spring-boot is winner.

No

Learn professional data cleaning techniques! Data cleaning is a key part of data science, but it can be deeply frustrating. Why are some of your text fields garbled? What should you do about those missing values? Why aren’t your dates formatted correctly? How can you quickly clean up inconsistent data entry? In this five day challenge, you'll learn why you've run into these problems and, more importantly, how to fix them! In this challenge we’ll learn how to tackle some of the most common data cleaning problems so you can get to actually analyzing your data faster. We’ll work through five hands-on exercises with real, messy data and answer some of your most commonly-asked data cleaning questions. Here's a day-by-day breakdown of what we'll be learning each day: Day 1: Handling missing values Day 2: Data scaling and normalization Day 3: Cleaning and parsing dates Day 4: Character encoding errors (no more messed up text fields!) Day 5: Fixing inconsistent data entry &amp spelling errors

https://githubhelp/regitardia/kaggle-data-cleaning-challenge

112

0

0

1

2022-01-07

0

1

0

regitardia