Montreal College of Information Technology
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Applied Data Science with Python

Advanced skills in Python is crucial for many data science roles. In this course, you will continue to build on the Python programming skills you acquired in the previous class by implementing machine learning using python libraries like TensorFlow, Py torch scikit-learn You will learn all the advanced Python libraries which are being used in the real world by data scientists. Data Science with Python training help you advance your career as a data scientist.  Through a combination of theoretical concepts, real-world projects, and interactive exercises, participants will learn how to collect, clean, analyze, and visualize data and effectively communicate their findings. 

Schedule: Monday, Wednesday, Friday - 6pm - 9pm


  • Applied Data Science with Python

    Get trained by industry Experts

    Our courses are delivered by professionals with years of experience having learned first-hand the best, in-demand techniques, concepts, and latest tools.
  • Applied Data Science with Python

    Official Certification curriculum

    Our curriculum is kept up to date with the latest official Certification syllabus and making you getting ready to take the exam.
  • Applied Data Science with Python

    Tax Credit

    Claim up to 25% of tuition fees and education tax credit from your taxes.
  • Applied Data Science with Python

    Discount on Certification Voucher

    Upto 50 percent discount voucher will be provided.
  • Applied Data Science with Python

    24/7 Lab access

    Our students have access to their labs and course materials at any hour of the day to maximize their learning potential and guarantee success.

This module introduces Scikit-Learn, an essential machine learning library in Python. It covers its core features, including supervised and unsupervised learning algorithms, model training, and evaluation. Students explore Scikit-Learn's functionalities for classification, regression, clustering, and model evaluation, establishing a foundational understanding of machine learning with this powerful library.

This module delves into practical machine learning applications using Scikit-Learn in Python. It covers supervised and unsupervised learning, including classification, regression, and clustering algorithms. Students explore model training, evaluation, and hyperparameter tuning, gaining practical insights into implementing machine learning techniques effectively with Scikit-Learn.

This module introduces TensorFlow, a popular open-source machine learning library. It covers its foundational aspects, including tensors, computation graphs, and building neural networks. Students explore TensorFlow's capabilities for creating, training, and deploying machine learning models, establishing a foundational understanding of deep learning using TensorFlow.

This module delves into the fundamentals of deep learning using TensorFlow. It covers neural network architecture, optimization, and model training techniques. Students explore TensorFlow's capabilities for building and fine-tuning deep neural networks, gaining foundational insights into implementing advanced machine learning models with TensorFlow.

This module focuses on Convolutional Neural Networks (CNNs) using TensorFlow. It covers CNN architecture, layers, and their applications in computer vision tasks. Students explore TensorFlow's CNN functionalities for image recognition, object detection, and feature extraction, gaining practical insights into implementing CNNs for various visual tasks.

Recurrent Neural Networks (RNNs) are the focus of this module, exploring their architecture and applications in sequential data analysis. Students delve into RNN fundamentals, understanding their ability to process and predict sequential data, such as text or time series. This module covers RNN variations, training techniques, and their diverse applications in natural language processing, speech recognition, and sequential data modeling.

This module introduces PyTorch, a popular deep learning framework, focusing on its fundamental components like tensors and computational graphs. Students explore PyTorch's tensor operations, computational graph creation, and manipulation, establishing a foundational understanding of PyTorch for building neural networks.

This module delves into practical deep learning applications using PyTorch. It covers building neural networks, training models, and implementing deep learning techniques. Students explore PyTorch's capabilities for creating, training, and deploying sophisticated deep learning architectures, gaining hands-on experience in implementing advanced machine learning models with PyTorch.

In this module, advanced PyTorch techniques and applications are explored. It covers topics such as customizing neural network architectures, transfer learning, fine-tuning models, and leveraging PyTorch's advanced functionalities. Students delve into complex applications, model optimization, and deploying PyTorch models for various tasks, enhancing their expertise in advanced deep learning techniques and practical applications with PyTorch.



The applied data science with python certification course is ideal for anyone who wants to pursue a career in data science, machine learning, or artificial intelligence. It is suitable for those with background in mathematics and programming, including software developers, data analysts, business analysts, and IT professionals.
It is relevant for professionals in other fields who want to transition into data science or machine learning roles. Ultimately, anyone who is interested in learning how to develop and deploy machine learning models can benefit from this certification course.
Aimed at professionals who deal with large amounts of Data in their jobs on a daily basis to help organizations understand trends and take critical decisions.
Academic achievers who are just out of universities. This program will help add competencies to their portfolio.

Eligibility and Requirements

Learners need to possess an undergraduate degree or a high school diploma. No need of any professional experience is required as this is the fundamental course.



Statistics, Machine Learning and Python Programming are prerequisites for this certification course.

Applied Data Science with Python certification.


Upon completing this certification course you will:

  • Receive an industry-recognized certificate from MCIT.
  • Be prepared for any real time certification related to data science python libraries.



— F.A.Q —

All of our exceptionally skilled instructors have a decent experience of training and industry experience and are AW certified in the respective field. Each of them through a rigorous selection procedure that included profile screening, technical examination, and a training demo. 
Yes, there're Teaching Assistants (TAs) available for this program to help you during your labs and ease your learning process.
Yes, there are vouchers to take the official exam.
Definitely. Please feel free to contact our office, we will be more than happy to work with you to meet your training needs.
Upon completion of the certification course classes you will be provided with an MCIT certificate.