CS 0155 -- Data Witchcraft (Spring 2017)

Any sufficiently advanced technology is indistinguishable from magic
[Arthur C. Clark]

This quote comes to mind when considering the availability of data nowadays and the advances in data technologies over just the last 10 years!

Why you should take this course?
Computer literacy in general and in data science/data management in particular are becoming required skills for all career paths, especially in business and in the sciences. This course will give you the fundamental skills for a solid start on a data-driven path in your specific discipline.


NEW: Spring 2017 information

For the Spring 2017, the course will go through three transformations:

  • it will be offered as a first course in computing, i.e., it will have NO prerequisites and will also cover programming (Python)
  • it will fulfill the Quantitative Reasoning General Education requirement for Arts and Sciences
  • it will be offered in a room that facilitates group work during class


Instructor:

  • Prof. Alex Labrinidis
  • Syllabus:

  • Tentative Syllabus in pdf
  • Tentative Topics:

    • Introduction to Data Science and Big Data
    • Introduction to Python Programming (Control flow, Variables, Basic data structures)
    • Python Programming (User-Defined Functions)
    • Python Programming (File Input/Output)
    • Python Programming (Parsing Popular data exchange formats: CSV, JSON, RSS)
    • Introduction to Data Mining (Clustering, Association Rule Mining)
    • Intro to Data Visualization (Powerpoint, Tableau, Google Fusion Tables)
    • Data Visualization using Python
    • Introduction to Recommendation Systems
    • Introduction to Data Warehousing and (Statistical) Data Summarization
    • Introduction to Relational Databases
    • Introduction to SQL (Select, Project, Join queries)
    • SQL Programming using Python
    • Advanced Topics (Graph Databases, REST APIs)
    • Term Project Presentations

    Format:
    Exams, hands-on projects, quizzes, lab activities. There will also be at least one assignment that requires you to present your findings in class.

    Prerequisites:
    None, although some programming and some statistics knowledge/background are encouraged.

    Course Links: