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  • The Data Science Course in Canada is a comprehensive program designed to help you master data analytics, machine learning, and artificial intelligence. Whether youre a beginner or an experienced professional, this course will provide hands-on training in Python, R, SQL, cloud computing, big data, and AI applications.
  • With the growing demand for data-driven decision-making, companies worldwide are looking for skilled data scientists. This course ensures you gain real-world experience through live projects, industry case studies, and expert mentorship. By the end of this program, youll be ready to apply for high-paying jobs in Canada and globally.

Data Science Course in Canada

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  • Reasons for Choosing Data Science Course in Canada:
    • Industry-Relevant Curriculum Learn the latest technologies and tools used by top companies.

      100% Hands-On Learning Work on real-world datasets and live projects.

      Placement Assistance Resume building, mock interviews, and job referrals.

      Flexible Learning Options Choose from live online classes or recorded sessions.

      Certifications Included Earn globally recognized certificates from top tech companies.

      Internship & Networking Gain real-world experience with industry mentors.

  • This Data Science Training in Canada is designed to help students develop expertise in data science and AI. You will:
    • Learn Python, R, and SQL to manipulate and analyze data

      Master data visualization with Tableau, Power BI, and Matplotlib

      Understand machine learning and deep learning using TensorFlow and Scikit-Learn

      Work on cloud computing platforms like AWS, Google Cloud, and Azure

      Use Big Data tools like Hadoop and Spark for large-scale data processing

      Develop predictive models for real-world business applications

      Prepare for global certifications in data science and AI

  • This course includes industry-focused projects to ensure you get hands-on experience with real-world challenges.
  • Project 1: Customer Churn Prediction (Telecom Industry)
  • What You Will Learn:
    • Analyze customer data and identify factors causing customer churn

      Build a machine learning model to predict which customers are likely to leave

      Develop retention strategies based on data insights

  • Real-World Benefit: This project helps companies improve customer retention, saving millions in lost revenue.
  • Project 2: House Price Prediction (Real Estate Analytics)
  • What You Will Learn:
    • Use regression models to predict property prices

      Understand how factors like location, size, and demand affect pricing

      Implement feature engineering for better model accuracy

  • Real-World Benefit: Real estate firms use such models to make data-driven pricing decisions.
  • Project 3: Sentiment Analysis (Social Media & Marketing)
  • What You Will Learn:
    • Analyze customer reviews and social media comments.

      Use NLP (Natural Language Processing) to classify sentiments (positive, negative, neutral).

      Build a dashboard to track brand reputation over time.

  • Real-World Benefit: This helps brands improve customer experience by understanding public opinion.
  • Project 4: Fraud Detection (Banking & Finance)
  • What You Will Learn:
    • Build a fraud detection model using machine learning

      Analyze transaction patterns to identify fraudulent activities

      Deploy the model using cloud platforms

  • Real-World Benefit: Financial institutions save billions by preventing fraud before it happens.
  • Project 5: Recommendation System (E-commerce & Streaming)
  • What You Will Learn:
    • Develop a recommendation engine like Amazon & Netflix

      Use collaborative filtering and deep learning models

      Improve customer engagement by providing personalized recommendations

  • Real-World Benefit: Companies like Amazon, Flipkart, and Netflix use such systems to boost sales and customer satisfaction.

  • Tools Covered in Data Science Course in Canada:
    • Programming Languages Python, R, SQL for data manipulation and analysis

      Data Visualization Tableau, Power BI, Matplotlib for creating insightful visuals

      Machine Learning TensorFlow, Scikit-Learn for building predictive models

      Cloud Computing AWS, Google Cloud, Azure for scalable data solutions

      Big Data Processing Hadoop, Spark for handling large datasets

  • Completing Data Science in Canada can land you high-paying jobs, with freshers earning between 42 lakhs to 60 lakhs per year.
  • Job-wise Salary Breakdown:
    • Data Analyst 40-55 LPA

      Machine Learning Engineer 50-75 LPA

      Data Scientist 60-85 LPA

      AI Engineer 70-90 LPA

  • Long-Term Growth: With experience, salaries can go up to 1.5 Cr per year, making data science one of the most lucrative career choices today.

  • The demand for data scientists is booming, and career growth opportunities are endless.
  • Step-by-Step Career Path:
    • Entry-level Data Analyst / Business Analyst

      Mid-level Data Scientist / Machine Learning Engineer

      Senior-level AI Engineer / Data Architect

      Leadership roles Chief Data Officer / AI Strategist

  • Hiring Companies: Google, Amazon, Microsoft, Deloitte, IBM, Facebook, Tesla, and many more!

  • After completing this Data Science Training in Canada, you will receive a course completion certificate and be prepared for:
    • Google Data Analytics Certification

      IBM Data Science Professional Certificate

      AWS Certified Data Analytics

      Microsoft Azure Data Scientist Associate

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CURRICULUM & PROJECTS

Data Science Training Program

    NA

    • Introduction To Python
      • Installation and Working with Python
      • Understanding Python variables
      • Python basic Operators
      • Understanding the Python blocks.
      • Version Control with Git & GitHub
    • 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
      • Basic SQL, DDL and DML commands
      • 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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    NA

    • Data Analysis and Visualization using 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 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)
    • Data Analysis and Visualization using 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
      • NumPys Mean and Axis
      • NumPys Mode, Median and Sum Function
      • NumPys Sort Function and More
    • Data Analysis and Visualization using 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
      • 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
      • Visualizing a Categorical and a Quantitative Variable
      • Customizing Seaborn Plots
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    NA

    • Foundation for AI: Learn traditional ML models, evaluation, and workflows.
      • Introduction to ML, AI, and Deep Learning
      • Types of ML (Supervised, Unsupervised, Reinforcement)
      • ML Pipeline: Data Cleaning, Feature Engineering
      • Common ML Algorithms: Linear, Logistic, DT, RF, SVM, KNN
      • Model Evaluation: Accuracy, Precision, Recall, F1, ROC-AUC
      • Overfitting, Underfitting, Cross-Validation
      • Hands-on Project: Titanic Dataset (or similar)
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    NA

    • Understand the inner workings of neural networks and train them with Keras.
      • Introduction to Neural Networks & Deep Learning
      • Activation Functions (ReLU, Sigmoid, Tanh)
      • Feedforward Neural Network
      • Backpropagation & Gradient Descent
      • Learning Rate, Schedulers & Optimizers (SGD, Adam, RMSProp)
      • Softmax, Cross-Entropy Loss
      • Keras Basics: Sequential API & Functional API
      • Fully Connected Layer Forward/Backward Pass
      • Regularization Dropout, Batch Normalization
      • Data Preprocessing & Data Augmentation
      • Weight Initialization Strategies
      • Babysitting Learning: Overfit detection, TensorBoard Monitoring
      • Hands-on: MLP on MNIST / Tabular data (e.g. HR Analytics)
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    NA

    • Master CNNs, object detection, segmentation, and deployment.
      • Basics of Images, Image Preprocessing
      • Convolution: 2D Conv, Forward & Backward
      • Pooling, Padding, Stride, Transposed Conv
      • CNN Architectures: LeNet, AlexNet, VGG, ResNet
      • GPU vs CPU for DL
      • Transfer Learning: Inception, MobileNet, fine-tuning
      • Semantic Segmentation using UNet
      • Object Detection YOLO, SSD, Region Proposal
      • Bounding Box Regressor
      • Siamese Networks for Similarity Search
      • Hands-on:
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    NA

    • Train text models from scratch and with BERT.
      • Introduction to NLP and Use Cases
      • Preprocessing: Tokenization, Lemmatization, Stopwords, Normalization
      • Feature Extraction: BOW, TF-IDF, N-Grams
      • Word Embeddings: Word2Vec, GloVe, Dense Vectors
      • POS Tagging, Named Entity Recognition
      • RNN, LSTM Forward Pass and BPTT
      • Advanced LSTM Applications + Architectures
      • Attention Mechanism + Encoder-Decoder
      • Transformers, BERT, Hugging Face Pipelines
      • NLP Evaluation Metrics: BLEU, ROUGE
      • Hands-on:
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    NA

    • Take models from notebooks to real-world applications.
      • Saving & Loading Models (Pickle, Joblib, Keras)
      • Flask vs FastAPI Serving ML models
      • Streamlit/Gradio for Web Apps
      • Hosting Models on Hugging Face Spaces, Streamlit Cloud
      • MLflow Intro Model Tracking & Versioning
      • Hands-on:
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    NA

    • Build, evaluate, and deploy a mini AI project end-to-end.
      • Project Selection: Tabular, CV, or NLP
      • Data Collection/Exploration
      • Preprocessing + Feature Engineering
      • Model Training & Tuning
      • Evaluation & Interpretation
      • App Creation (Streamlit/Gradio)
      • Deployment + Final Presentation/Submission
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FAQ's

Basic knowledge of mathematics and programming (Python or R) is helpful, but not mandatory.

The course duration can range from a few months (certifications) to 1-2 years (degree programs).

Yes, most courses include practical projects and real-world data sets to work with.

Many programs offer job placement support, internships, and career counseling to help you land a job.

Requirements typically include a bachelor’s degree and a basic understanding of math or programming.

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