Dirty Data Dojo: Cleaning Data (Excel & Python) (The Course)
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Course Info

This course includes 1 attempt.


This course is about cleaning data in Excel and Python.

If you're looking for a data cleaning course in Excel and R, you can find what you're looking for here:



Data analysts, statisticians and Data Scientists typically spend 80% of their time cleaning data before they can get started on the analysis and, depending on the dataset, this process can often take up to two weeks to complete!

With a solid process and the right tricks of the trade, though, datasets – irrespective of their size – can be rendered analysis-ready in just a couple of hours.

In Dirty Data Dojo: Cleaning Data, you’re a statistician in a busy research-active teaching hospital.

Most days you will have researchers coming into your office to give you a new dataset in Microsoft Excel that they wish you to analyse, and they often have short deadlines – usually within 2 weeks.

The problem you have is that no dataset you’re given is ever analysis-ready, and you need to clean and prepare each one before you can get started, a process that often takes up to 2 weeks to complete manually – for every dataset!

At this stage, you’re no doubt thinking to yourself that this is an impossible situation and is unrealistic.

Au contraire! This was the precise situation your author found himself in, and in 8 years in the job he never missed a deadline. EVER!

The key is in finding ways to automate your data handling processes as much as possible, and Dirty Data Dojo: Cleaning Data is designed to teach you all the necessary principles you need to fast-track your data handling process to get any dataset analysis-ready in just a couple of hours.

You will learn the concepts in Excel as it is a non-threatening, familiar medium that everyone starts with when handling data.

You will also be encouraged to grow beyond Excel and create and submit your own code using Python and the Pandas library (optional) so you will have data cleaning and preparation skills to die for!

(Important Note - I won't formally teach the concepts in Python, but I will point you in the right direction with code hints).

Here’s a quick rundown of what you’re going to learn:

  • Step 1: you’ll learn the features of what makes a good dataset and – more importantly – a bad one!
  • Step 2: you’ll learn how to remove all unwanted spaces from your dataset in one second!
  • Step 3: you’ll learn how to standardise the case of text entries in one epic ninja move
  • Step 4: you’ll learn all the essential steps in cleaning numerical and text data
  • Step 5: you’ll learn the precise order in which you should apply all these techniques

Is This Course Right For Me?

Data collected from many sources is often dirty and needs to be cleaned before analysis can begin – especially data collected non-electronically. Data handling, cleaning, processing and understanding are crucial aspects of any type of data analysis, Data Science or statistics job.

Dirty Data Dojo: Cleaning Data does not require a technical background and is therefore accessible to researchers, business analysts and anyone analysing data – the data handling processes in this course can be applied to data from any sector, not just medical data.

There are no pre-requisites for Dirty Data Dojo: Cleaning Data, but a basic understanding of the principles of data and Microsoft Excel would be an advantage, as would a basic grounding in Python and Pandas.

The principles contained in this course, while essential skills for all types of data analysis, are not difficult to learn. As such, Dirty Data Dojo: Cleaning Data is classified as ‘For Beginners’ – although even the more advanced analyst will also find huge benefits to learning how to super-speed their data cleaning processes!

Upon completion of Dirty Data Dojo: Cleaning Data, the successful student will be able to transform datasets from dirty to analysis-ready in hours rather than weeks – irrespective of the size of dataset*.

The student will learn to clean and prepare data in the following technologies:

  • Microsoft Excel
  • Pandas library in Python (optional)


*Note – in Dirty Data Dojo: Cleaning Data we will only consider ‘Small Data’, that is, data that is small enough to fit into a single Excel spreadsheet – 1,048,576 rows by 16,384 columns. Datasets larger than this require specialist tools and methodologies that are outside the scope of Dirty Data Dojo: Cleaning Data.


Suitable for beginners to cleaning data, in this course you’ll learn to ‘play’ with your data, learning what you can and – more importantly – can’t do with it.

Each lesson starts with a quick question to get your mental juices flowing, then you’ll dive straight into the data. There will be LOTS of playing and practising with data, and you’ll learn by doing.

In this course you will get:

  1. Around 2 hours of video content
  2. Over 2 hours of practice exercises
  3. Excel is used as a learning tool, but the lessons learned are transferable to other media
  4. The student will be encouraged to write their own Python scripts (optional)
  5. Data files are provided for the student to practice with
  6. Practical learning experience with real data

Course Outline

1. Anatomy of a Good Excel Workbook

  • Here you’ll learn how to set up an Excel workbook to version-control data cleaning and create ‘flow’ to your data cleaning processes across several worksheets
  • (1 hour)

2. Removing Unwanted Spaces – Part 1

  • Here you’ll learn how to use various Excel formulae to automate the removal of extraneous leading, interstitial and trailing spaces and all unwanted characters from your data
  • (1 hour in Excel + 1-2 hours in Pandas)

3.  Removing Unwanted Spaces – Part 2

  • Here you’ll learn how to combine these formulae into a single formula that automates the removal of all extraneous spaces and unwanted characters from your entire dataset in one awesome step!
  • (1 hour in Excel + 1-2 hours in Pandas)

4. Standardising the Case of Text Entries

  • Here you’ll learn how to standardise the case of your text data and integrate this into the previous formula
  • (1 hour in Excel + 1 hour in Pandas)

5. How to Clean Text (Categorical) Data – 2 methods

  • Here you’ll learn 2 methods of automating the cleaning of text data
  • (1 hour in Excel + 1-2 hours in Pandas)

6. How to Clean Numerical (Continuous) Data

  • Here you’ll learn how to identify numerical data that is contaminated with text – and clean it!
  • (1 hour in Excel + 1-2 hours in Pandas)

Learning Outcomes

In this course you will learn how to:

  1. Set up an Excel workbook for maximum effectiveness
  2. Remove all unwanted spaces from your entire dataset – in one step!
  3. Clean text data and numerical data
  4. Perform all steps in the correct order
  5. Improve your data handling skills


Students completing Dirty Data Dojo: Cleaning Data will have the knowledge and confidence to be able to clean their data in Excel and Python (optional) quickly and accurately.

Complete with HD videos, data, examples and practice exercises, you’ll be able to work alongside the instructor as you work through each concept, and will receive a certificate of completion upon finishing the course.



  • Python
  • Business Analysis
  • Data Science
  • Sciences
  • Social Sciences

Course Material

  • Preamble
  • Course Introduction
  • Who the Project is For and What They Will Learn
  • Feedback
  • What to Expect
  • Course or Project?
  • Excel
  • Python
  • Getting Started
  • Course Outline
  • About This Course
  • 14 Day Data Cleaning Challenge
  • All 6 Dirty Data Dojo Courses
  • 14 Day Data Cleaning Challenge
  • All Our Courses
  • Get in Touch
  • Prerequisite Test
  • Artboard 3 Created with Sketch.
    Exercise 1
  • Course Downloads
  • Chapter 1 - Getting Your Data Organised
  • Lesson 1.1 - Practical Data Cleaning
  • Quiz - Practical Data Cleaning
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    Quiz 13 attempts allowed
  • Chapter 2 - How to Kill the Invisible Man (Part 1)
  • Lesson 2.1 - Removing Unwanted Spaces
  • Lesson 2.2 - Removing Non-Printing Characters
  • Lesson 2.3 - Removing Other Unwanted Characters
  • Lesson 2.4 - Single Solution
  • Quiz - Removing Unwanted Spaces (and other stuff)
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    Quiz 23 attempts allowed
  • Chapter 3 - How to Kill All the Invisible Men (Part 2)
  • Lesson 3.1 - Single Solution in Larger Datasets
  • Quiz - Removing Unwanted Spaces Part 2
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    Quiz 33 attempts allowed
  • Chapter 4 - Standardising the Case of Text Entries
  • Lesson 4.1 - Case Standardising in the Single Solution
  • Quiz - Case Standardisation
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    Quiz 43 attempts allowed
  • Chapter 5 - How to Clean Text Data (2 Methods)
  • Lesson 5.1 - Remove Duplicates and Find & Replace
  • Quiz - Cleaning Text Data
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    Quiz 53 attempts allowed
  • Lesson 5.2 - Remove Duplicates and VLOOKUP
  • Quiz - Cleaning Text Data
  • icon/quiz Created with Sketch.
    Quiz 63 attempts allowed
  • Chapter 6 - How to Clean Numerical Data
  • Lesson 6.1 - ISNUMBER - Cleaning Numerical Data
  • Quiz - Cleaning Numerical Data
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    Quiz 73 attempts allowed
  • Chapter 7 - Order, Order…
  • Lesson 7.1 - The Plan
  • Chapter 8 - Submit Your Work
  • Quiz - Using the Single Solution
  • icon/quiz Created with Sketch.
    Quiz 81 attempts allowed
  • Chapter 9 – Practice Session
  • Epilogue
  • 14 Day Data Cleaning Challenge
  • All 6 Dirty Data Dojo Courses
  • 14 Day Data Cleaning Challenge
  • All Our Courses
  • Get in Touch
  • Nothing to see here…


    • Lee Baker is an award-winning software creator with a passion for turning data into a story.

      A proud Yorkshireman, he now lives by the sparkling shores of the East Coast of Scotland.

      Physicist, statistician and programmer, child of the flower-power psychedelic ‘60s, it’s amazing he turned out so normal!

      Turning his back on a promising academic career to do something more satisfying, as the CEO and co-founder of Chi-Squared Innovations he now works double the hours for half the pay and 10 times the stress - but 100 times the fun!


This course has a private forum for learners who are taking this course.

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