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.
- 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
- 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.
|3/06||Course introduction||Homework 0 (Due: 3/11)|
|3/13||Tools for pragmatic data mining||-|
|3/20||Scraping from the Web||Project proposals (300 words+)|
|3/27||Text mining 1: Text exploration||-|
|4/03||Text mining 2: Topic modeling||-|
|4/10||Regression and Predictive modeling||-|
|4/24||Clustering and Dimensionality Reduction||-|
|5/01||Visualization and storytelling 1||-|
|5/08||Visualization and storytelling 2||Progress report (1K+ words)|
|5/15||Going deep: Deep learning||-|
|5/22||Going big 1: Map/reduce||-|
|5/29||Going big 2: Spark||-|
|6/05||Final presentation||Project final report (3K+ words)|