About course

Data science plays an important role in many industries. In facing massive amount of heterogeneous data, scalable machine learning and data mining algorithms and systems become extremely important for data scientists. The growth of volume, complexity and speed in data drives the need for scalable data analytic algorithms and systems. In this course, we study such algorithms and systems in the context of healthcare applications.

In healthcare, large amounts of heterogeneous medical data have become available in various healthcare organizations (payers, providers, pharmaceuticals). This data could be an enabling resource for deriving insights for improving care delivery and reducing waste. The enormity and complexity of these datasets present great challenges in analyses and subsequent applications to a practical clinical environment. In this course, we introduce the characteristics of medical data and associated data mining challenges on dealing with such data. We cover various algorithms and systems for big data analytics. We focus on studying those big data techniques in the context of concrete healthcare analytic applications such as predictive modeling, computational phenotyping and patient similarity. We also study big data analytic technology:

1. scalable machine learning algorithms such as online learning and fast similarity search;
2. big data analytic system such as Hadoop family (Hive, Pig, HBase), Spark and Graph DB

To be successful in the course, students should have acquired:

1. Basic machine learning and data mining concepts such as classification and clustering;
2. Proficient programming and system skills in python, java and scala;
3. Proficient knowledge and experience in dealing with data (recommended skills include SQL, NoSQL such as MongoDB).

Grading scheme

• 40% Homework 4 homework 10% each

• 45% Project

  • 10% proposal
    
  • 10% paper draft
    
  • 10% final presentation
    
  • 15% final paper
    

• 6% Peer feedback

  • 2% draft paper
    
  • 2% final presentation
    
  • 2% final paper
    

• 9% Participation

  • 9% piazza activities for OMS
  • 5% piazza activities for on-campus ones
  • 4% in-class quiz & attendence for on-campus ones

Formal prerequisites for CX4803

Minimum grade of C for MATH 3215 or MATH 3225 or ECE 3077 or ISYE 2027. Two of the following:

• CX 4240. Introduction to Computing for Data Analysis
• CS 4400 - Introduction to Database Systems
• CX 4242. Data and Visual Analytics

Formal prerequisites for CX8803

None as long as you have learned the background covered in the prerequisites for CX4803.

Textbooks and reading materials

None required but papers and book chapters will be listed under lectures.