Best Data Analyst Training In Delhi

Data analyst course Overview

KVCH is a top provider of data analyst training in Delhi. Our training institute is renowned for its qualified trainers and industry-oriented curriculum. Our teaching methods are extraordinary, as we stress practical knowledge more than theoretical. Enrol in our data analyst course in Delhi and build a successful career in data analysis.

What will you learn at our data analyst training institute in Delhi?

Skills to carry out the day-to-day activities of an entry-level data analyst.
Cleaning and organising data
Visualise and present data using common tools
Learn key analytics skills like visualisations, spreadsheets, SQL, R programming languages, Tableau, etc.

What makes KVCH a top data analyst institute in Delhi?

The best industry trainers
Placement assistance
Experiential learning
Constant support
Stress on practical learning
offline/online availability

Our data analyst offline course in Delhi is designed to impart knowledge of the latest technologies in data analysis. We have included all the necessary skills in one course to prepare you for all the roles and duties of a data analyst.

With our data analyst course in Delhi, we aim to offer:

Hands-on training:

Our data analyst coaching in Delhi aims to provide hands-on training to data analyst aspirants. With real-world projects, you can learn to use theoretical concepts practically.

Placement assistance:

Besides skills and theoretical knowledge, we provide a data analyst course in Delhi with placement. We offer interview and resume preparation training, so you can get placed at top organisations.

Prepare for jobs:

The end goal of our data analyst training in Delhi is not merely to provide you with a certificate but to prepare you for the real world. We teach you skills that will help you start your career successfully.

Industry updates:

At our data analyst institute in Delhi, our technical trainers keep themselves updated about the latest industry trends. Our trainers keep themselves afloat with trends to impart the best knowledge.

Comprehensive learning:

Our approach to comprehensive learning makes KVCH the best institute for data analyst courses in Delhi. We offer to teach skills like data cleaning and organising, data visualisation and presentation, and data analysis using various programming languages.

With our data analyst offline course in Delhi, you are ready to take on entry-level roles at various positions. A data analytics course makes you eligible to become a:

Data Analyst?
Business Analyst?
Data Scientist?
Data Engineer?
Data Architect?
Marketing Analyst?

Trust KVCH, a top data analytics institute in Delhi, and grow your career as a data analyst. Whether you are a fresher or have some experience, our data analyst course in Delhi is fit for everyone.

After completing data analyst training in Delhi, you can expect to get reasonable compensation as a fresher. With increasing experience, you can move up the ladder and become a senior data analyst. Your salary will skyrocket as you gain more experience.

As per Glassdoor, the average salary of a fresh data analyst is ₹6,60,000.

As per the Ambition box, the average annual salary is ₹5.7 lacs.

As per Payscale, the average annual salary is ₹5,01,729.

These are the starting salaries of data analysts in India. Our data analyst course in Delhi with placement, can help you start your career at reputed organisations.

With the widespread use of smartphones and high-speed internet, a vast amount of data is being generated every second. This data needs to be cleaned, organised, analysed, and visualised to help organisations make insightful decisions. Data Analyst is important for:

Reducing operational costs
Enhancing decision-making speed
Creating new products
Promoting existing products
Boosting security

Data analysts are highly sought after. Data analysts are required by almost every organisation to analyse large volumes of data. Data analysis helps organisations make informed decisions. The demand for data analysts is increasing, and many organisations are actively recruiting data analysts.

In India, the demand for data analysts is rising. Be it the healthcare industry, entertainment industry, product-based companies, or service-based companies, a data analyst is required to handle large volumes of data.

Top companies that hire data analysts are TCS, Tech Mahindra, Capgemini, Accenture, Myntra, Paytm, Genpact, Adidas, etc.

After completing the data analyst course in Delhi from KVCH, you will receive a certificate. This certificate will be proof of your skills and knowledge. If you are a fresher, then this certificate can help you get entry-level jobs.

Our data analytics institute in Delhi will provide you with the certificate only upon successful completion of the course and not before that.

Can’t find a BATCH you were looking for?

Data Analyst Curriculum

Data Ananyst is a powerful analytics platform to make discoveries. By using different aspects of computer science, data visualisations, data analytics, statistics, R and Python Programming in Data Analyst , you may convert voluminous data into meaningful contents. It's a 9 months Master’s Program in Data Analyst with Power BI, Tableau & R (including Data Visualization & Cloud Implementation) which includes a 6 months online project internship.

Topics:

Python for Data Analyst
Data Analysis and Visualization
Databases – MS SQL and SQL Queries
Statistics for Data Analyst
Analytics with Excel
Analytics Microsoft Power BI
Analytics with Tableau
Analytics with R Proramming
Cloud: AWS(Amazon Web Services)
Cloud: Microsoft Azure Fundamentals
Data Analyst - Live Projects

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Introduction To Python

Installation and Working with Python
Understanding Python variables
Python basic Operators
Understanding the Python blocks.

Introduction To Variables

Variables, expression condition and function
Global and Local Variables in Python
Packing and Unpacking Arguments
Type Casting in Python
Byte objects vs. string in Python
Variable Scope

Python Data Type

Declaring and usingNumeric data types
Using stringdata type and string operations
Understanding Non-numeric data types
Understanding the concept of Casting and Boolean.
Strings
List
Tuples
Dictionary
Sets

Introduction Keywords and Identifiers and Operators

Python Keyword and Identifiers
Python Comments, Multiline Comments.
Python Indentation
Understating the concepts of Operators

List

What is List.
List Creation
List Length
List Append
List Insert
List Remove
List Append & Extend using “+” and Keyword
List Delete
List related Keyword in Python
List Revers
List Sorting
List having Multiple Reference
String Split to create a List
List Indexing
List Slicing
List count and Looping
List Comprehension and Nested Comprehension

Dictionary

Dict Creation
Dict Access (Accessing Dict Values)
Dict Get Method
Dict Add or Modify Elements
Dict Copy
Dict From Keys.
Dict Items
Dict Keys (Updating, Removing and Iterating)
Dict Values
Dict Comprehension
Default Dictionaries
Ordered Dictionaries
Looping Dictionaries
Dict useful methods (Pop, Pop Item, Str , Update etc.)

Sets, Tuples and Looping Programming

Sets

What is Set
Set Creation
Add element to a Set
Remove elements from a Set
PythonSet Operations
Frozen Sets

Tuple

What is Tuple
Tuple Creation
Accessing Elements in Tuple
Changinga Tuple
TupleDeletion
Tuple Count
Tuple Index
TupleMembership
TupleBuilt in Function (Length, Sort)

Control Flow

Loops
Loops and Control Statements (Continue, Break and Pass).
Looping techniques in Python
How to use Range function in Loop
Programs for printing Patterns in Python
How to use if and else with Loop
Use of Switch Function in Loop
Elegant way of Python Iteration
Generator in Python
How to use nested IF and Else in Python
How to use nested Loop in Python
Use If and Else in for and While Loop
Examples of Looping with Break and Continue Statements
How to use IN or NOTkeywordin Python Loop.

Exception and File Handling, Module, Function and Packages

Python Exception Handling

Python Errors and Built-in-Exceptions
Exception handing Try, Except and Finally
Catching Exceptions in Python
Catching Specific Exception in Python
Raising Exception
Try and Finally

Python File Handling

Opening a File
Python File Modes
Closing File
Writing to a File
Reading from a File
Renaming and Deleting Files in Python
Python Directory and File Management
List Directories and Files
Making New Directory
Changing Directory

Python Function, Modules and Packages

Python Syntax
Function Call
Return Statement
Write an Empty Function in Python –pass statement.
Lamda/ Anonymous Function
*argsand **kwargs
Help function in Python
Scope and Life Time of Variable in Python Function
Nested Loop in Python Function
Recursive Function and Its Advantage and Disadvantage
Organizing python codes using functions
Organizing python projects into modules
Importing own module as well as external modules
Understanding Packages
Programming using functions, modules & external packages
Map, Filter and Reduce function with Lambda Function
More example of Python Function

Data Automation (Excel, SQL, PDF etc)

Python Object Oriented Programming—Oops

Concept of Class, Object and Instances
Constructor, Class attributes and Destructors
Real time use of class in live projects
Inheritance, Overlapping and Overloading operators
Adding and retrieving dynamic attributes of classes
Programming using Oops support

Python Database Interaction

SQL Database connection using
Creating and searching tables
Reading and Storing configinformation on database
Programming using database connections

Reading an excel

Reading an excel file usingPython
Writing toan excel sheet using Python
Python| Reading an excel file
Python | Writing an excel file
Adjusting Rows and Column using Python
ArithmeticOperation in Excel file.
Plotting Pie Charts
Plotting Area Charts
Plotting Bar or Column Charts using Python.
Plotting Doughnut Chartslusing Python.
Consolidationof Excel File using Python
Split of Excel File Using Python.
Play with Workbook, Sheets and Cells in Excel using Python
Creating and Removing Sheets
Formatting the Excel File Data
More example of Python Function

Working with PDF and MS Word using Python

Extracting Text from PDFs
Creating PDFs
Copy Pages
Split PDF
Combining pages from many PDFs
Rotating PDF’s Pages

Complete Understanding of OS Module of Python

Check Dirs. (exist or not)
How to split path and extension
How to get user profile detail
Get the path of Desktop, Documents, Downloads etc.
Handle the File System Organization using OS
How to get any files and folder’s details using OS

Data Analysis & Visualization

Pandas

Read data from Excel File using Pandas More Plotting, Date Time Indexing and writing to files
How to get record specific 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 files 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 Aggregate Function
Complete Understanding of Pivot Table Data Slicing using iLocand Locproperty (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 DataFrameand 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)

NumPy

Introduction to NumPy: Numerical Python
Importing NumPy and Its Properties
NumPy Arrays
Creating an Array from a CSV
Operations an Array from aCSV
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’sMean and Axis
NumPy’sMode, Median and Sum Function
NumPy’sSort 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 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 whiskers
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

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Python - MySQL

Data visualization is the graphical way to representation of information and data. By using visual elements like graphs, maps and charts. Data visualization tools provide an accessible easy way to see and understand the data.

Statistics

Categorical Data
Numerical Data
Mean
Median
Mode
Outliers
Range
Interquartile range
Correlation
Standard Deviation
Variance
Box plot

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.

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

Python - MySQL

Introduction to MySQL
What is the MySQLdb
How do I Install MySQLdb
Connecting to the MYSQL
Selecting a database
Adding data to a table
Executing multiple queries
Exporting and Importing data tables.

SQL Functions

Single Row Functions
Character Functions, Number Function, Round, Truncate, Mod, Max, Min, Date

General Functions

Count, Average, Sum, Now etc.

Joining Tables

Obtaining data from Multiple Tables
Types of Joins (Inner Join, Left Join, Right Join & Full Join)
Sub-Queries Vs. Joins

Operators (Data using Group Function)

Distinct, Order by, Group by, Equal to etc.

Database Objects (Constraints & Views)

Not Null
Unique
Primary Key
Foreign Key

SQL Basic

SQL Introduction
SQL Syntax
SQL Select
SQL Distinct
SQL Where
SQL And & Or
SQL Order By
SQL Insert
SQL Update
SQL Delete

SQL Advance

SQL Like
SQL Wildcards
SQL In
SQL Between
SQL Alias
SQL Joins
SQL Inner Join
SQL Left Join
SQL Right Join
SQL Full Join
SQL Union

SQL Functions

SQL Avg()
SQL Count()
SQL First()
SQL Last()
SQL Max()
SQL Min()
SQL Sum()
SQL Group By

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This module offers knowledge to introduce you to the basic principles based on statistical methods and procedures followed in data analysis. This course will help you to understand the work process involved with summarizing the data, data storage, visualizing the data results, and a hands-on approach with statistical analysis with python.

Introduction to Data Analytics

What is Analytics & Data Analyst
Common Terms in Data Analyst
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 Analyst tools & their popularity.
Data Analyst Methodology & problem-solving framework.
List of steps in Data Analyst projects
Identify the most appropriate solution design for the given problem statement
Project plan for Data Analyst project & key milestones based on effort estimates
Build Resource plan for Data Analyst project
Why Python for Data Analyst

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

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Excel is one of the most popular data analysis tool, to help visualize and gain insights from your data. Analytics with Excel helps you to boost your Microsoft Excel skills.

Creation of Excel Sheet Data
Range Name, Format Painter
Conditional Formatting, Wrap Text, Merge & Centre
Sort, Filter, Advance Filter
Different type of Chart Creations
Auditing, (Trace Precedents, Trace Dependents)Print Area
Data Validations, Consolidate, Subtotal
What if Analysis (Data Table, Goal Seek, Scenario)
Solver, Freeze Panes
Various Simple Functions in Excel(Sum, Average, Max, Min)
Real Life Assignment work

Advance Data Sorting
Multi-level sorting
Restoring data to original order after performing sorting
Sort by icons
Sort by colours
Lookup Functions

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The Power BI course assists the user to understand the way to install Power BI desktop also by understanding and developing the workshop and insights using the data. It offers tools and techniques that are used to visualize and analyze data. The course will help you to learn and grab insights on everything an organization need; to manage the data with Power BI.

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
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

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Tableau is an end-to-end data analytics platform that allows you to prep, analyze, collaborate, and share your data insights. Tableau excels in self-service visual analysis, allowing people to ask new questions of governed big data and easily share those insights across the organization.

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

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Many data scientists use R while analyzing data because it has static graphics that produce good-quality data visualizations. Moreover, the programming language has a comprehensive library that provides interactive graphics and makes data visualization and representation easy to analyze.

Overview :

History of R
Advantages and disadvantages
Downloading and installing
How to find documentation

R Programming Basics :

Using the R console and R Studio
Getting help
Learning about the environment
Writing and executing scripts
Object oriented programming
Introduction to vectorised calculations
Introduction to data frames
Installing and loading packages
Working directory
Saving your work

Variable types and data structures in base R :

Variables and assignment
Data types
Numeric, character, Boolean, and factors
Data structures
Vectors, matrices, arrays, data frames, lists
Indexing, sub-setting
Assigning new values
Viewing data and summaries
Naming conventions
Objects

Getting data into the R environment :

Built-in data
Reading data from structured text files
Reading data using ODBC

Data frame manipulation :

Introduction to tables, enhanced data frames
Renaming columns
Adding new columns
Binning data (continuous to categorical)
Combining categorical values
Transforming variables
Handling missing data
Merging datasets together
Stacking datasets together (concatenation)

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AWS allows you to easily move data between the data lake and purpose-built data services. For example, AWS Glue is a serverless data integration service that makes it easy to prepare data for analytics, machine learning, and application development.

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.

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 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.

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.

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.

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.

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
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.

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Data Scientists know how to train Predictive Models. So, by enabling them to work together, Microsoft Azure Data Analyst ensures high-quality models at scale in production. With MLOps incorporated as a part of the Microsoft Azure Data Analyst platform, Data Scientists can create a discrete pipeline for each model

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

Manage Azure identities and governance (15-20%)

Manage Azure AD objects
create users and groups
manage user and group properties
manage device settings
perform bulk user updates
manage guest accounts
configure Azure AD Join
configure self-service password reset
NOTE Azure AD Connect; PIM
Manage role-based access control (RBAC)
create a custom role
provide access to Azure resources by assigning roles
subscriptions
resource groups
resources (VM, disk, etc.)
interpret access assignments
manage multiple directories
Manage subscriptions and governance
configure Azure policies
configure resource locks
apply tags
create and manage resource groups
move resources
remove RGs
manage subscriptions
configure Cost Management
configure management groups
Implement and Manage Storage (10-15%)
Manage storage accounts
configure network access to storage accounts
create and configure storage accounts
generate shared access signature
manage access keys
implement Azure storage replication
configure Azure AD Authentication for a storage account
Manage data in Azure Storage
export from Azure job
import into Azure job
install and use Azure Storage Explorer
copy data by using AZ Copy
Configure Azure files and Azure blob storage
create an Azure file share
create and configure Azure File Sync service
configure Azure blob storage
configure storage tiers for Azure blobs

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.

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.

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.

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.