- Big Data Analytics (Bank of Sulselbar)
- Ruhimat Game of Trust
- Data-Driven-Banking (Bank of DKI)
- IT Risk Management
- System Analyst
- Data-Driven-Banking
- Data Driven Decision
- Data Visualization
- Data Storytelling
- Knime Academy Data Science
- Gertaone Trading School-Preschool
- Pinbar Trading Strategy
- Price Action Trading Strategy
- Price Action Trading Intro
- Dow Theory Trading
- Knime Academy 101
- Data-Driven Impact
- Elliot Wave Trading
- Chart Pattern Trading
- Candlestick Price Action Trading
- Support & Resistance Trading
- Fundamental-News Trading
- Fundamental Analysis of Trading
- BI & Visual Analytics-Tableau Overview
- Use Case Business Intelligence
- Knime Data Science & ML
- Knime Text Mining
- Knime Advanced Training
- Knime Beginner Training
- BI & Visual Analytics-Tableau
- Intro to Web Development
- Applied Statistics
- Tableau Desktop Specialist
- Hands-On Tableau Workshop
- Tableau Public
- DWBI
- Operations Research 101
- Machine learning 101
- DataViz in R
- Regression 101 in R
- HOPR
- Data cleaning 101 in R
- Zeitreihenanalyse-DE
- Open Refine 101
- Colorology in R
- Default R Graphs
- Python for Data Science
- Data Science-HR
- EDA with R
- People Analytics
- Cevipedia
- R-Shiny
- Github
- BI-Tableau
- BI-Power BI
- Codepedia
- Power Pivot-DAX
- Power Query
- Certification Wiki
- KPI-Dashboard
- Insta-DE
- WKR-DE
- Desta-DE
- R Programming
Data cleaning 101 in R
Checklist for Data Cleansing
The 80/20 data science dilemma:
data scientists spend 80% of their time cleaning and manipulating data and only 20% of their time actually analyzing it.” (forbes.com)
Click here: Data cleaning 101 in R
This Notebook using iterative development. It means the process starts with a simple implementation of a small set of idea requirements and iteratively enhances the evolving versions until the complete version is implemented and perfect. Find the file and others stuff in my github account.
January 2019