Social Media Analytics
| Semester: |
Spring |
| Pre-requisites: |
None |
| Syllabus |
PDF |
This course is split into five main parts:
- Network Patterns, which describes and seeks to explain several common patterns found in real-world social networks,
- Branding and Community, which explores the best methods for
maintaining a strong brand online and managing the user community,
- Importance and Influence, which discusses an individual's place
in the network, and how memes, early adoption, and such cascades propagate,
- Advertising and Marketing, which focuses on viral marketing and social advertising techniques, and
- Advanced Analytics, which describes the latest methods for
inferring user interests and recommending items to them, and related topics.
Books:
- Networks, Crowds, and Markets, by Easley and Kleinberg
- Networks: An Introduction, by Newman
Case studies will be available in an online coursepack.
Slides will be made available on Canvas.
| 1 Group assignment |
10% |
| 1 Group assignment with presentation |
20% |
| Group project with presentation |
20% |
| Midterm |
20% |
| Final exam |
30% |
Advanced Analytics Programming
| Semester: |
Spring |
| Pre-requisites: |
MIS 304 (Intro to Programming) |
| Syllabus |
PDF |
This course has five main parts:
- Introductory Python,
where we learn the basic language syntax, and gain familiarity with
general-purpose tools such as string manipulation,
- Pandas,
which is a powerful data analysis toolkit (similar to R) that makes it
easy to explore and visualize data,
- Classification,
where we develop an understanding of how to make predictions,
- Clustering,
where we learn how to discover the major groups or components of a
given dataset, and
- Other Topics,
including regression.
Books (all optional):
- Think Python, by Downey (see
here)
- Python for Data Analysis, by McKinney
- Applied Predictive Modeling, by Kuhn and Johnson
Slides will be made available on Canvas.
| 3 Group assignment |
30% |
| Group project with presentation |
20% |
| Midterm |
20% |
| Final exam |
30% |
Data Analytics Programming
| Semester: |
Summer |
| Pre-requisites: |
Basic Python programming |
| Syllabus |
PDF |
This course is split into five main parts:
- Introductory Python,
where we learn the basic language syntax, and gain familiarity with
general-purpose tools such as string manipulation,
- Pandas,
which is a powerful data analysis toolkit (similar to R) that makes it
easy to explore and visualize data,
- Classification,
where we develop an understanding of how to make predictions,
- Clustering,
where we learn how to discover the major groups or components of a
given dataset, and
- Other Topics,
including regression and hypothesis testing.
Books:
- Think Python, by Downey (see
here)
- Python for Data Analysis, by McKinney
Slides will be made available on Canvas.
| 3 Group assignments |
45% |
| Group project with presentation |
25% |
| Final exam |
30% |