This is a mandatory course for all MSc students in Information Systems Engineering. It is designed to cover the main steps of a typical graduate-level research cycle in information and data sciences (e.g., Ideas, Literature Survey, Proposal Preparation, Research Process, Publication, and Presentation).
Every record in the DB represents a post on the Facebook page of a company.
The research goal is to realize what are the factors (i.e., columns value) to popular posts.
| Columns Name | Description |
|---|---|
| Category | “1” – “Promotion” ; “2” – “Product”; 3 – “General” |
| PostMonth | Month ID |
| PostWeekday | Day ID |
| PostHour | Hour ID |
| Paid | Whether the company paid Facebook to prompt the post |
| LifetimePostTotalReach | The number of people who saw a page post (unique users). |
| LifetimePostTotalImpressions | The total number of times the post was viewed. |
| LifetimeEngagedUsers | The number of people who clicked the post (unique users). |
| LifetimePostConsumers | The total number of clicks on the post |
| comment | The number of comments to the post |
| like | The number of likes to the post |
| share | The number of shares to the post |
| TotalInteractions | The total interactions (comment + like + share) |
[1] Moro, S., Rita, P., & Vala, B. (2016). Predicting social media performance metrics and evaluation of the impact on brand building: A data mining approach. Journal of Business Research, 69(9), 3341-3351.
Histogram:
Box plot:
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent