Innovation

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  1. EFFECTIVE COMMUNICATION FOR LEADERSHIP CM4-001-I-M

    The tutorial takes the student through the aspects of oral communication which research and observation show will make the speaker be perceived as more charismatic. It covers the least frequently taught side of charismatic communication: the content of the speech. The first part shows how to frame the message, through the depiction of values, the future, the individuals as capable of achieving the vision, the power of the group to succeed, and the leader himself as the standard to follow. The second part teaches which rhetorical recourses are most effective in conveying the message. The third part studies the attributes and behaviors most frequently observed in charismatic leaders. The fourth part shows the uses of language that will reduce the perceived power of the speaker. 

    Academic Area:
    Organisational Behaviour | Human Resources | Marketing & Communications | Others
  2. Machine Learning SI2-108-I-M

    This multimedia is in a template form with the goal of giving students a background on machine learning, its types, and examples of what it’s used for. It is a simple and straight-forward material that involves text, video, GIF and interactive tools (quizzes, etc.)

    The breakdown of sections is as follows:

    1) Home

    2) What is Machine Learning

    3) Why Now?

    4) Infrastructure Needed

    5) Types of Machine Learning

    6) Test Your ML IQ

    The tutorial is meant for students to do before they come into class, to give them a taste of the area of machine learning, without going too much in-depth (that will be the professor’s role).

    Academic Area:
    Strategy | Entrepreneurship | Digital Technologies & Data Science | Innovation
  3. Machine Learning SI2-108-M

    This multimedia is in a template form with the goal of giving students a background on machine learning, its types, and examples of what it’s used for. It is a simple and straight-forward material that involves text, video, GIF and interactive tools (quizzes, etc.)

    The breakdown of sections is as follows:

    1) Home

    2) What is Machine Learning

    3) Why Now?

    4) Infrastructure Needed

    5) Types of Machine Learning

    6) Test Your ML IQ

    The tutorial is meant for students to do before they come into class, to give them a taste of the area of machine learning, without going too much in-depth (that will be the professor’s role).

    Academic Area:
    Strategy | Entrepreneurship | Digital Technologies & Data Science | Innovation
  4. Math Pre-program, videos EC1-130-I-M

    This material serves as an introduction to basic mathematical concepts: basic rules about numbers and their operations equations and other topics such as notation and logic.

    This course is intended as general reference material for introductory courses.

    Academic Area:
    Others
  5. Principales conceptos sobre métodos cuantitativos DO2-129-M

    This is a series of videos that help students to understand the main concepts covered in the Quantitative Methods course. These videos are enhanced through the use of presentations and diagrams on an interactive whiteboard in order to simplify the understanding of the topics. The topics covered are the following:

    - Introduction to Probability
    - Conditional Probability and Bayes’ Theorem
    - Random Variables
    - The Normal Distribution
    - Sampling & Confidence Intervals
    - Hypothesis Testing

    Academic Area:
    Operations & Supply Chain Management
  6. Main Concepts about Quantitative Methods DO2-129-I-M

    This is a series of videos that help students to understand the main concepts covered in the Quantitative Methods course. These videos are enhanced through the use of presentations and diagrams on an interactive whiteboard in order to simplify the understanding of the topics. The topics covered are the following:

    - Introduction to Probability
    - Conditional Probability and Bayes’ Theorem
    - Random Variables
    - The Normal Distribution
    - Sampling & Confidence Intervals
    - Hypothesis Testing

    Academic Area:
    Operations & Supply Chain Management
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