Michael Grogan

Data Scientist - Consultant and Educational Publisher


My name is Michael Grogan, I am a data science consultant with a passion for statistics and programming. I specialise primarily in Python and R.

My educational background is a Master's degree in Economics, and as such I have a particular interest in the application of data science to implementing business solutions.

My clients have included individuals, startup firms, as well as larger corporate clients. I have worked on projects across a range of industries, including finance, sales and marketing, healthcare, and government policy.

Additionally, I have also published educational courses on the field of data science for various publishers. Feel free to take a look at my O'Reilly Profile to view my comprehensive video series on R, where I cover data manipulation, regression analysis and machine learning techniques..

You can get in touch with me at michael@michaeljgrogan.com.

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My Regression Analysis Courses In Both Python and R

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Why You Should Subscribe To This Course

Regression Analysis is one of the most fundamental analytics tools within data science. It is how we make sense of relationships between different sets of data, and a skill that you must absolutely master if you are to be a successful data scientist.

In my courses, you will learn how to run cross-sectional and time series regression techniques in both Python and R. You will receive access to the e-books, as well as all datasets and code to allow you to replicate the examples yourself.

What's more, you will also be able to e-mail me anytime at michael@michaeljgrogan.com for personalised help with any queries you may have on the course content.

By the end of this course, you will be able to:

  1. Identify important terminology in regression analysis and be able to construct regression equations
  2. Run an Ordinary Least Squares regression
  3. Test for multicollinearity and heteroscedasticity
  4. Construct and interpret a logistic regression
  5. Screen a time series for stationarity
  6. Forecast a time series using an ARIMA (Autoregressive Integrated Moving Average) model


Michael Grogan is the best data scientist I have ever worked with. I found him through his web site. From his first email, it was clear Michael is a professional and capable individual who truly cares about his clients and ensuring their success. I had a significant project to complete on a tight timeline.

Despite changing expectations and scope, Michael was able to deliver timely, comprehensive, and, most importantly, easy-to-understand analysis of my data. Michael and I live in different continents and are separated by several time zones. This was never an issue and it was easy to communicate with him via Skype and email.

Normally, I don’t write these types of testimonials, but I feel compelled to write one for him, because without his knowledge and willingness to dive deep into my data and utilize different statistical methods, my project would have not been successful. Thanks, Michael.

J. Gariepy, Communications Manager

Michael’s website is a fantastic reference point for anyone interested in Data Science. He provides real world problems and solutions, showing practical applications often lacking in online resources. He approaches Data Science from a strong mathematics background, providing a fresh perspective not found in the mostly computer science based tutorials.

B. Larson, Analytics4all

“We certainly want to publish your R-Shiny article. This is a superb effort. To be honest, I don’t get many article drafts as well written, structures and crafted as this, so special kudos for that.”

Editor, Sitepoint

Your work was very useful to me, and I also want to incorporate new ideas from your portfolio into the project, such as k-means clustering. After this project, I want to learn more R coding from you - I am very impressed with your work.

M. Fawad, PhD Student