Data Cleaning for Analysts and Researchers

Data Cleaning is a Waste of Time!

Having to correct errors and mistakes in your data is a waste of your time and it gets in the way of your analysis and getting your results.

But there are steps you can take not only to clean your data efficiently but also to minimise the errors going into your data in the first place.

When you learn the steps and the essential Excel tools you need to clean your data, you:

  • become more productive and save hundreds of hours
  • get your results much faster
  • keep your boss happy

I have been there.

Every week, while I was working as a medical statistician at one of the largest research hospitals in Europe, somebody would bring me their new project data and give me the tightest deadline to get it analysed. The problem was that I had to clean it first, which took me weeks every time. So I had to develop a system to get the data analysis-ready quickly – and to also teach researchers how to reduce the errors and mistakes in their data before they gave it to me.

This system was fine-tuned over several years of being a statistician, and is what you will be learning in Data Cleaning For Analysts and Researchers.

 

What’s included in the course

This mega-bundle of courses will teach you step by step how to go from recording your data to getting it clean and ready for analysis. No more, no less. Everything is explained in simple language and is perfect for beginners.

  1. Welcome and Overview
  2. Data Collection
    1. How to import data into Excel … for when you already have data
    2. How to record data on paper … for when you’re just starting out collecting data
    3. How to enter data Manually into Excel … for when you’ve collected your data and you need to transcribe it into Excel
  3. Data Cleaning
    1. How to remove Trailing and Leading Spaces … and all other invisible and non-printing characters, from your entire dataset in just 60 seconds!
    2. How to clean Text Data … correcting typos and incorrect entries and getting your text data ready for analysis
    3. How to clean Numerical Data … identifying and correcting poorly formatted numerical data
  4. Data Codification and Classification
    1. How to make your data calculations … use formulae to calculate composite variables and prepare it for analysis
    2. How to code your data … get your text data ready for analysis – including transforming it to numerical codes
    3. Data Types … learn the difference between ratio, interval, ordinal and nominal data to help make better analysis decisions
  5. Data Integrity
    1. Prepare your dataset for analysis … learn how to transform a dataset that’s been created for data storage into one that’s analysis-ready
    2. Check that your numerical data is sensible … real life has rules, and so does your data – learn how to discover and correct data that is not fit-for-purpose
    3. Remove outliers … learn how to quickly and easily identify outliers in your data so you can decide whether to include or exclude them from your analysis
  6. Work Smarter