Official Description

4.5 Continuing Education Units (CEUs)

Types of data analytics. Connecting business problems to analytics techniques and methods. The analytics process: identification, extraction, cleaning, transformation, analysis, and interpretation. Producing and improving data science and machine learning models and assessing their performance. Data analysis and modelling using Python.


Supplementary Information

30 contact hours plus approximately 15 hours of assignments

Topics Covered

  • Statistical analysis 
  • Mathematical models and basic linear algebra 
  • Regression, classification, and clustering 
  • Linear regression models 
  • Logistic regression models 
  • Model performance evaluation 
  • Clustering techniques 
  • Decision tree models 

Learning Outcomes

  • Select appropriate statistical and machine learning techniques to solve various business problems  
  • Apply and interpret fundamental concepts of statistics and probability to analyze data and deliver insights 
  • Execute essential data preparation and data cleaning techniques 
  • Produce and improve regression and classification models using Python 
  • Evaluate machine learning models using different performance and accuracy metrics 

Prerequisite(s) and Corequisite(s)

Introductory Data Science for Business Decisions (YCBS 274)

Basic programming skills in Python. Students without any knowledge of Python should complete the following (or similar) online course: Introduction to Python.


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