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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.
Data Analytics Course in Canada
- 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:
- Salary Based on Role:
- Growth Potential: With experience, you can earn CAD 90,000 to CAD 120,000 annually (54,00,000 to 72,00,000).
Starting Salary Range: CAD 45,000 to CAD 70,000 annually.
In Indian Rupees: 27,00,000 to 42,00,000 per year.
Data Analyst: CAD 50,000 to CAD 65,000 annually.
Business Analyst: CAD 55,000 to CAD 70,000 annually.
- 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:
- These projects will help you build a strong portfolio to show potential employers.
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.
- When you successfully complete your Data Analytics Training in Canada, youll receive a certificate that proves your skills and boosts your career.
- You May Also Read:
Why Should You Learn Data Analytics?
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CURRICULUM & PROJECTS
Data Analytics Training Program
- 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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- Installation and Working with Python
- Understanding Python variables
- Python basic Operators
- Understanding the Python blocks.
- Python Comments, Multiline Comments.
- Python Indentation
- Understating the concepts of Operators
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- Categorical Data
- Numerical Data
- Mean
- Median
- Mode
- Outliers
- Range
- Interquartile range
- Correlation
- Standard Deviation
- Variance
- Box plot
- 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
- Data Preparation
- Feature Engineering
- Feature Scaling
- Datasets
- Dimensionality Reduction
- Anomaly Detection
- Parameter Estimation
- Data and Knowledge
- Selected Applications in Data Mining
- 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
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- Statistics
- Pandas
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- 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 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 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
- 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
- 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
- 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
- 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
- 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
- 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
- Cursor declaration and Life cycle
- STATIC
- DYNAMIC
- SCROLL Cursors
- FORWARD_ONLY and LOCAL Cursors
- KEYSET Cursors with Complex SPs
- ACID Properties and Scope
- EXPLICIT Transaction types
- IMPLICIT Transactions and options
- AUTOCOMMIT Transaction and usage
- AI Tools:
- Copilot in Azure Data Studio or GitHub for SQL suggestions
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- Creation of Excel Sheet Data
- Range Name, Format Painter
- Conditional Formatting, Wrap Text, Merge & Centre
- Sort, Filter, Advance Filter
- Different type of Chart Creations
- Auditing, (Trace Precedents, Trace Dependents)Print Area
- Data Validations, Consolidate, Subtotal
- What if Analysis (Data Table, Goal Seek, Scenario)
- Solver, Freeze Panes
- Various Simple Functions in Excel(Sum, Average, Max, Min)
- Real Life Assignment work
- Advance Data Sorting
- Multi-level sorting
- Restoring data to original order after performing sorting
- Sort by icons
- Sort by colours
- Lookup Functions
- 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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- 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
- Extracting data from various sources
- Workspaces in Power BI
- 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
- 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
- What is DAX
- Data Types in DAX
- Calculation Types
- Syntax, Functions, Context Options
- DAX Functions
- Measures in DAX
- Measures and Calculated Columns
- ROW Context and Filter Context in DAX
- Operators in DAX - Real-time Usage
- Quick Measures in DAX - Auto validations
- In-Memory Processing DAX Performance
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- 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
- Features of Tableau Desktop
- Connect to data from File and Database
- Types of Connections
- Joins and Unions
- Data Blending
- Tableau Desktop User Interface
- Visual Analytics
- Basic Charts Bar Chart, Line Chart, and Pie Chart
- Hierarchies
- Data Granularity
- Highlighting
- Sorting
- Filtering
- Grouping
- Sets
- 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
- Parameters
- Tool tips
- Trend lines
- Reference lines
- Forecasting
- Clustering
- Count Customer by Order
- Profit per Business Day
- Comparative Sales
- Profit Vs Target
- Finding the second order date
- Cohort Analysis
- Introduction to Geographic Visualizations
- Manually assigning Geographical Locations
- Types of Maps
- Spatial Files
- Custom Geocoding
- Polygon Maps
- Web Map Services
- Background Images
- Box and Whiskers 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
- 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
- Tableau Tips and Tricks
- Choosing the right type of Chart
- Format Style
- Data Visualization best practices
- 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
- Ask Data: Natural Language Data Exploration
- Explain Data: Automatic statistical insights
- Tableau GPT (Einstein Copilot - Salesforce)
- AI forecasting in visualizations
- Integration with Python (TabPy) and R
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