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Home /Data Analytics & BI/ Data Analytics Master Course in Canada

Master the Fundamental Concepts of Data Analytics. Enroll today and become a skilled Data Analytics expert.

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Course Duration

32 Hrs.

Live Project

2 Project

Certification Pass

Guaranteed

Training Format

Live Online /Self-Paced/Classroom

Speciality

300+

Professionals Trained

Batches every month

20+

Countries & Counting

100+

Corporate Served

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The Data Analytics Master Course in Canada helps you learn how to analyze data to make better business decisions. It covers skills like data collection, cleaning, visualization, and using tools like Excel, SQL, Python, and Tableau. As more businesses rely on data for decision-making, this course prepares you for jobs in Data Analysis, Data Science, and Business Analytics. Whether you're new to the field or want to upgrade your skills, this course will give you the knowledge and tools to succeed in the data analytics world.

The Data Analytics Master Training in Canada aims to:

Teach Data Collection & Cleaning: Learn how to gather and clean data for analysis.

Master Data Analysis Techniques: Understand how to analyze data using different methods and tools.

Work with Analytics Tools: Gain hands-on experience with Excel, SQL, Python, and Tableau.

Create Data Visualizations: Learn how to display data in charts, graphs, and dashboards to make it easier to understand.

Apply Data-Driven Decision Making: Use data to solve problems and help businesses make smarter decisions.

Complete Real-Life Projects: Work on real-world projects to practice your skills.

After completing the Data Analytics Master Course, freshers can expect to earn:

Starting Salary: Between CAD 50,000 - CAD 70,000 per year (approx. 30,00,000 - 42,00,000).

With Experience: After a few years, salaries can go up to CAD 80,000 - CAD 100,000 (approx. 48,00,000 - 60,00,000).

After completing the course, you can grow in your career by moving into:

Entry-Level Jobs: Start as a Data Analyst or Junior Data Scientist.

Mid-Level Jobs: With experience, you can become a Senior Data Analyst or Data Engineer.

Advanced Roles: After a few years, you can move into roles like Data Scientist or Data Analytics Consultant.

Leadership Positions: You could also work your way up to become a Data Analytics Manager or Chief Data Officer.

The Data Analytics Master Training in Canada is popular because:

High Demand: Theres a growing need for data analytics professionals in many industries.

Versatile Skills: Data analytics skills are used in industries like finance, healthcare, marketing, and more.

Good Salaries: Data analysts earn good salaries and have plenty of opportunities for career growth.

Practical Learning: You will get hands-on experience with popular tools like SQL, Python, and Tableau, which are essential in the industry.

With the Data Analytics Master Training in Canada, you can apply for jobs like:

Data Analyst: Analyze and interpret data to provide insights for businesses.

Business Intelligence Analyst: Use data to help companies make smart decisions.

Data Scientist: Apply advanced techniques like machine learning to analyze large datasets.

Data Engineer: Build and maintain the systems that store and process data.

Data Analytics Consultant: Advise companies on how to use data to solve problems and improve performance.

Data analytics professionals are needed in various industries, including:

Technology: Tech companies need data analysts to work on product and customer data.

Finance: Financial companies use data to make investment decisions and manage risks.

Healthcare: Healthcare providers use data to improve patient care and manage costs.

Retail: Retail companies analyze data to improve sales and customer satisfaction.

Government: Government agencies use data for policy-making and managing resources.

After finishing the course, you will receive:

Certificate of Completion: Shows youve learned the skills needed for a career in data analytics.

Specialization Certificates: You may also get certificates in specific areas like Python, SQL, or Tableau.

The course fee typically ranges between:

CAD 1,000 - CAD 3,000 (approximately 60,000 - 1,80,000).

This includes all the materials, practical sessions, and tools you will need during the course.

You can get an official certification after completing the course, which is helpful for your career. The certification fee usually ranges from:

CAD 100 - CAD 300 (approximately 6,000 - 18,000).

This certification validates your skills in data analytics and is a valuable asset for your resume.

During the course, you will work on projects that help you practice your skills:

Data Cleaning Project: Learn how to clean raw data to make it ready for analysis.

Data Visualization Project: Create dashboards using Tableau to display data in an easy-to-understand way.

Business Analysis Project: Use Excel and SQL to analyze business data and create reports.

Predictive Analytics Project: Use Python to create models that predict future trends.

These projects help you build a strong portfolio to show employers.

Our placement process is designed to help you get a job after completing the Data Analytics Master Training in Canada:

Career Counseling: We guide you in understanding career paths and job opportunities.

Resume Building: Learn how to create a strong resume that highlights your skills.

Interview Preparation: Practice with mock interviews to gain confidence before real interviews.

Job Referrals: We refer you to job openings with top companies.

Job Search Support: Our team helps you with job applications and interviews to increase your chances of getting hired.

Why Should You Learn Data Analytics Course?

The outburst of data transforming the businesses.

Helping out businesses to adapt to the new trends.

Data scientists have a tremendous impact on businesses.

It aims to capture out with the development technology.

Opens out the door for various opportunities in corporate.

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CURRICULUM & PROJECTS

Data Analyst Certification Training

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. 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.

Things you will learn:

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.

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

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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.

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

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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.

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

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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 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

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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.

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

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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

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

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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

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

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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)

Handling dates in R :

Date and date-time classes in R

Formatting dates for modeling

Exploratory Data Analysis (Descriptive Statistics) :

Continuous data

Distributions

Quantiles, mean

Bi-modal distributions

Histograms, box-plots

Categorical data

Tables

Bar plots

Group by calculations

Split-apply-combine

Reshaping and pivoting data in R (long to wide with aggregation)

Melt and cast

Working with text data :

Finding and matching patterns in text

Stringer package for text manipulation

Introduction to regular expressions in R

Categorical data wrangling with forcats

Control flow & functions :

Truth testing

Branching

Looping

Functions

Parameters

Return values

Variable scope

Exception handling

Applying functions across dimensions

Sapply, lapply, apply

Programming with map and purr

Graphics in R Overview :

Base graphics system in R

Scatterplots, histograms, bar charts, box and whiskers, dot plots

Labels, legends, titles, axes

Exporting graphics to different formats

Advanced R graphics :

Understanding the grammar of graphics

Quick plots (qplot function)

Building graphics by pieces (ggplot function)

Understanding geoms (geometries)

Linking chart elements to variable values

Controlling legends and axes

Exporting graphics

Inferential Statistics :

Bivariate correlation

T-test and non-parametric equivalents

Chi-squared test

General Linear Regression Models in R :

Understanding formulas

Linear and logistic regression models

Regression plots

Confounding / interaction in regression

Evaluating residuals

Scoring new data from models (prediction)

Useful plots from regression models

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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.

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,
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,

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

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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

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

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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.

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

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+ More Lessons

Course Design By

Nasscom & Wipro

Course Offered By

Croma Campus

Real

Stories

success

inspiration

Abhishek

career upgrad

Upasana Singh

career upgrad

Shashank

career upgrad

Abhishek Rawat

career upgrad

Course Duration

32 Hrs.

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Weekday1 Hr/Day

Weekend2 Hr/Day

Training ModeClassroom/Online

Flexible Batches For You

31-May-2025*
Weekend
SAT - SUN
Mor | Aft | Eve - Slot

02-Jun-2025*
Weekday
MON - FRI
Mor | Aft | Eve - Slot

04-Jun-2025*
Weekday
MON - FRI
Mor | Aft | Eve - Slot

31-May-2025*
Weekend

SAT - SUN

Mor | Aft | Eve - Slot

02-Jun-2025*
Weekday

MON - FRI

Mor | Aft | Eve - Slot

04-Jun-2025*
Weekday

MON - FRI

Mor | Aft | Eve - Slot

Course Price :

For Canadian

1,650

1,485 10 % OFF, Save logo

165

Expires in: 00D:00H:00M:00S

Program fees are indicative only* Know more

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