- The Python Course in Canada is designed to teach you Python, one of the most versatile and popular programming languages in the world today. Python is used in a variety of fields such as Data Science, Web Development, Machine Learning (AI), and Automation. Whether you're just starting or have some experience in programming, this course will teach you everything from Python basics to advanced concepts. You'll be able to write Python code, develop software, analyze data, and build real-world projects. Its an ideal choice for anyone looking to enter the technology field or upgrade their skills.
- By the time you complete the Python Training in Canada, you will achieve the following objectives:
Understand Python Syntax: Learn how Python works, including how to write simple code, use loops, and create functions.
Master Python Data Structures: Youll understand how to use lists, dictionaries, tuples, and sets to store and manage data efficiently.
Work with Object-Oriented Programming (OOP): Learn to design and organize your code into reusable objects and classes.
Build Real-World Projects: From building a web scraper that collects information from websites to creating interactive web applications, you will work on projects that solve real-world problems.
Explore Popular Libraries and Frameworks: You will work with libraries like NumPy, Pandas, Matplotlib, and frameworks like Flask and Django to make development easier and faster.
Understand Code Efficiency: Focus on writing code that is optimized, clean, and easy to understand while solving real-world problems.
- Freshers with a Python Training in Canada can expect the following salary range in Canada:
Starting Salary: New graduates or beginners can earn CAD 50,000 - CAD 65,000 per year (roughly 30,00,000 - 39,00,000 in Indian Rupees).
With Experience: After gaining a few years of experience, your salary can go up to CAD 70,000 - CAD 90,000per year (approximately 42,00,000 - 54,00,000).
- Once you have completed the Python Training in Canada, heres what you can expect for your career growth:
Entry-Level Roles: You will be qualified to start as a Junior Python Developer, Software Developer, or Web Developer.
Mid-Level Roles: With some work experience, you can move to higher roles like Senior Python Developer, Data Scientist, or Automation Engineer.
Advanced Specializations: You can further specialize in areas like Machine Learning, Artificial Intelligence, or Data Engineering.
Leadership Roles: With several years of experience, you can move into roles like Tech Lead, Engineering Manager, or Solutions Architect.
- The Python Training in Canada is widely popular for a few key reasons:
High Demand for Python Developers: Python is used across many sectors like Data Science, Web Development, and Machine Learning, making developers highly sought after.
Ease of Learning: Python is known for its simple and clean syntax, which makes it easier to learn for beginners and experienced developers alike.
Versatility: Python can be used for a wide range of projects, from building websites to working with big data, making it an all-purpose programming language.
High Salaries: Python developers are paid well in Canada, and as the demand for Python grows, so do the salary opportunities.
- Once you complete the Python Training in Canada, you will be qualified for several exciting job roles, such as:
Python Developer: Writing Python code to build applications, websites, or backend systems.
Data Scientist: Using Python to collect, clean, and analyze data to uncover trends and help businesses make decisions.
Machine Learning Engineer: Developing algorithms and machine learning models using Python libraries like TensorFlow and Scikit-learn.
Web Developer: Building websites and web apps using frameworks like Flask or Django.
Automation Engineer: Writing Python scripts to automate repetitive tasks and processes in industries like software development, finance, and more.
- Python developers are in demand across multiple industries, including:
Technology & Software Development: Python is the main language used to create software applications, websites, and tools.
Data Science and AI: Data scientists and AI engineers use Python to build algorithms and machine learning models.
Finance: Python is used for building trading algorithms, financial data analysis, and risk management models.
Healthcare: Python is used for analyzing medical data and building healthcare applications.
E-Commerce: Many online businesses use Python to build websites and manage user data.
- Upon completing the Python course, you will earn certifications that help prove your skills to employers.
- The Python course fee in Canada varies based on the institution and course duration:
Average Course Fee: CAD 500 - CAD 1,500 (approximately 30,000 - 90,000).
- Upon completing the course, you can opt for an official certification, which adds value to your resume. The cost of official certifications generally ranges between:
Official Certification Fee: CAD 100 - CAD 250 (approximately 6,000 - 15,000).
- During this course, you will work on hands-on projects that help solidify your skills:
Web Scraper: A tool built with Python to collect data from websites.
Data Analysis: Use libraries like Pandas and Matplotlib to analyze large datasets and create visualizations.
Web Development: Build a website using the Flask or Django framework.
Machine Learning: Learn to develop a simple machine learning model using Scikit-learn to make predictions.
- After completing the Python Training in Canada, we offer full support in helping you get a job:
Career Counseling: Receive guidance on how to create an effective resume and cover letter to showcase your Python skills.
Interview Preparation: We offer mock interviews where you can practice and get feedback, so you feel more confident during real interviews.
Job Referrals: We connect you with companies actively looking for Python developers and refer you for available positions.
Job Search Assistance: We help you understand how to find job openings, apply effectively, and communicate with employers.
Placement Assistance: With our guidance and your skills, youll have a better chance of securing a great job in Python development.
Why Should You Learn Python?
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Course Duration
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CURRICULUM & PROJECTS
Python Certification Training
- 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
- Arithmetic
- Relational
- Logical
- Assignment
- Membership
- Identity
- 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
- 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
- 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 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
- 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
- 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)
- 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.)
- What is Set
- Set Creation
- Add element to a Set
- Remove elements from a Set
- PythonSet Operations
- Frozen Sets
- What is Set
- Set Creation
- Add element to a Set
- Remove elements from a Set
- PythonSet Operations
- 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
- 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
- SQL Database connection using
- Creating and searching tables
- Reading and Storing cong information on database
- Programming using database connections
- 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
- 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
- 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
- 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 Nonvalues
- 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)
- 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
- NumPys Mean and Axis
- NumPys Mode, Median and Sum Function
- NumPys Sort Function and More
- Bar Chart using Python MatPlotLib
- Column Chart using Python MatPlotLib
- Pie Chart using Python MatPlotLib
- Area Chart using Python MatPlotLib
- Scatter Plot Chart using Python MatPlotLib
- Play with Charts Properties Using MatPlotLib
- Export the Chart as Image
- Understanding plt. subplots () notation
- Legend Alignment of Chart using MatPlotLib
- Create Charts as Image
- Other Useful Properties of Charts.
- Complete Understanding of Histograms
- Plotting Different Charts, Labels, and Labels Alignment etc.
- Introduction to Seaborn
- 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! Whats next
Mock Interviews

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FAQ's
No, this course is beginner-friendly and starts from the basics. It’s suitable for people with no prior programming experience.
The course typically lasts for 6-8 weeks, depending on your pace.
You can work as a Python Developer, Data Scientist, Machine Learning Engineer, Web Developer, or Automation Engineer.
Yes, you will receive a Certified Python Developer certification after completing the course.
Yes, there are free resources available, but a structured course with practical projects and certifications offers much more value and better career opportunities.

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