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Program Requirements

Ce programme et ses exigences sont disponibles sur la plateforme Athena de l'École d’éducation permanente de ²»Á¼Ñо¿Ëù. Veuillez consulter le site pour plus de détails.

Cours obligatoires (26 UEC)

• CBUS 255 Computational Applied Statistics (4 unités d'EP)

  Offered by: Développement de carrière et perfectionnement professionnel (École d'éducation permanente)

  Vue d'ensemble

  Affaires : This course provides a comprehensive practical introduction to fundamental statistical concepts of common distributions, statistical methods, and data analysis using Python and other software packages. Develop the ability to apply appropriate statistical methods to summarize and analyze data; report and interpret results. Focus is placed on data description, descriptive statistics, probability, random variables, binomial, poisson, normal distributions, sampling distribution of the mean, estimation, hypothesis testing, analysis of variance, tests of goodness of fit, regression, non-parametric statistics.

  Terms: This course is not scheduled for the 2018-2019 academic year.

  Instructors: There are no professors associated with this course for the 2018-2019 academic year.

  • Prerequisite: Strong quantitative background Proficiency in Excel, Access or other data analysis tools Essential programming skills, preferably in Python.

  • 1. 30 hours class plus at least 10 hours of assignments/readings.

• CBUS 256 Data Science for Business Decisions (4 unités d'EP)

  Offered by: Développement de carrière et perfectionnement professionnel (École d'éducation permanente)

  Vue d'ensemble

  Affaires : This course aims to provide an overview of how data science can help drive business decisions and create new business models. The emphasis is placed on data strategy and how to move from data to insight. The course explores the data science process and how companies could surmount the different challenges they face when implementing a data driven business including ethics, data governance and privacy. The evolution of data technology and storage, as well as application of data science tools and techniques to different business areas such as customer and web analytics, operations analytics, human resources related analytics are explored through examples from various fields such as retail, healthcare and marketing.

  Terms: This course is not scheduled for the 2018-2019 academic year.

  Instructors: There are no professors associated with this course for the 2018-2019 academic year.

  • Prerequisite: CBUS 255

  • 1. 30 hours in class plus at least 10 hours of assignments/readings.

• CBUS 257 Data at Scale (6 unités d'EP)

  Offered by: Développement de carrière et perfectionnement professionnel (École d'éducation permanente)

  Vue d'ensemble

  Affaires : This course familiarizes participants with different aspects of large data sets and how they are managed both on site and in the Cloud. Emphasis is placed on providing participants with hands-on experience from data ingestion to analysis of large data sets, both data-at-rest or data-in-motion (streaming data), including defining Big Data and its 5 V's: Volume, Velocity, Variety, Veracity, and Value. Architectures of distributed databases and storage, ecosystems such as Hadoop and Spark are covered followed by introduction to Scala, Spark-Shell and PySpark.

  Terms: This course is not scheduled for the 2018-2019 academic year.

  Instructors: There are no professors associated with this course for the 2018-2019 academic year.

  • Prerequisite: CBUS 255

  • 1. 35 hours in class plus at least 25 hours of assignments/readings.

• CBUS 258 Practical Machine Learning (6 unités d'EP)

  Offered by: Développement de carrière et perfectionnement professionnel (École d'éducation permanente)

  Vue d'ensemble

  Affaires : This course aims to introduce participants to essential machine learning methods and techniques through an end-to-end machine learning project. Emphasis is placed on practical experience with machine learning using Python programming language, scikit-learn and TensorFlow, as well as on understanding classification and training models. The course will provide an introduction to artificial Neural Networks, deep learning, convolutional and recurrent neural nets and reinforcement learning.

  Terms: This course is not scheduled for the 2018-2019 academic year.

  Instructors: There are no professors associated with this course for the 2018-2019 academic year.

  • Prerequisite(s): CBUS 255 Plus intermediate level programming skills in Python.

  • 1. 35 hours in class plus at least 25 hours of assignments.

• CBUS 299 Data Science Capstone Project (6 unités d'EP)

  Offered by: Développement de carrière et perfectionnement professionnel (École d'éducation permanente)

  Vue d'ensemble

  Affaires : This capstone course supported by our industry partners will provide the opportunity to apply all the knowledge gained during the program in order to build a full data science pipeline from preparing and visualizing data, building and testing models, analyzing results and deriving business insights from their analysis. The focus is placed on communicating the insights gleaned from the data analysis through visualizations and on presenting the recommendations reached.

  Terms: This course is not scheduled for the 2018-2019 academic year.

  Instructors: There are no professors associated with this course for the 2018-2019 academic year.

  • Prerequisite(s): CBUS 255, CBUS 256, CBUS 257 and CBUS 258 or CBUS 260, CBUS 261 and CBUS 262.

  • 1. 30 hours in class plus at least 30 hours of assignments