hfsl_course

BIA660 Web Analytics
Business Intelligence & Analytics
Section A: T 06:15-08:45PM, Section B: W 03:15-05:45PM, Section C: R 06:15-08:45PM
Section A and B: BC 321, Section C: McLean218B
  • Fall On Campus
  • Spring On Campus
Students will learn through hands-on experience how to extract data from the web and analyze web-scale data using distributed computing.

Students will learn different analysis methods that are widely used across the range of internet companies, from start-ups to online giants like Amazon or Google. At the end of the course, students will apply these methods to answer a real scientific question.

 

Prerequisites: Students must have programming experience. It is also highly recommended for the students to have taken Multivariate Data Analytics (BIA 652), Data & Knowledge Management (MIS 630), and Knowledge Discovery in Databases (MIS 637).

Additional learning objectives include the development of:

Data collection and preprocessing skills: students will learn how to identify and profile candidate sources of valuable data, as well as how to automatically collect and manage the information they need for their analytics tasks.

Diverse Analytic Skills: students will be exposed to a wide range of both quantitative and qualitative analytics techniques with applications across multiple business domains.

Team Skills: the students will be organized in teams and collaborate on projects for the duration of the course. Each student will evaluate his/her teammates twice during the semester via a customized team survey tool. The tool provides a detailed analysis of a person’s contributions to the different stages of the team’s operation and will be used to promptly identify and address possible problems.

 

 

Evaluation Grade Percent
Class work   30%
Peer Evaluation  10%
Mid-Term Project 20%
Final Project 40%
Total 100%

 

Mid-term project

Collect, clean and organize online data from 2 different websites of your choice. The deliverable includes 2 datasets, the collection & cleaning scripts, and a presentation to be given in class.

Final project

Choose an important research question that emerges in the context of one of the two datasets collected for the midterm project. Develop, apply and record an analytics methodology to address your question. This work will be presented in class.

Syllabus

Lecture Description
Week 1

Introduction to the course

 

Introduction to Python I (basic concepts)

Week 2

Introduction to Python II (parsing & using libraries)

Week 3

Using Python to scrape the web I (regex & other libraries)

Week 4

Using Python to scrape the web II (data cleaning)

Week 5

Text Mining with Python (nltk)

Week 6

Midterm Project Presentations

Week 7

Sentiment Analysis with Python

Week 8

Social Network & Graph Mining with Python (networkx)

Week 9

Machine Learning & Analytics with Python I (sklearn)

Week 10

Machine Learning & Analytics with Python II (sklearn)

Week 11

Machine Learning & Analytics with Python III (sklearn)

Week 12

Visualization (matplotlib & other tools)

Week 13

Work on Final projects

Week 14

Final Project Presentations