IoT Training Institute in Gurgaon

Python Introduction

• Numbers, List, Tuple & dictionaries

• If else statements

• Loop & Control Statements

• File Handling

• Understanding Functions

• Raspberry Pi Programming

• Interfacing Sensors with Raspberry Pi

IoT Gateways

• Introduction to IoT Gateway

• ESP8266 Module as Wi-Fi Gateway

• Learning AT Commands of ESP8266

• Programming on ESP8266 Wi-Fi Module

Cloud Platforms for IoT

• Introduction to Cloud

• It’s different models of Cloud

• Overview different IoT cloud platforms

• How to get Access to Cloud Platforms

• Sending Data to IoT Cloud Platform

• Sending Creating Alerts Using Cloud Platform

• Developing IoT Prototype

IoT Implementations [Applications]

• Cities

• Home

• Agriculture

• Healthcare Industry

• Transportation

• Environment

• Infrastructure

• Security

Future & Security Concerns

• Future Scope

• Challenges

• Security

Data Analysis and Visualization using Pandas.

Statistics

• Categorical Data

• Numerical Data

• Mean

• Median

• Mode

• Outliers

• Range

• Interquartile range

• Correlation

• Standard Deviation

• Variance

• Box plot

Pandas

• Read data from Excel File using Pandas More Plotting, Date Time Indexing and writing to les

• How to get record specic records Using Pandas Adding & Resetting Columns, Mapping with function

• Using the Excel File class to read multiple sheets More Mapping, Filling Nonvalue’s

• Exploring the Data Plotting, Correlations, and Histograms

• Getting statistical information about the data Analysis Concepts, Handle the None Values

• Reading les with no header and skipping records Cumulative Sums and Value Counts, Ranking etc

• Reading a subset of columns Data Maintenance, Adding/Removing Cols and Rows

• Applying formulas on the columns Basic Grouping, Concepts of Aggre gate Function

• Complete Understanding of Pivot Table Data Slicing using iLoc and Loc property (Setting Indices)

• Under sting the Properties of Pivot Table in Pandas Advanced Reading CSVs/HTML, Binning, Categorical Data

• Exporting the results to Excel Joins:

• Python | Pandas Data Frame Inner Join

• Under sting the properties of Data Frame Left Join (Left Outer Join)

• Indexing and Selecting Data with Pandas Right Join (Right Outer Join)

• Pandas | Merging, Joining and Concatenating Full Join (Full Outer Join)

• Pandas | Find Missing Data and Fill and Drop NA Appending Data Frame and Data

• Pandas | How to Group Data How to apply Lambda / Function on Data Frame

• Other Very Useful concepts of Pandas in Python Data Time Property in Pandas (More and More)

Data Analysis and Visualization using NumPy and MatPlotLib

NumPy

• Introduction to NumPy: Numerical Python

• Importing NumPy and Its Properties

• NumPy Arrays

• Creating an Array from a CSV

• Operations an Array from a CSV

• Operations with NumPy Arrays

• Two-Dimensional Array

• Selecting Elements from 1-D Array

• Selecting Elements from 2-D Array

• Logical Operation with Arrays

• Indexing NumPy elements using conditionals

• NumPy’s Mean and Axis

• NumPy’s Mode, Median and Sum Function

• NumPy’s Sort Function and More

MatPlotLib

• Bar Chart using Python MatPlotLib

• Column Chart using Python MatPlotLib

• Pie Chart using Python MatPlotLib

• Area Chart using Python MatPlotLib

• Scatter Plot Chart using Python MatPlotLib

• Play with Charts Properties Using MatPlotLib

• Export the Chart as Image

• Understanding plt. subplots () notation

• Legend Alignment of Chart using MatPlotLib

• Create Charts as Image

• Other Useful Properties of Charts.

• Complete Understanding of Histograms

• Plotting Different Charts, Labels, and Labels Alignment etc.

Introduction to Data Visualization with Seaborn

Introduction to Seaborn

• Introduction to Seaborn

• Making a scatter plot with lists

• Making a count plot with a list

• Using Pandas with seaborn

• Tidy vs Untidy data

• Making a count plot with a Dataframe

• Adding a third variable with hue

• Hue and scattera plots

• Hue and count plots

Visualizing Two Quantitative Variables

• Introduction to relational plots and subplots

• Creating subplots with col and row

• Customizing scatters plots

• Changing the size of scatter plot points

• Changing the style of scatter plot points

• Introduction to line plots

• Interpreting line plots

• Visualizing standard deviation with line plots

• Plotting subgroups in line plots

Visualizing a Categorical and a Quantitative Variable

• Current plots and bar plots

• Count plots

• Bar plot with percentages

• Customizing bar plots

• Box plots

• Create and interpret a box plot

• Omitting outliers

• Adjusting the whisk

• Point plots

• Customizing points plots

• Point plot with subgroups

Customizing Seaborn Plots

• Changing plot style and colour

• Changing style and palette

• Changing the scale

• Using a custom palette

• Adding titles and labels: Part 1

• Face Grids vs. Axes Subplots

• Adding a title to a face Grid object

• Adding title and labels: Part 2

• Adding a title and axis labels

• Rotating x-tics labels

• Putting it all together

• Box plot with subgroups

• Bar plot with subgroups and subplots

• Well done! What’s next

SQL Server Fundamentals

• SQL Server 2019 Installation

• Service Accounts & Use, Authentication Modes & Usage, Instance Congurations

• SQL Server Features & Purpose

• Using Management Studio (SSMS)

• Conguration Tools & SQLCMD

• Conventions & Collation

SQL Server 2019 Database Design

• SQL Database Architecture

• Database Creation using GUI

• Database Creation using T-SQL scripts

• DB Design using Files and File Groups

• File locations and Size parameters

• Database Structure modications

SQL Tables in MS SQL Server

• SQL Server Database Tables

• Table creation using T-SQL Scripts

• Naming Conventions for Columns

• Single Row and Multi-Row Inserts

• Table Aliases

• Column Aliases & Usage

• Table creation using Schemas

• Basic INSERT

• UPDATE

• DELETE

• SELECT queries and Schemas

• Use of WHERE, IN and BETWEEN

• Variants of SELECT statement

• ORDER BY

• GROUPING

• HAVING

• ROWCOUNT and CUBE Functions

Data Validation and Constraints

• Table creation using Constraints

• NULL and IDENTITY properties

• UNIQUE KEY Constraint and NOT NULL

• PRIMARY KEY Constraint & Usage

• CHECK and DEFAULT Constraints

• Naming Composite Primary Keys

• Disabling Constraints & Other Options

Views and Row Data Security

• Benets of Views in SQL Database

• Views on Tables and Views

• SCHEMA BINDING and ENCRYPTION

• Issues with Views and ALTER TABLE

• Common System Views and Metadata

• Common Dynamic Management views

• Working with JOINS inside views

Indexes and Query tuning

• Need for Indexes & Usage

• Indexing Table & View Columns

• Index SCAN and SEEK

• INCLUDED Indexes & Usage

• Materializing Views (storage level)

• Composite Indexed Columns & Keys

• Indexes and Table Constraints

• Primary Keys & Non-Clustered Indexes

Stored Procedures and Benets

• Why to use Stored Procedures

• Types of Stored Procedures

• Use of Variables and parameters

• SCHEMABINDING and ENCRYPTION

• INPUT and OUTPUT parameters

• System level Stored Procedures

• Dynamic SQL and parameterization

System functions and Usage

• Scalar Valued Functions

• Types of Table Valued Functions

• SCHEMABINDING and ENCRYPTION

• System Functions and usage

• Date Functions

• Time Functions

• String and Operational Functions

• ROW_COUNT

• GROUPING Functions

Triggers, cursors, memory limitations

• Why to use Triggers

• DML Triggers and Performance impact

• INSERTED and DELETED memory tables

• Data Audit operations & Sampling

• Database Triggers and Server Triggers

• Bulk Operations with Triggers

Cursors and Memory Limitations

• Cursor declaration and Life cycle

• STATIC

• DYNAMIC

• SCROLL Cursors

• FORWARD_ONLY and LOCAL Cursors

• KEYSET Cursors with Complex SPs

Transactions Management

• ACID Properties and Scope

• EXPLICIT Transaction types

• IMPLICIT Transactions and options

• AUTOCOMMIT Transaction and usage

• SAVEPOINT and Query Blocking

Introduction to Data Science

• What is Analytics & Data Science

• Common Terms in Data Science

• What is data

• Classication of data

• Relevance in industry and need of the hour

• Types of problems and business objectives in various industries

• How leading companies are harnessing the power of analytics

• Critical success drivers.

• Overview of Data Science tools & their popularity.

• Data Science Methodology & problem-solving framework.

• List of steps in Data Science projects

• Identify the most appropriate solution design for the given problem statement

• Project plan for Data Science project & key milestones based on effort estimates

• Build Resource plan for Data Science project

• Why Python for data science

Accessing/Importing and Exporting Data

• Importing Data from various sources (Csv, txt, excel, access etc)

• Database Input (Connecting to database)

• Viewing Data objects - sub setting, methods

• Exporting Data to various formats

• Important python modules: Pandas

Data Manipulation: Cleansing - Munging Using Python Modules

• Cleansing Data with Python

• Filling missing values using lambda function and concept of Skewness.

• Data Manipulation steps (Sorting, ltering, duplicates, merging, append ing, sub setting, derived variables, sampling, Data type conversions, renaming, formatting.

• Normalizing data

Feature Engineering in Data Science

• Feature Engineering

• Feature Selection

• Feature scaling using Standard Scaler/Min-Max scaler/Robust Scaler.

• Label encoding/one hot encoding

Data Analysis: Visualization Using Python

• Introduction exploratory data analysis

• Descriptive statistics, Frequency Tables and summarization

• Univariate Analysis (Distribution of data & Graphical Analysis)

• Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)

• Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ densi ty etc.)

• Important Packages for Exploratory Analysis (NumPy Arrays, Matplotlib, seaborn, Pandas etc.)

Introduction to Statistics

• Descriptive Statistics

• Sample vs Population Statistics

• Random variables

• Probability distribution functions

• Expected value

• Normal distribution

• Gaussian distribution

• Z-score

• Spread and Dispersion

• Correlation and Co-variance

Introduction to Predictive Modelling

• Concept of model in analytics and how it is used

• Common terminology used in Analytics & Modelling process

• Popular Modelling algorithms

• Types of Business problems - Mapping of Techniques

• Different Phases of Predictive Modelling

EDA (Exploratory Data Analysis)

• Need for structured exploratory data

• EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)

• Identify missing data

• Identify outliers’ data

• Imbalanced Data Techniques

Data Pre-Processing & Data Mining

• Data Preparation

• Feature Engineering

• Feature Scaling

• Datasets

• Dimensionality Reduction

• Anomaly Detection

• Parameter Estimation

• Data and Knowledge

• Selected Applications in Data Mining

Introduction to Machine Learning

• Machine Learning

• Machine Learning Algorithms

• Algorithmic models of Learning

• Applications of Machine Learning

• Large Scale Machine Learning

• Computational Learning theory

Techniques of Machine Learning

• Supervised Learning

• Unsupervised Learning

• Semi-supervised and Reinforcement Learning

• Bias and variance Trade-off

• Representation Learning

Regression

• Regression and its Types

• Logistic Regression

• Linear Regression

• Polynomial Regression

Classication

• Meaning and Types of Classication

• Probability and Bayes Theorem

• Support Vector Machines

• Naive Bayes

• Decision Tree Classier

• Random Forest Classier

Introduction to Deep Learning

• What are the Limitations of Machine Learning

• What is Deep Learning

• Advantage of Deep Learning over Machine learning

• Reasons to go for Deep Learning

• Real-Life use cases of Deep Learning

Deep Learning Networks

• What is Deep Learning Networks

• Why Deep Learning Networks

• How Deep Learning Works

• Feature Extraction

• Working of Deep Network

• Training using Backpropagation

• Variants of Gradient Descent

Introduction to Power BI

• Overview of BI concepts

• Why we need BI

• Introduction to SSBI

• SSBI Tools

• Why Power BI

• What is Power BI

• Building Blocks of Power BI

• Getting started with Power BI Desktop

• Get Power BI Tools

• Introduction to Tools and Terminology

• Dashboard in Minutes

• Interacting with your Dashboards

• Sharing Dashboards and Reports

Power BI Desktop

• Power BI Desktop

• Extracting data from various sources

• Workspaces in Power BI

Power BI Data Transformation

• Data Transformation

• Query Editor

• Connecting Power BI Desktop to our Data Sources

• Editing Rows

• Understanding Append Queries

• Editing Columns

• Replacing Values

• Formatting Data

• Pivoting and Unpivoting Columns

• Splitting Columns

• Creating a New Group for our Queries

• Introducing the Star Schema

• Duplicating and Referencing Queries

• Creating the Dimension Tables

• Entering Data Manually

• Merging Queries

• Finishing the Dimension Table

• Introducing the another DimensionTable

• Creating an Index Column

• Duplicating Columns and Extracting Information

• Creating Conditional Columns

• Creating the FACT Table

• Performing Basic Mathematical Operations

• Improving Performance and Loading Data into the Data Model

Modelling with Power BI

• Introduction to Modelling

• Modelling Data

• Manage Data Relationship

• Optimize Data Models

• Cardinality and Cross Filtering

• Default Summarization & Sort by

• Creating Calculated Columns

• Creating Measures & Quick Measures

Data Analysis Expressions (DAX)

• What is DAX

• Data Types in DAX

• Calculation Types

• Syntax, Functions, Context Options

• DAX Functions
• Date and Time
• Time Intelligence
• Information
• Logical
• Mathematical
• Statistical
• Text and Aggregate

• Measures in DAX

• Measures and Calculated Columns

• ROW Context and Filter Context in DAX

• Operators in DAX - Real-time Usage

• Quick Measures in DAX - Auto validations

• In-Memory Processing: DAX Performance

Power BI Desktop Visualisations

• How to use Visual in Power BI

• What Are Custom Visuals

• Creating Visualisations and Colour Formatting

• Setting Sort Order

• Scatter & Bubble Charts & Play Axis

• Tooltips and Slicers, Timeline Slicers & Sync Slicers

• Cross Filtering and Highlighting

• Visual, Page and Report Level Filters

• Drill Down/Up

• Hierarchies and Reference/Constant Lines

• Tables, Matrices & Conditional Formatting

• KPI's, Cards & Gauges

• Map Visualizations

• Custom Visuals

• Managing and Arranging

• Drill through and Custom Report Themes

• Grouping and Binning and Selection Pane, Bookmarks & Buttons

• Data Binding and Power BI Report Server

Introduction to Power BI Dashboard and Data Insights

• Why Dashboard and Dashboard vs Reports

• Creating Dashboards

• Conguring a Dashboard: Dashboard Tiles, Pinning Tiles

• Power BI Q&A

• Quick Insights in Power BI

Direct Connectivity

• Custom Data Gateways

• Exploring live connections to data with Power BI

• Connecting directly to SQL Server

• Connectivity with CSV & Text Files

• Excel with Power BI: Connect Excel to Power BI, Power BI Publisher for Excel

• Content packs

• Update content packs

Publishing and Sharing

• Introduction and Sharing Options Overview

• Publish from Power BI Desktop and Publish to Web

• Share Dashboard with Power BI Service

• Workspaces (Power BI Pro) and Content Packs (Power BI Pro)

• Print or Save as PDF and Row Level Security (Power BI Pro)

• Export Data from a Visualization

• Export to PowerPoint and Sharing Options Summary

Refreshing Datasets

• Understanding Data Refresh

• Personal Gateway (Power BI Pro and 64-bit Windows)

• Replacing a Dataset and Troubleshooting Refreshing

Introduction to Data Preparation using Tableau Prep

• Data Visualization

• Business Intelligence tools

• Introduction to Tableau

• Tableau Architecture

• Tableau Server Architecture

• VizQL Fundamentals

• Introduction to Tableau Prep

• Tableau Prep Builder User Interface

• Data Preparation techniques using Tableau Prep Builder tool

Data Connection with Tableau Desktop

• Features of Tableau Desktop

• Connect to data from File and Database

• Types of Connections

• Joins and Unions

• Data Blending

• Tableau Desktop User Interface

Basic Visual Analytics

• Visual Analytics

• Basic Charts: Bar Chart, Line Chart, and Pie Chart

• Hierarchies

• Data Granularity

• Highlighting

• Sorting

• Filtering

• Grouping

• Sets

Calculations in Tableau

• Types of Calculations

• Built-in Functions (Number, String, Date, Logical and Aggregate)

• Operators and Syntax Conventions

• Table Calculations

• Level of Detail (LOD) Calculations

• Using R within Tableau for Calculations

Advanced Visual Analytics

• Parameters

• Tool tips

• Trend lines

• Reference lines

• Forecasting

• Clustering

Level of Detail (LOD) Expressions in Tableau

• Count Customer by Order

• Profit per Business Day

• Comparative Sales

• Profit Vs Target

• Finding the second order date

• Cohort Analysis

Geographic Visualizations in Tableau

• Introduction to Geographic Visualizations

• Manually assigning Geographical Locations

• Types of Maps

• Spatial Files

• Custom Geocoding

• Polygon Maps

• Web Map Services

• Background Images

Advanced charts in Tableau

• Box and Whisker’s Plot

• Bullet Chart

• Bar in Bar Chart

• Gantt Chart

• Waterfall Chart

• Pareto Chart

• Control Chart

• Funnel Chart

• Bump Chart

• Step and Jump Lines

• Word Cloud

• Donut Chart

Dashboards and Stories

• Introduction to Dashboards

• The Dashboard Interface

• Dashboard Objects

• Building a Dashboard

• Dashboard Layouts and Formatting

• Interactive Dashboards with actions

• Designing Dashboards for devices

• Story Points

Get Industry Ready

• Tableau Tips and Tricks

• Choosing the right type of Chart

• Format Style

• Data Visualization best practices

Exploring Tableau Online

• Publishing Workbooks to Tableau Online

• Interacting with Content on Tableau Online

• Data Management through Tableau Catalog

• AI-Powered features in Tableau Online (Ask Data and Explain Data)

• Understand Scheduling

• Managing Permissions on Tableau Online

• Data Security with Filters in Tableau Online

Placement Guide

• Tips to clear an Interview

• Common Interview questions and answers

• Tableau Interview Questions and Answers

• Resume Building Guide

• Attempt for the related Global Certification Exam

• Earn Credentials and Start applying for Jobs

Introduction to Cloud Computing

• In this module, you will learn what Cloud Computing is and what are the different models of Cloud Computing along with the key differentiators of different models. We will also introduce you to virtual world of AWS along with AWS key vocabulary, services and concepts.
• A Short history
• Client Server Computing Concepts
• Challenges with Distributed Computing
• Introduction to Cloud Computing
• Why Cloud Computing
• Benefits of Cloud Computing

Amazon EC2 and Amazon EBS

• In this module, you will learn about the introduction to compute offering from AWS called EC2. We will cover different instance types and Amazon AMIs. A demo on launching an AWS EC2 instance, connect with an instance and host ing a website on AWS EC2 instance. We will also cover EBS storage Architecture (AWS persistent storage) and the concepts of AMI and snapshots.
• Amazon EC2
• EC2 Pricing
• EC2 Type
• Installation of Web server and manage like (Apache/ Nginx)
• Amazon EBS
• Demo of AMI Creation
• Backup, Restore
• Exercise
• Mock
• Hands on both Linux and Windows

Amazon Storage Services S3 (Simple Storage Services)

• In this module, you will learn how AWS provides various kinds of scalable storage services. In this module, we will cover different storage services like S3, Glacier, Versioning, and learn how to host a static website on AWS.
• Versioning
• Static website
• Policy
• Permission
• Cross region Replication
• AWS-CLI
• Mount Point with S3
• Life cycle
• Classes of Storage
• AWS CloudFront
• Real scenario Practical
• Hands-on all above

Cloud Watch & SNS

• In this module, you will learn how to monitoring AWS resources and setting up alerts and notifications for AWS resources and AWS usage billing with AWS CloudWatch and SNS.
• Amazon Cloud Watch
• SNS - Simple Notification Services
• SQS
• Cloud Watch with Agent
• Cloud Watch with System Manager

Scaling and Load Distribution in AWS

• In this module, you will learn about 'Scaling' and 'Load distribution techniques' in AWS. This module also includes a demo of Load distribution & Scaling your resources horizontally based on time or activity.
• Amazon Auto Scaling
• Auto scaling policy with real scenario based
• Type of Load Balancer
• Path Based load balancer
• Hands on with scenario based
• Routing policy on Load balancer

AWS VPC

• In this module, you will learn introduction to Amazon Virtual Private Cloud. We will cover how you can make public and private subnet with AWS VPC. A demo on creating VPC. We will also cover overview of AWS Route 53.
• Amazon VPC with subnets
• Gateways
• Route Tables
• Subnet
• Cross region Peering
• Endpoint Creation with VPC

Identity and Access Management Techniques (IAM)

• In this module, you will learn how to achieve distribution of access control with AWS using IAM.

• Amazon IAM
• add users to groups,
• manage passwords,
• log in with IAM-created users.

• User

• Group

• Role

• Policy

Amazon Relational Database Service (RDS)

• In this module, you will learn how to manage relational database service of AWS called RDS.
• Amazon RDS
• Type of RDS
• RDS Failover
• RDS Subnet
• RDS Migration
• Dynamo DB (No SQL DB)
• Redshift Cluster
• SQL workbench
• JDBC / ODBC

Multiple AWS Services and Managing the Resources' Lifecycle

• In this module, you will get an overview of multiple AWS services. We will talk about how do you manage life cycle of AWS resources and follow the DevOps model in AWS. We will also talk about notification and email service of AWS along with Content Distribution Service in this module.
• Cloud Trail,
• SQS

AWS Architecture and Design

• In this module, you will cover various architecture and design aspects of AWS. We will also cover the cost planning and optimization techniques along with AWS security best practices, High Availability (HA) and Disaster Recovery (DR) in AWS.
• AWS Backup and DR Setup
• AWS High Availability Design
• AWS Best Practices (Cost +Security)
• AWS Calculator & Consolidated Billing

Migrating to Cloud & AWS

• In this module, you will learn how to migrate to cloud
• Migration to Cloud
• Migration to AWS
• Step by step process

Router S3 DNS

• Public DNS

• Private DNS

• Routing policy

• Records

• Register DNS

• Work with third party DNS as well

Cloud Formation

• Stack

• Templet

• Json / Ymal

Elastic Beanstalk

EFS / NFS (hands-on practice)

Hands-on practice on various Topics

• ECS, EKS (Kubernetes), Docker
• Comprehensive hands-on with Dockers & Kubernetes Components
• Docker & Kubernetes Architecture & Components and installation
• Get introduced to deploy stateful and stateless apps on the cluster
• Learn how to expose the app outside the cluster and to auto scale apps
• Expertise learning with use cases of Containers and Docker
• Installation of Linux
• Configuration
• Manage
• Installation of app on Linux (apache / Nginx etc)
• AWS cli configuration on Linux
• Complete hands-on on Linux.

Linux

• Python

• Boto

• DMS

• System Manager

• Mock

• Interview preparation

• Scenario based lab and practical

• Each topic and services will be cover with lab and theory.

• Security: KMS / SSM/ WAF

• Storage: EFS, NFS, FSX, Storage Gateway

These topics will be covered with this AWS course with real scenario based

Describe Cloud Concepts

• Identify the benefits and considerations of using cloud services Cloud Computing Basics.
• Identify the benefits of cloud computing, such as High Availability,Scalability, Elasticity,
• Agility, and Disaster Recovery
• Identify the differences between Capital Expenditure (Cap Ex) and Operational.
• Expenditure (Op Ex)
• Describe the consumption-based model

• Describe the differences between categories of cloud services
• Describe the shared responsibility model
• Describe Infrastructure-as-a-Service (IaaS),
• Describe Platform-as-a-Service (PaaS)
• Describe server less computing
• Describe Software-as-a-Service (SaaS)
• Identify a service type based on a use case

• Describe the differences between types of cloud computing
• Define cloud computing
• Describe Public cloud
• Describe Private cloud
• Describe Hybrid cloud
• Compare and contrast the three types of cloud computing Describe Core Azure Services

Describe Core Azure Services

• Describe the core Azure architectural components
• Describe the benefits and usage of Regions and Region Pairs
• Describe the benefits and usage of Availability Zones
• Describe the benefits and usage of Resource Groups
• Describe the benefits and usage of Subscriptions
• Describe the benefits and usage of Management Groups
• Describe the benefits and usage of Azure Resource Manager
• Explain Azure resources

• Describe core resources available in Azure
• Describe the benefits and usage of Virtual Machines, Azure App Services, Azure Container Instances (ACI), Azure Kubernetes Service (AKS), and Azure Virtual Desktop.
• Describe the benefits and usage of Virtual Networks, VPN Gateway, Virtual Network peering, and ExpressRoute
• Describe the benefits and usage of Container (Blob) Storage, Disk Storage,File Storage, and storage tiers
• Describe the benefits and usage of Cosmos DB, Azure SQL Database, Azure Database for MySQL, Azure Database for PostgreSQL, and SQL Managed Instance.
• Describe the benefits and usage of Azure Marketplace

Describe core solutions and management tools on Azure

• Describe core solutions available in Azure
• Describe the benefits and usage of Internet of Things (IoT) Hub, IoT Central, and Azure Sphere
• Describe the benefits and usage of Azure Synapse Analytics, HDInsight, and Azure Data bricks
• Describe the benefits and usage of Azure Machine Learning, Cognitive Services and Azure Bot Service
• Describe the benefits and usage of server less computing solutions that include Azure Functions and Logic Apps
• Describe the benefits and usage of Azure DevOps, GitHub, GitHub Actions, and Azure Dev Test Labs

• Describe Azure management tools
• Describe the functionality and usage of the Azure Portal, Azure PowerShell, Azure CLI, Cloud Shell, and Azure Mobile App
• Describe the functionality and usage of Azure Advisor
• Describe the functionality and usage of Azure Resource Manager (ARM) templates.
• Describe the functionality and usage of Azure Monitor
• Describe the functionality and usage of Azure Service Health

Describe general security and network security features

• Describe Azure security features
• Describe basic features of Azure Security Center, including policy compliance, security alerts, secure score, and resource hygiene.
• Describe the functionality and usage of Key Vault
• Describe the functionality and usage of Azure Sentinel
• Describe the functionality and usage of Azure Dedicated Hosts

• Describe Azure network security
• Describe the concept of defence in depth
• Describe the functionality and usage of Network Security Groups (NSG)
• Describe the functionality and usage of Azure Firewall
• Describe the functionality and usage of Azure DDoS protection

Describe identity, governance, privacy, and compliance features

• Describe core Azure identity services
• Explain the difference between authentication and authorization
• Define Azure Active Directory
• Describe the functionality and usage of Azure Active Directory
• Describe the functionality and usage of Conditional Access, Multi-Factor Authentication
• MFA), and Single Sign-On (SSO)

• Describe Azure governance features
• Describe the functionality and usage of Role-Based Access Control (RBAC)
• Describe the functionality and usage of resource locks
• Describe the functionality and usage of tags
• Describe the functionality and usage of Azure Policy
• Describe the functionality and usage of Azure Blueprints
• Describe the Cloud Adoption Framework for Azure

• Describe privacy and compliance resources

• Describe the Microsoft core tenets of Security, Privacy, and Compliance

• Describe the purpose of the Microsoft Privacy Statement, Online Services Terms (OST) and Data Protection Amendment (DPA)

• Describe the purpose of the Trust Centre

• Describe the purpose of the Azure compliance documentation

• Describe the purpose of Azure Sovereign Regions (Azure Government cloud services and Azure China cloud services)

Describe Azure cost anagement and Service Level Agreements

• Describe methods for planning and managing costs
• Identify factors that can affect costs (resource types, services, locations,ingress and egress traffic)
• Identify factors that can reduce costs (reserved instances, reserved capacity,hybrid use benefit, spot pricing)
• Describe the functionality and usage of the Pricing calculator and the Total Cost of Ownership (TCO) calculator
• Describe the functionality and usage of Azure Cost Management

• Describe Azure Service Level Agreements (SLAs) and service lifecycles
• Describe the purpose of an Azure Service Level Agreement (SLA)
• Identify actions that can impact an SLA (i.e. Availability Zones)
• Describe the service lifecycle in Azure (Public Preview and General Availability)

Data Science Professionals - Live Projects

• Managing credit card Risks

• Bank Loan default classification

• YouTube Viewers prediction

• Super store Analytics (E-commerce)

• Buying and selling cars prediction (like OLX process)

• Advanced House price prediction

• Analytics on HR decisions

• Survival of the fittest

• Twitter Analysis

• Flight price prediction