data wrangling : It is often the case with data science projects that you’ll have to deal with messy or incomplete data. The raw data we obtain from different data sources is often unusable at the beginning. All the activity that you do on the raw data to make it “clean” enough to input to your analytical algorithm is called data wrangling or data munging. If you want to create an efficient ETL pipeline (extract, transform and load) or create beautiful data visualizations, you should be prepared to do a lot of data wrangling. (†2614)
data wrangling : Data wrangling is an important part of any data analysis. You’ll want to make sure your data is in tip-top shape and ready for convenient consumption before you apply any algorithms to it. Data preparation is a key part of a great data analysis. By dropping null values, filtering and selecting the right data, and working with timeseries, you can ensure that any machine learning or treatment you apply to your cleaned-up data is fully effective. (†2615)
