MGT-492 Data science and machine learning I

Today, we have access to an unprecedented amount of data. Historical data can be combined with machine learning techniques to create very accurate predictive models. Both data and machine learning are the main driving forces of the tech-celeration that we have witnessed in the recent years.

This class will provide a hands-on introduction to data science and machine learning topics, exploring areas such as:

  • Data acquisition and data cleaning
  • Data visualization
  • Regression (predicting numerical values) 
  • Classification (predicting categorical values)
  • Dimensionality Reduction 
  • Clustering 
  • Neural networks 
  • Text Analytics

The students will be introduced to the Wolfram Language which elegantly meshes code, curated data, interactive interfaces and natural language recognition into a single platform. The Wolfram language is very terse and has built-in functions and data for all of the topics that we will cover. This will allow the students to focus on the concepts rather than on learning a new computing language. During the class we will also learn how to deploy our code and projects on the cloud.


Evaluation

Your grade will be based on work that you do during the semester and depends on the following components:

  • In-class quizzes (30%): Multiple choice conducted in-class that test your understanding of the topics, not how well you memorize.
  • Personal assignments (35%): Coding assignments (30%) + Book self-reflection Essay (5%).
  • One group project (35%): 3 students per group. The project will deal with solving a data-science  problem using the techniques we have learned. You will also have to create a video (up to 15mins), upload it in youtube and share it in our slack channel.


Books:

The following are good books on data mining which you can use, but you are not required to purchase them to follow the class:

Non-technical Books (you will be asked to write a self-reflection essay.)