Data Analytics 2014


Instructor: Professor Peter Fox
TA: Lakshmi Chenicheri chenil at rpi dot edu
Meeting times: TF 12-1:50
Office Hours:Winslow 2120 or by appointment in Lally 207A
phone: x4862
TA Office Hours: TBD
Class Listing: ITWS 4963/ITWS 6965
Class Location: SAGE 3101

Syllabus/ Calendar

Refer to Reading/ Assignment/ Reference list for each week (see below).

Reading/ Assignment/ Reference List

Class 1: Reading Assignment:

Assignment 1 [Download]

Class 2 Reading Assignment: no reading

Class 3 Reading Assignment:

(Lab) Assignment 2 [Download]

Class 4 Reading Assignment: none

Assignment 3 [Download]

Class 5 Reading Assignment: none

Assignment 4 [Download]

Class 6 Reading Assignment: none

Assignment 5 [Download]

Class 7 Reading Assignment:

Assignment 6 [Download]

Class 9 Reading Assignment: none

Class 10 Reading Assignment:

Class 11 Reading Assignment: none

Class 12 Reading Assignment:

Assignment 7 [Download]

Class 13 Reading Assignment: none

Class 14 Reading Assignment: none

Reference material (available through RPI library - RCS login required):

Course Description:

Data and Information analytics extends analysis (descriptive and predictive models to obtain knowledge from data) by using insight from analyses to recommend action or to guide and communicate decision-making. Thus, analytics is not so much concerned with individual analyses or analysis steps, but with an entire methodology. The world at-large is confronted with increasingly larger and complex sets of structured/unstructured information; from sensors, instruments, and generated by computer simulations; data is "hidden" in websites, application servers, social networks and on mobile devices. As a nation, assimilating information across disparate domains (e.g., intelligence, economics, science) has the potential to provide improved capabilities for decision makers. In commerce and industry, analytics-driven enterprises are becoming mainstream. Yet, there is a shortfall in the key education skills needed to meet the growing needs. Traditional enterprises are moving toward analytics-driven approaches for core business functions. In the government and corporations, cybersecurity problems are prevalent. The investment in advanced analytics capabilities could potentially be more broadly leveraged today and greater than any prior government investments in computing. Emphasis is now placed on disruptive data and information sources on the Web and Internet: using Web Science and informatics to explore social networks, platform competition, the "long tail" and economic or resource impacts of the search for new findings. Key topics include: advanced statistical computing theory, multivariate analysis, and application of computer science courses such as data mining and machine learning and change detection by uncovering unexpected patterns in data.

Course goals:

• Introduce students to relevant methods to recognize and apply quantitative algorithms, techniques and interpretation
• To develop students' strategic thinking skills, combined with a solid technical foundation in data and model-driven decision-making.
• Develop ability to apply critical and analytical methods to formulate and solve science, engineering, medical, and business problems
• Students will examine real-world examples using modern cyberinfrastructure to place statistical and data-mining techniques in context, to develop data-analytic thinking, and to illustrate that proper application is as much an art as it is a science.
• By the end of the course, students can effectively communicate analytic findings to non-specialists

Course Learning Objectives:

  • Students to demonstrate knowledge of relevant analytic methods, and to recognize and apply quantitative algorithms, techniques and interpret results
  • Students to demonstrate strategic thinking skills, combined with a solid technical foundation in data and model-driven decision-making.
  • Students to develop ability to apply critical and analytical methods to formulate and solve science, engineering, medical, and business problems
  • Students will examine real-world examples to place data-mining techniques in context, to develop data-analytic thinking, and to illustrate that proper application is as much an art as it is a science.
  • Students must effectively communicate analytic findings to non-specialists.
  • [graduate level]
    Students must develop and demonstrate a working knowledge of decision making under uncertainty, be able to build optimization models that incorporate random parameters: static stochastic optimization, two-stage optimization with recourse, chance-constrained optimization, and sequential decision making.