ch 46: SCD with Azure SQL DB

Delta Tables: Upsert Activity
Reading Azure SQL DB Tables
Temp Views with Upserts
Upsert: Incremental Loads
MERGE INTO Statement (Spark)
SCD with Azure SQL DB (OLTP)

Ch 47: Python Widgets (PySpark)

Widgets: Notebook Parameters
dbutils.widgets.text()
dbuitls.widget.get()
Reading Widgets into Variables
Using Variables in Notebook
Aggregated Loads with Widgets

Ch 48: Workflows (PySpark)

Python Notebook Schedules
Adding Tasks to Jobs
Job Clusters & Cluster Sizes
High Performance Cluster
Job Notifications, Verifications

Ch 49: Unity Catalog

Unity Catalog & Big Data Storage
Unity Catalog Connectors
Catalog Explorer, HIVE
Ubuntu VM: Azure Resource
Cluster Size & VM Size Options
Default Spark Database, Usage

Ch 50: Delta Lake

Declarative Frameworks
Streaming Data Handling
Batch Data Handling
Data Quality & Data Lineage
Medallion with Delta Lake
Time Travel with Delta Lake

Ch 51: Auto Loader in Delta Lake

Structured Streaming Source
Incremental File Uploads
Read, Write Stream (OLTP)
Upsert: Incremental Loads
Data Ingestions & List Modes
File Notification, CheckPoints

Ch 52: Delta LIVE Tables (DLT)

Delta LIVE Tables: Scope
Creating DLT Tables
Streaming Tables
Lake Flow Concepts
Materialized Views
Pipeline Graphs, Lake Flow

Ch 53: Security

RBAC Concepts: IAM Roles
Databricks Workspace Security
Notebook Job Level Security
Job Level Security, Sharing
JDBC Connections: Server Host
Access Tokens & API Access

Ch 54: Scala Notebooks – V1

Scala Notebooks: Realtime Use
JVM and Scala Notebooks
Data Frames, Temp Views
Creating Temp Tables in Scala
Medallion with DBFS, SQL DB
Parquet Tables & Delta Tables

Ch 55: Azure Data Engineer (Integrations, Projects)

👉🏻ADF with Storage Tables

👉🏻ADF with Databricks, Azure Storage

👉🏻Microsoft Fabric Concepts

👉🏻Fabric Configurations

👉🏻Azure Versus Fabric Implementations

👉🏻Azure to Fabric Migrations

👉🏻Realtime Project For your Resume: End to End Project

👉🏻Databricks Data Engineer: Certification Guidance

Module 3: Power BI

Ch 1: Power BI Intro, Installation

Power BI & Data Analysis
5 Design Tools, 3 Techniques
2 Hosting Solutions
Power BI with Co-Pilot & AI
Power BI Installation

Ch 2: Report Design Concepts

Basic Report Design (PBIX)
Get Data, Canvas (Design)
Data View, Data Models
Data Points, Spotlight
Focus Mode, PDF Exports

Ch 3: Visual Interactions, PBIT

Visual Interactions & Edits
Limitations with Visual Edits
Creating Power BI Templates
CSV Exports & PBIT Imports

Ch 4: Grouping, Hierarchies

Creating Groups : Lists
Creating Groups: Bins
List Items & Group Edits
Bin Size & Bin Count

Ch 5: Slicer & Visual Sync

Slicer Visual in Power BI
Slicer: Format Options
Single Select, Multi Select
Slicer: Select All On / Off
Visual Sync with Slicers

Ch 6: Hierarchies & Drill-Down

Hierarchies: Creation, Use
Hierarchies: Advantages
Drill Up, Drill Down
Conditional Drill Down
Filtered Drill Down, Table View

Ch 7: Filters & Drill Thru

Power BI Filters
Basic, Top & Advanced
Visual Filters, Page Filters
Report Level Filters, Clear Filter
Drill Thru Filters & Usage

Ch 8: Bookmarks, Buttons

Power BI Bookmarks
Images: Actions, Bookmarks
Buttons: Actions, Bookmarks
Page to Page NavigationsScore Cards, Master Pages

Ch 9: SQL DB Access & Big Data

SQL DB Access, Queries
Storage Modes: Direct Query
Formatting & Date Time
Storage Modes in Power BI
Azure (Big Data) Access & Formatting

Ch 10: Power BI Visualizations

Charts, Bars, Lines, Area
TreeMaps & HeatMaps
Funnel, Card, Multrow Card
PieCharts & Waterfall
Scatter Chart, Play Axis
Infographics, Classifications

Ch 11: Power Query Introduction

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

Ch 12: Power Query : Table Tfns

Table Duplicate, Header Promotion
Group By Transformation
Aggregate, Pivot Operation
Reverse Rows, Count Rows
Advanced Power Query Mode

Ch 13: Power Query: Column Tfn

Any Column Transformations
Data Type Detection, Change
Rename, Replace, Move
Fill Up, Fil Down
Step Edits & Rollbacks

Ch 14: Power Query: Text, Date

String / Text Transformations
Split, Merge, Extract, Format
Numeric and Date Time
Add Column & Expressions
Expressions and New Columns
Column From Examples

Ch 15: Power Query: Parameters

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

Ch 16: Power BI Cloud: Publish

Power BI Cloud Concepts
Workspace Creation, Usage
Report Publish Cloud
Report Edits in Cloud
Semantic Models & Usage

Ch 17: Power BI Cloud Dashboards

Power BI Dashboards
Dashboard Creation, Usage
Pin Visuals, Pin LIVE Pages
Add Image, Video Tiles
Q&A & Pin Tiles

Ch 18: Power BI Cloud Operations

Report Shares, Alerts
Subscriptions, Exploration
Downloads & Edits
Report Cloning in Cloud
QR Codes, Web Publish
Lineage & Metrics

Ch 19: Power BI Cloud Gateways

Data Gateways, Data Refresh
Install, Configure Gateways
Data Sources Configurations
Data Refresh & Scheduling
Gateway Optimizations

Ch 20: Power BI Cloud Apps

Power BI Apps: Creation
App Sections & Content
Audience Options
App Security & Sharing
App Updates, Favourites
App URL, End User Access

Ch 21: Power BI Report Server

SQL Server 2025 (Mandatory Installations)
Power BI Report Server
Report Server Vs Cloud
Installation, Configuration
RS Config Tool Options
Report Database, TempDB
Web Service & Server URL

Ch 22: Paginated Reports

Report Builder Tool
Paginated Report (RDL)
Report Expressions (RDL)
Tablix, Chart Wizards
Fields & Drill-Down
RDL Report Publish

Ch 23: DAX Concepts (Basics)

DAX Concepts: Intro & Realtime Need
DAX Columns: Creation, Use
DAX Measures: Creation, Use
DAX Functions: IIF, ISBLANK
SUM, CALCULATE Functions
DAX Cheat Sheet

Ch 24: DAX Quick Measures

Quick Measures in Power BI
Average & Filters
Running Totals
Star Rating Calculations
DAX Measures in Data View
DAX in Visuals
DAX in Cloud Reports

Ch 25: Data Modelling, DAX

Dimensions Tables
Fact Tables & DAX Measures
Data Models & Relations
DAX Expressions
Star & Snowflake Schemas
DAX Joins & Expressions

Ch 26: DAX Joins, Variables

CALCULATEX & Variables
COUNT, COUNTA, etc..
SUM, SUMX, etc..
SELECTED MEMEBER
Filter Context, RETRUN
Dynamic Report with DAX

Ch 27: DAX Time Intelligence

Need for Time Intelligence
Date Table Generation
Time Intelligence with DAX
PARALLELPERIOD, DATE
CALENDAR, Total Functions
YTD, QTD, MTD with DAX

Ch 28: DAX – Row Level Security

RLS: Row Level Security
Data Modelling & Roles
Verify Roles (Testing)
Add Cloud Users to Roles
Dynamic Row Level Security
Testing RLS in Power BI

Ch 29: Analytical Reports

Analytical Report Concepts
Excel Data Analytics
Excel with Power BI Cloud
SQL, AVRO, JSON Sources
Analyse in Excel (Cloud)
Excel Reports to Cloud

Ch 30: Power BI AI, CoPilot, Projects

👉🏻 AI Components in Power BI
👉🏻 CoPilot Practical Uses
👉🏻 CoPilot with Desktop
👉🏻 CoPilot with Cloud
👉🏻 Need for AI Analytics (Fabric)
👉🏻 PL 300 Exam (Microsoft Certified Data Analyst) Guidance
👉🏻 PL 300 Exam Mocks

👉🏻 Realtime Project 1 (ECommerce / Financial Analysis)

👉🏻 Realtime Project 2 (Health Care / Banking)

Module 4: Python For Data Analysis

Ch 1: Python Introduction

Python Introduction
Python Versions
Python Implementations
Python Installations
Python IDE & Usage
Jupyter Notebooks

Ch 2: Python Operations

Basic Operations in Python
Python Scripts, Print()
Single, Multiline Statements
Python: Internal Architecture
Compiler Versus Interpreter

Ch 3: Data Types & Variables

Integer / Int Data Types
Float, String Data Types
Sequence Types: List, Tuple
Range, Complex & memview
Retrieving Data Type: type()

Ch 4: Python Operators

Arithmetic, Assignment Ops
Comparison Operators
Operator Precedence
If … Else Statement, Pass
Short Hand If, OR, AND
ELIF and ELSE IF Statements

Ch 5: Python Loops, Iterations

Python Loop & Realtime Use
Python While Loop Statement
Break and Continue Statement
Iterations & Conditions
Exit Conditions & For Loops
iter() and Looping Options

Ch 6: Python Functions

Python Functions & Usage
Function Parameters
Default & List Parameters
Python Lambda Functions
Recursive Functions, Usage
Return & Print @ Lamdba

Ch 7: Python Modules

Import Python Modules
Built In Modules & dir
datetime module in Python
Date Objections Creation
strftime Method & Usage
imports &now()

Ch 8: Python User Inputs & TRY

Try Except, Exception Handling
Raise an exception method
TypeError, Scripting in Python
Python User Inputs
Python Index Numbers
input() & raw_input()

Ch 9: Python File Handling

File Handling, Activities
Loop, Write, Close Files
Appending, Overwriting
import os,exists
open, f.write
read, f.close

Ch 10: Pandas DataFrames 1

Installation of Pandas
Python Modules & Pandas
Pandas Codebase & Usage
import DataFrame
Pandas Series, arrays

Ch 11: Pandas DataFrames 2

Indexes & Named Options
Locate Row and Load Rows
Row Index & Index Lists
Load Files Into a DataFrame
to_string() Function
tail() & null() Function

Ch 12: Pandas Transformations

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

Ch 13: Realtime Project (Banking / Finance) For Data Analysis [End to End]

Ch 14: Weekly Mock Interview Review meet ++ 1:1 Resume Meet

Module 5: Python Programming

Ch 19: Python Dictionary

Dictionary Creation, Use
Hashing, Copy, Update
Deletion, Sorting
Len(), Inbuilt Functions
Variable Types – python List
Cmp() List Method
Python Dictionary Str(dict)
Programming Concepts
Loops and Sets
Realtime Usage

Ch 20: Python Packages

Package in Python
Creating a package
Package Imports, Modules
Sub Packages Creation
Sub Package Imports
Popular Packages in Python
NumPy & SciPy
Libraries in Python
Python Seaborn
Python framework

Ch 21: Exception Handling

Shell Script Commands
OS operations in Python
File System Shell Methods
os – math – cmd -csv – random
Numpy (numerical python)
Pandas – sys – Matplotlib;
Common RunTime Errors
Python Custom Exception;
Exception Handling

Ch 22: Python Class & Objects

Class variables, Instances
Built in Class Attributes
Objects – Constructors
Modifiers – Self Variable
Python Garbage Collections
Hierarchical Inheritance
Multilevel, Multiple, Hybrid
Overloading & OverRiding
Polymorphism – Abstraction

Ch 23: Regular Expressions

Regular Expression
Regular Expression Patterns
Literals – Repetition Cases
Groups andGrouping
w+ and ^ , \s Expressions
split function
Regular expression methods
match() in Regular Expr
search(), re.findall for Text

Ch 24: Multi-Threading

Python Multi-Threading
Thread Synchronization
Multiprocessing
Python Gil & Programming
Thread Control Block (TCB)
Stack Pointers & App Usage
Program Counters in Realtime
Thread State Concept
Python Exception Handling

Ch 25: Python TKinter

Tkinter GUI Program
Components & Events
Adding Controls inTkinter
Entry, Text Widgets
Radio & Check Buttons
Tkinter Forms in Realtime
List Boxes, Menu, ComboBox
Mainloop () & Functions

Ch 26: Python Web & IoT Intro

Python Web Frameworks
Django : Advantages
Web Framework
MVC and MVT – Django
Web Pages using python
HTML5, CSS3 usage
PYTHON Bottle & Pyramid
Falcon ; smart_open in python

Module 6: Python with AI – ML

Ch 28: Machine Learning Basics

Machine Learning Funda
Python ML in Realtime
Pandas Extension in ML
Machine Learning Ops
Business to Data Conversions
ML Algorithms in Realtime

Ch 29: Python ML Concepts

Machine Learning (ML) Intro
Supervised, Unsupervised
Scikit-Learn Library
Python Libraries for ML
sklearn : Advantages & Uses
sklearn : Functions, Use

Ch 30: Python Data Handling

Data structures
Lists, Tuples, Sets
Dictionaries,
Pandas Data Operations
Data Visualizations
Matplotlib & Seaborn

Ch 31: AI With Python Intro

Artificial Intelligence
Applications of AI
AI Applicative Uses
AI Usage with Python
AI – Python Environment
Python Libraries
AI with Python in Realtime

Ch 32: Supervised Learning

Linear & Logistic Regression
Decision Trees
Random Forests
Support Vector Machines
Neural Networks Basics
Linear Regression Steps
Linear Regression in AI-ML

Ch 33: Unsupervised Learning – 1

Clustering & K-means
DBSCAN & Realtime Usage
Dimensionality Reduction
K clustering hierarchical
DBScan : Realtime Uses
KMeans clustering Vs DBSCAN?
PCA Vs t-SNE

Ch 34: Unsupervised Learning 2

Unsupervised Learning
Concepts and Scope
Realtime Usage
Dimensionality Reduction
Component Analysis (PCA)
PCA: Concept & Usage

Ch 35: Generalized Models

GLM Concept in Python
GLM in Regression
Considerations for GLM
Problem Solving Skills
Python Libraries
Python Extensions: GLM

Ch 36: Python Tree Models

Decision Tree Models
Decision Tree Working
Model Works, Algorithms
Random Forest Concept
Random Forest Tree
Random Forest Vs Knn

Ch 37: Big Data and ML

Spark and Big Data
Big Data with Python
Spark with Python
Spark with Big Data
Spark Algorithms
AI ML Libraries

Ch 38: Natural Lang” Processing

NLP : Purpose, Usage
NLP Applicative Uses
NLP Vs Machine Learning
NLP in Machine Learning
Using NLP in AI – ML
NLP code in Python?

Ch 39: AI in Real-World

AI in Chatbots
AI in Virtual Assistants
AI Ethical Considerations
AI Deployments (Flask)
AI with FastAPI
AI with Streamlit

Module 7: End to End Project Work

👉🏻Python AI

👉🏻Python ML

👉🏻Realtime Project

👉🏻Resume Guidance

👉🏻1:1 mentorship

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Azure Data Engineer training modules showing Azure Logic Apps, Azure Data Factory, Azure Synapse, Azure Data Lake, Stream Analytics, Python ETL, Spark, PySpark, Unity Catalog, Azure Databricks, Delta Lake, Hadoop Hive, Azure Monitor, Azure Functions, AI ML, and real-time projects

Training Modes

LIVE Online Training

Instructor Led

Self Paced Videos

On-Demand

Corporate Training

With 100% Hands-On

Placement Partners

Tata Group - A Symbol of Global Excellence

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SQL SCHOOL

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

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100% Real-Time and Practical
ISO 9001:2008 Certified
Weekly Mock Interviews
24/7 LIVE Server Access
Realtime Project FAQs
Course Completion Certificate
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