Do I need a technical background to join the Data Analytics training in Canada?

No, you don’t need a technical background. We start from the basics and guide you through every step of the learning process.

What job opportunities can I expect after completing the Data Analytics course in Canada?

You can apply for roles like Data Analyst, Business Analyst, or Data Visualization Specialist in industries such as tech, healthcare, and finance.

How does this course help me stand out in the competitive job market in Canada?

The course gives you hands-on experience with top tools like SQL, Python, Tableau, and Power BI, along with real-world projects to help build your portfolio.

How much time should I dedicate weekly to successfully complete the Data Analytics course?

You should aim to dedicate about 8-12 hours per week, covering live sessions, assignments, and hands-on projects.

Can I switch to a more advanced Data Science course after completing this Data Analytics course?

Yes, this course provides a solid foundation for more advanced Data Science programs, preparing you with key skills and tools.

Do I need a technical background to join the Data Analytics training in Canada?

No, you don’t need a technical background. We start from the basics and guide you through every step of the learning process.

What job opportunities can I expect after completing the Data Analytics course in Canada?

You can apply for roles like Data Analyst, Business Analyst, or Data Visualization Specialist in industries such as tech, healthcare, and finance.

How does this course help me stand out in the competitive job market in Canada?

The course gives you hands-on experience with top tools like SQL, Python, Tableau, and Power BI, along with real-world projects to help build your portfolio.

How much time should I dedicate weekly to successfully complete the Data Analytics course?

You should aim to dedicate about 8-12 hours per week, covering live sessions, assignments, and hands-on projects.

Can I switch to a more advanced Data Science course after completing this Data Analytics course?

Yes, this course provides a solid foundation for more advanced Data Science programs, preparing you with key skills and tools.

Data Analytics Course in Canada

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Item: Data Analytics Training Program
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Learn how to extract data for analysis. Join now and learn under an expert data analyst.

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Are you excited to dive into the world of data analytics in Canada This Data Analytics course in Canada is designed to teach you how to work with data, uncover valuable insights, and help businesses make smarter decisions. Canada is a leader in technology, finance, and healthcare, making it an ideal place to grow your career in data analytics. By the end of this course, youll have the skills to analyze data and create reports that make trends easy to understand and drive decision-making.

The best Data Analytics courses in Canada are created to give you a complete learning experience. Heres what youll achieve:

Data Collection & Management: Learn how to gather, clean, and organize data from different sources to ensure it is accurate and useful.

Data Visualization: Use tools like Excel, Tableau, and Power BI to make complex data easy to understand through charts, graphs, and dashboards.

Statistical Analysis: Learn techniques to analyze data and make decisions based on real-world data.

Predictive Analysis: Understand how to use models and tools to predict future trends and make informed decisions.

Data Tools & Software: Get hands-on experience with popular tools like SQL, Python, and R, which are essential for any data analyst.

Salary Range For Freshers:

Starting Salary Range: CAD 45,000 to CAD 70,000 annually.

In Indian Rupees: 27,00,000 to 42,00,000 per year.

Salary Based on Role:

Data Analyst: CAD 50,000 to CAD 65,000 annually.

Business Analyst: CAD 55,000 to CAD 70,000 annually.

Growth Potential: With experience, you can earn CAD 90,000 to CAD 120,000 annually (54,00,000 to 72,00,000).

There are plenty of career growth opportunities in Data Analytics in Canada. Heres how your career could progress:

Entry-Level Jobs: As a beginner, you can work as a Data Analyst, Business Analyst, or Research Analyst. These roles help companies understand their data and make smarter decisions.

Mid-Level Roles: After gaining experience, you could become a Senior Data Analyst, Data Consultant, or Operations Analyst. In these roles, youll handle more complex data tasks and assist in business strategy.

Leadership Roles: With more experience, you can move up to roles like Data Analytics Manager, Business Intelligence Manager, or Chief Data Officer (CDO), where youll lead teams and make key data-driven decisions.

The popularity of Data Analytics in Canada is increasing because of the growing importance of data in various industries. Heres why this course is in demand:

Growing Industry Demand: Businesses in tech, healthcare, and other sectors need data analysts to help make data-driven decisions.

High Earning Potential: Data analysts earn competitive salaries, with a lot of opportunities to increase their pay as they gain experience.

Recognition in Canada & Worldwide: A certificate from a Canadian institution is recognized globally, opening doors for opportunities both in Canada and abroad.

Versatility Across Industries: Almost every sector, from tech to healthcare and finance, needs skilled data analysts.

Here are some of the top job roles youll be prepared for after completing your Data Analytics course in Canada:

Data Analyst: Analyze data to identify trends and patterns that help businesses improve their performance.

Business Analyst: Work with business teams to understand data needs, develop reports, and provide insights to improve decision-making.

Data Scientist: Many data analysts move into this advanced role, using statistical techniques and machine learning to predict trends.

Operations Analyst: Help businesses optimize operations by analyzing internal data and suggesting improvements.

Data Visualization Specialist: Create easy-to-understand visual representations of data for businesses to make informed decisions.

Data analysts are needed across many industries. Some of the top sectors hiring data professionals in Canada include:

Tech & IT Companies: Technology firms hire data analysts to optimize software development and improve user experiences.

Finance & Banking: Financial institutions rely on data analysts to assess risks, forecast trends, and improve strategies.

Healthcare: Hospitals and healthcare organizations use data analytics to improve patient care and reduce costs.

Retail & E-Commerce: Retail businesses need data analysts to understand customer behavior, optimize inventory, and boost marketing strategies.

Government & Public Sector: Government agencies use data to make policy decisions, improve services, and monitor the economy.

During the Data Analytics course in Canada, youll get hands-on experience with industry-standard tools:

Excel: Learn how to organize, analyze, and visualize data.

Tableau: Master this tool to create dashboards and insightful reports.

Power BI: Get comfortable using this tool to create business intelligence reports.

SQL: Learn how to use Structured Query Language (SQL) to manage and extract data from databases.

Python & R: Explore programming basics to analyze data and build statistical models.

Google Analytics: Learn how to analyze web data and optimize performance.

In this course, youll work on real-world projects that will help you apply what youve learned:

Customer Data Analysis: Analyze customer data to understand their buying patterns and trends.

Sales Data Analysis: Work with sales data to identify top-performing products and seasonal trends.

Market Research Project: Use data to analyze the market and recommend business strategies.

Predictive Analytics Project: Build predictive models to forecast future trends.

Data Visualization Project: Create reports and dashboards that help businesses make data-driven decisions.

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

When you successfully complete your Data Analytics Training in Canada, youll receive a certificate that proves your skills and boosts your career.

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Why Should You Learn Data Analytics?

The outburst of data transforming the businesses.

Helping out businesses to adapt to the new trends.

Data scientists have a tremendous impact on businesses.

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CURRICULUM & PROJECTS

Data Analytics Training Program

Introduction

What is Data Analytic

Common Terms in Data Analytics

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 Analytics tools & their popularity.

Data Analytics Methodology & problem-solving framework.

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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

Subtotal, Multi-Level Subtotal

Grouping Features

Consolidation With Several Worksheets

Filter

Printing of Raw & Column Heading on Each Page

Workbook Protection and Worksheet Protection

Specified Range Protection in Worksheet

Excel Data Analysis

Data Table

Pivot Table

Generating MIS Report In Excel

Text Functions

Lookup & Reference Function

Logical Functions & Date and Time Functions

Database Functions

Statistical Functions

Financial Functions

Functions for Calculation Depreciation

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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

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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

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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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

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

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

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 folders details using OS

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Data Analysis and Visualization using Pandas.

Statistics

Pandas

Data Analysis and Visualization using NumPy and MatPlotLib

NumPy

MatPlotLib

Introduction to Data Visualization with Seaborn

Introduction to Seaborn

Visualizing Two Quantitative Variables

Visualizing a Categorical and a Quantitative Variable

Customizing Seaborn Plots

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Capstone Project

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