3A. Preparing Data - Exploratory Analysis & Data Wrangling

Описание: This video is Part 3A of the series "Machine Learning Essentials for Biomedical Data Science" covering the key essentials for using machine learning as part of a data science analysis pipeline. While topics are primarily framed around applications in biomedicine, this content is broadly applicable to other domains.

The material presented was assembled based on my 15 years experience as a machine learning researcher and educator. I'm currently an Assistant Professor (Pending) of Computational Biomedicine at the Cedars Sinai Medical Center. Some of the slide content is original, with much adapted from a wide variety of textbooks, slides, and lectures by various authors and speakers. This video series expands upon a full-day workshop prepared for and presented at the Cedars Sinai Medical Center in Los Angeles. This video represents my own understanding and perspectives.

Weblinks:
http://ryanurbanowicz.com/
https://github.com/UrbsLab
https://github.com/UrbsLab/STREAMLINE

Chapters:
0:00 Introduction
1:10 Define Problem
2:21 Data Collection
3:27 Bad Data (ML Pitfall)
5:05 How Many Instances Needed?
7:55 Data Preparation
8:35 Exploratory Data Analysis
10:31 Class Imbalance (ML Pitfall)
11:50 Data Wrangling
12:35 Feature Extraction
15:15 Data Formatting
17:26 Data Cleaning
18:38 Data Leakage (ML Pitfall)
20:00 Cleaning Outliers
22:15 Cleaning Missing Values
23:59 Imputation of Missing Values
25:06 Data Integration
27:00 Conclusion