Masters in Data Analytics

Item: Masters in Data Analytics
Author: Amit

Master the fundamentals of data analytics and learn how to use it for retrieval of business insights.

INR 90000

Excluding GST

100% Placement

Assistance

Ranked#2 among Top Full-Time

Masters in Data Analytics in India- 2010-2023

Start Date : 27^th Mar 2023Duration : 9 Months
Format : Live Online /Self-Paced/Classroom

Course 1Python for Data Analyst
Course 2Data Analysis and Visualization
Course 3Databases – MS SQL and SQL Queries
Course 4Statistics for Data Analyst
Course 5Analytics with Excel
Course 6Analytics Microsoft Power BI
Course 7Analytics with Tableau
Course 8Cloud: AWS(Amazon Web Services)
Course 9Cloud: Microsoft Azure Fundamentals
Course 10Data Analyst - Live Projects
Master's Certificate

Future of Masters in Data Analytics

₹3 LPA to ₹12 LPA

A data analyst, on average, can make around ₹3 Lakh PA. On the other hand, an experienced analyst can make around ₹12 Lakh PA.

Job Opportunities

As per the reports of IBM, the number of data analyst jobs in the United States of America will cross the mark of 2,720,000 in the upcoming years.

Future Analytics

As per a survey, most of the companies, by the year 2023, will start storing their data for analysis in the public cloud warehouses.

ABOUT TOOLS COVERED CURRICULUM PROJECTS PLACEMENT ADMISSION PROCESS FEE STRUCTURE FAQ's ENROLL NOW

Program Overview

The data analytics training program will help you gain in detail knowledge of vital data analytics technologies. Moreover, the course will help you master the skills that are necessary to become a confident data analyst. For example, you will master skills like Tableau, R, Python, SQL, etc. The main aim of this training program is to help students develop skills that are essential for marketing themselves as skilled data analytics professionals. After going through this course, you can get a job as a:

Junior Data Analyst
Junior Data Scientist
Finance Analyst
Business Intelligence Analyst
Data Engineer

There are lots of excellent opportunities from which you can choose after completing the data analytics training program. This is because today, a large number of organizations and government agencies rely upon data analytics to improve their company's performance. As a result, today, every organization is searching for data experts such as data scientists, data engineers, etc. that can help them transform their data into powerful business insights. Thus, pursuing your career in the data analytics industry can be very beneficial for you.

According to a survey of IBM, the number of data analyst jobs in the United States of America will cross the mark of 2,720,000 in the upcoming years.
Ample of excellent opportunities are available for students who pursue their careers in the data analytics industry. For example, you can get a job as a data analyst, data scientist, etc.
As per a survey, most of the companies, by the year 2023, will start storing their data for analysis in the public cloud warehouses.
As per a survey, the data analytics market is expected to grow with a compound annual growth rate of 25% till the year 2030.

The popularity of data analytics is growing with every passing day. This course will help you gain in detail knowledge of vital data analytics technologies. Furthermore, the main aim of the training program is to help students become familiar with the advantages of using data analytics.

With project-based training and lots of quizzes and assignments, you will gain the knowledge and skills that are essential for getting placed as an analyst in a reputed company.

After completing the data analytics training program, you can easily get placed in a respected company or firm with handsome remuneration of ₹ 3,00,000-₹12,00,000.

As per a survey, 2,720,000 job opportunities will be created in the data analytics industry in the upcoming years.

The main aim of the data analytics training program is to make students familiar with all the concepts of data analytics and various tools used for performing data analytics. Besides this, you will learn to use data analytics to help a firm grow and improve its performance and profits.
Things you will learn:

Data fundamentals
How to work with different data tools?
Importance of data analytics
Data analysis

The objective of this data analytics course is to give students quality data analytics training to students and make them experts in working with different data analytics technologies. The course is developed in partnership with data experts and keeping in mind the changing demands of the data analytics industry.

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Tools Covered of Masters in Data Analytics

Masters in Data Analytics Curriculum

Course 1

Python for Data Analyst

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 science, you may convert voluminous data into meaningful contents.

Course Content

Introduction To Python

Installation and Working with Python

Understanding Python variables

Python basic Operators

Understanding the Python blocks.

Python Keyword and Identiers

Python Comments, Multiline Comments.

Python Indentation

Understating the concepts of Operators

Arithmetic

Relational

Logical

Assignment

Membership

Identity

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 using Numeric data types

Using string data type and string operations

Understanding Non-numeric data types

Understanding the concept of Casting and Boolean.

Strings

List

Tuples

Dictionary

Sets

Control Structure & Flow

Statements – if, else, elif

How to use nested IF and Else in Python

Loops

Loops and Control Statements.

Jumping Statements – Break, Continue, 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 Loop in Python

Use If and Else in for and While Loop

Examples of Looping with Break and Continue Statement

How to use IN or NOT IN keyword in Python Loop.

Python Function, Modules and Packages

Python Syntax

Function Call

Return Statement

Arguments in a function – Required, Default, Positional, Variable-length

Write an Empty Function in Python –pass statement.

Lamda/ Anonymous Function

*args and **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

Random functions in python

Programming using functions, modules & external packages

Map, Filter and Reduce function with Lambda Function

More example of Python Function

Python Date Time and Calendar

Day, Month, Year, Today, Weekday

IsoWeek day

Date Time

Time, Hour, Minute, Sec, Microsec

Time Delta and UTC

StrfTime, Now

Time stamp and Date Format

Month Calendar

Itermonthdates

Lots of Example on Python Calendar

Create 12-month Calendar

Strftime

Strptime

Format Code list of Data, Time and Cal

Locale’s appropriate date and time

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

Tuple

What is Tuple

Tuple Creation

Accessing Elements in Tuple

Changing a Tuple

Tuple Deletion

Tuple Count

Tuple Index

Tuple Membership

TupleBuilt in Function (Length, Sort)

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

What is Set

Set Creation

Add element to a Set

Remove elements from a Set

PythonSet Operations

Frozen Sets

Strings

What is Set

Set Creation

Add element to a Set

Remove elements from a Set

PythonSet Operations

Python Exception Handling

Python Errors and Built-in-Exceptions

Exception handing Try, Except and Finally

Catching Exceptions in Python

Catching Specic 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 Database Interaction

SQL Database connection using

Creating and searching tables

Reading and Storing cong information on database

Programming using database connections

Contacting user Through Emails Using Python

Installing SMTP Python Module

Sending Email

Reading from le and sending emails to all users

Reading an excel

Working With Excel

Reading an excel le using Python

Writing to an excel sheet using Python

Python| Reading an excel le

Python | Writing an excel le

Adjusting Rows and Column using Python

ArithmeticOperation in Excel le.

Play with Workbook, Sheets and Cells in Excel using Python

Creating and Removing Sheets

Formatting the Excel File Data

More example of Python Function

Complete Understanding of OS Module of Python

Check Dirs. (exist or not)

How to split path and extension

How to get user prole detail

Get the path of Desktop, Documents, Downloads etc.

Handle the File System Organization using OS

How to get any les and folder’s details using OS

Course 2

Data Analysis and Visualization

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.

Course Content

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

Course 3

Databases – MS SQL and SQL Queries

Microsoft SQL Server is a relational database management system (RDBMS) that supports a wide variety of transaction processing, business intelligence and analytics applications in corporate IT environments. In order to experiment with data through the creation of test environments, data scientists make use of SQL as their standard tool, and to carry out data analytics with the data that is stored in relational databases like Oracle, Microsoft SQL, MySQL, we need SQL.

Course Content

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

Course 4

Statistics for Data Analyst

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.

Course Content

Introduction to Data Analytics

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

Course 5

Analytics with Excel

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.

Course Content

Understanding Concepts of Excel

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

Ms Excel Advance

Advance Data Sorting

Multi-level sorting

Restoring data to original order after performing sorting

Sort by icons

Sort by colours

Lookup Functions

Lookup

VLookup

HLookup

Subtotal, Multi-Level Subtotal

Grouping Features

Column Wise

Row Wise

Consolidation With Several Worksheets

Filter

Auto Filter

Advance Filter

Printing of Raw & Column Heading on Each Page

Workbook Protection and Worksheet Protection

Specified Range Protection in Worksheet

Excel Data Analysis

Goal Seek

Scenario Manager

Data Table

Advance use of Data Tables in Excel

Reporting and Information Representation

Pivot Table

Pivot Chat

Slicer with Pivot Table & Chart

Generating MIS Report In Excel

Advance Functions of Excel

Math & Trig Functions

Text Functions

Lookup & Reference Function

Logical Functions & Date and Time Functions

Database Functions

Statistical Functions

Financial Functions

Functions for Calculation Depreciation

MIS Reporting & Dash Board

Dashboard Background

Dashboard Elements

Interactive Dashboards

Type of Reporting In India

Reporting Analyst

Indian Print Media Reporting

Audit Report

Accounting MIS Reports

HR Mis Reports

MIS Report Preparation Supplier, Exporter

Data Analysis

Costing Budgeting Mis Reporting

MIS Report For Manufacturing Company

MIS Reporting For Store And Billing

Product Performance Report

Member Performance Report

Customer-Wise Sales Report

Collections Report

Channel Stock Report

Prospect Analysis Report

Calling Reports

Expenses Report

Stock Controller MIS Reporting

Inventory Statement

Payroll Report

Salary Slip

Loan Assumption Sheet

Invoice Creation

Course 6

Analytics Microsoft Power BI

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.

Course Content

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

Course 7

Analytics with Tableau

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.

Course Content

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

Course 8

Cloud: AWS(Amazon Web Services)

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.

Course Content

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)

Demo of AMI Creation

Exercise

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

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

Cloud Watch with Agent

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

Hands on with scenario based

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

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,

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,

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 High Availability Design

AWS Best Practices (Cost +Security)

AWS Calculator & Consolidated Billing

Migrating to Cloud & AWS

Router S3 DNS

Public DNS

Private DNS

Routing policy

Records

Work with third party DNS as well

Cloud Formation

Stack

Templet

Json / Ymal

Installation of Linux

Configuration

Manage

Installation of app on Linux (apache / Nginx etc)

AWS cli configuration on Linux

Complete hands-on on Linux.

Scenario based lab and practical

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

Elastic Beanstalk

EFS / NFS (hands-on practice)

Hands-on practice on various Topics

Linux

Course 9

Cloud: Microsoft Azure Fundamentals

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

Course Content

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

Deploy and Manage Azure Compute Resources (25-30%)

Configure VMs for high availability and scalability

configure high availability

deploy and configure scale sets

Create and configure VMs

configure Azure Disk Encryption

move VMs from one resource group to another

manage VM sizes

add data discs

configure networking

redeploy VMs

Create and configure Web Apps

create and configure App Service

create and configure App Service Plans

Configure and Manage Virtual Networking (30-35%)

Implement and manage virtual networking

create and configure VNET peering

configure private and public IP addresses, network routes, network interface,

subnets, and virtual network

Configure name resolution

configure Azure DNS

configure custom DNS settings

configure a private or public DNS zone

Secure access to virtual networks

create security rules

associate an NSG to a subnet or network interface

evaluate effective security rules

deploy and configure Azure Firewall

deploy and configure Azure Bastion Service

NOT Implement Application Security Groups; DDoS

Configure load balancing

configure Application Gateway

configure an internal load balancer

configure load balancing rules

configure a public load balancer

troubleshoot load balancing

NOT Traffic Manager and Front Door and Private Link

Monitor and Back up Azure Resources (10-15%)

Implement backup and recovery

configure and review backup reports

perform backup and restore operations by using Azure Backup Service

create a Recovery Services Vault

use soft deletes to recover Azure VMs

Course 10

Data Analyst - Live Projects

The training offers complete career transitioning projects based on the current needs of the organization. These projects are guided by experts and help you to add more value to your profile. You will learn to initiate data analytics projects based on a high-level perspective helping you to understand and articulate the innovative solutions.

Course Content

Here is the project list you will going to work on

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

Master's Program Certificate

You will get certificate after completion of program

Course Structure

- 9 Months Online Program
- 120+ Hours of Intensive Learning
- 10+ Assigments & 4+ Projects
- 4 Live Projects

Career Assistance

- Build an Impressive Resume
- Get Tips from Trainer to Clear Interviews
- Attend Mock-Up Interviews with Experts
- Get Interviews & Get Hired

Get Ahead with Croma Campus master Certificate

Earn your certificate

Our Master program is exhaustive and this certificate is proof that you have taken a big leap in mastering the domain.

Differentiate yourself with a Masters Certificate

The knowledge and skill you've gained working on projects, simulation, case studies will set you ahead of competition.

Share your achievement

Talk about it on Linkedin, Twitter, Facebook, boost your resume or frame it- tell your friend and colleagues about it.

SAMPLE CERTIFICATE

Industry Project

Real-life Case Studies

Work on case studies based on top industry frameworks and connect your learning with real-time industry solutions right away.

Best Industry-Practitioners

All of our trainers and highly experienced, passionate about teaching and worked in the similar space for more than 3 years.

Acquire essential Industrial Skills

Wisely structured course content to help you in acquiring all the required industrial skills and grow like a superstar in the IT marketplace.

Hands-on Practical Knowledge

Case studies based on top industry frameworks help you to relate your learning with real-time based industry solutions.

Collaborative Learning

Take your career at the top with collaborative learning at the Croma Campus where you could learn and grow in groups.

Assignment & Quizzes

Practice different assignments and quizzes on different topics or at the end of each module to evaluate your skills and learning speed.

Placement & Recruitment Partners

Placement Assistance

We provide 100 percent placement assistance and most of our students are placed after completion of the training in top IT giants. We work on your resume, personality development, communication skills, soft-skills, along with the technical skills.

CAREERS AND SALARIES IN Masters in Data Analytics

Demand for data experts is expected to grow a lot in the market in the coming years. According to a survey, around 2,720,000 data analytics job opportunities will be created in the US job market. Moreover, a data expert can also get a hefty paycheck for his services. As per the Glassdoor website, data analytics can make approximately ₹3,00,000-₹12,00,000 PA. In simple words pursuing your career in the data analytics industry can be very beneficial for students.

Data analysts help a firm in interpreting and representing its data in different forms for extracting or deriving actionable insights. Moreover, an analyst also helps a firm in segregating and simplifying data from various systems. A competent analyst must be proficient in working with different Microsoft products like MS Excel, MS Access, MS SQL, etc. Besides this, he should have adequate knowledge about data mining, data modeling, and various data visualization tools/software. On average, a data analyst can make approximately ₹3,00,000-₹12,00,000 PA.

Data scientist helps a company to collect/analyze data that can be communicated to the stakeholders as actionable insights. A data scientist often works with complex data and requires expertise in data analytics, R programming, various data visualization tools, etc. On average, a data scientist can make approximately ₹5,00,000-₹21,00,000 PA.

A financial analyst is a data analyst who is an expert in the finance domain and uses financial data to provide actionable insights to a firm. This role is perfect for expert data analysts that have a background in finance. Besides this, a financial analyst must be an expert in working with different financial assets such as bonds, stocks, etc. On average, a financial analyst can make approximately ₹3,00,000 to 12,00,000 PA.

A business intelligence analyst uses BI tools for converting raw data of an organization into insights. A business intelligence analyst must be an expert in working with different BI tools and data visualization tools. Furthermore, he should be proficient in creating charts, graphs, and reports. On average, a business intelligence analyst can make approximately ₹4,00,000-₹14,00,000 PA.

A data engineer has to work with massive data sets. Moreover, he is responsible for optimizing the infrastructure as well as the data analytics process of an organization. Data engineers must be proficient in data visualization and programming. Plus, he should also have some sort of experience in developing/testing solutions. On average, a data engineer can make approximately ₹4,00,000-₹16,00,000 PA.

RECRUITMENT PARTNERS

On the completion of the course, you may work in various domains like manufacturing, It, healthcare, telecom, and more. Also, most of the students get 200 percent hike after completing this course. The average you will get 6 lac p.a. and for a little more efforts you may acquire salary packages up to 12 lacs p.a.

Admission Process

Important Date & Time

You can apply for the master program online at our site. Mark the important date and time related to the program and stay in touch with our team to get the information about the program in detail.

Enrollment Criteria

Once you submit your profile online, it will be reviewed by our expert team closely for the eligibility like graduation degree, basic programming skills, etc. Eligible candidates can move to the next step quickly.

Final Enrollment Process

Eligible candidates have to appear for the online assessment based on your graduation and basic programming knowledge. Candidates who clear the exam will appear for the interview and finally they can join the program.

WEEKDAY

Rs.85,500 ~~Rs.90000~~

27-Mar-2023*

Take class during weekdays and utilize your weekend for practice.

Get regular training by Industry Experts.

Get Proper guidance on certifications.