Welcome to Business Analytics class of 2015! Here you'll find all course materials, guides and schedules. In case you have questions, feel free to send an email to [email protected] (or directly write a new topic at the forum below).

Data mining and analytical skills are at the heart of solving many important problems in our world. In this course, we aim to derive technology-based solutions to such problems, and develop strategical decision making abilities based on data.

Specifically, we discuss technologies, applications, practices, and skills for continuous iterative exploration and investigation of business performance with external data collected from diverse sources such as the Web, in order to gain insights and drive business planning. Topics include statistical and quantitative analysis, explanatory and predictive modeling, as well as text analytics with visualization. Students are required to present progress on their work during the semester, and are assessed by a set of assignments, quizzes and exams.

Note that this course is the second part of a two part sequence. We assume you have already taken Data Mining (ex: IISE113503), the first part of the sequence, where methods and algorithms for mining data were discussed. This course is the latter part of the sequence, and will be more advanced and project-focused.

Prerequisites

  • Familiarity in data mining algorithms
  • Good knowledge with at least one programming language (ex: R, Python, Java, etc.)

What you will learn

  • Use algorithms to extract meaningful insight from large datasets
  • Understand the usage of data mining in a domain of interest
  • Develop analytical and data-based thinking

Grading

  • Assignments (20%): You will be given four graded assignments during the semester.
  • Mid-term Quiz (20%): A mid-term quiz.
  • Final Exam (60%): A final exam and presentation.

Schedule

datelectureassignment
3/06Course introductionHomework 0 (Due: 3/11)
  • CV + Self-intro
3/13Tools for pragmatic data mining
  • Bash
  • Python
  • Statistical Summaries and Exploratory Data Analysis
-
3/20Scraping from the Web
  • Proposal presentations
Project proposals (300 words+)
3/27Text mining 1: Text exploration-
4/03Text mining 2: Topic modeling
  • Share reading assignment
-
4/10Regression and Predictive modeling
  • Recommender systems
  • Share project progress
-
4/17Mid-term Quiz-
4/24Clustering and Dimensionality Reduction
  • Singular value decomposition (SVD)
  • Share reading assignment
-
5/01Visualization and storytelling 1
  • Share reading assignment
-
5/08Visualization and storytelling 2
  • Share reading assignment
Progress report (1K+ words)
5/15Going deep: Deep learning
  • Share reading assignment
-
5/22Going big 1: Map/reduce
  • Share reading assignment
-
5/29Going big 2: Spark
  • Share reading assignment
-
6/05Final presentationProject final report (3K+ words)
6/12Final exam

Forum