This data as .json
| rowid | event_id | name | clean_description | location_room | start_date_clean | start_time | end_time | pre_reg_flag | paid_flag | laptop_flag | speakers_cleaned | session_type | keywords | skill_level | session_title |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 203 | 4231 | Regular expressions for the rest of us | Regular expressions are a powerful way to slice, dice and clean up dirty data — a must-know skill for anyone who works with data. They may look intimidating, but they are really just pattern-matching puzzles. We'll learn the fundamentals of "regex" so you save time cleaning your next batch of data. This session is good for: People who have ever done more than two search/replace actions to clean a data set or had to split a ZIP code from an address or otherwise want to conquer their fears of regex. | 4 | 2019-03-07 | 2019-03-07 09:00:00 | 2019-03-07 10:00:00 | 0 | 0 | 0 | Christian McDonald, University of Texas | 3 | 2 | Regular expressions for the rest of us |