Remote Data Science Consultant
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.
I also provide a host of data science tutorials through my website, and my work has also been published on leading data outlets such as Data Science Central and Sitepoint.
You can get in touch with me at firstname.lastname@example.org. Alternatively, you can give me a call at either of the following numbers depending on where you are based:
US/Canada: +1 424 644 6743
Europe: +44 208 144 7926
Portfolio and Skills
Programming Experience: Python (3 years), R (4 years), Shiny (1 year), mySQL (4 years), C++ (1 year), SPSS (2 years), STATA (2 years), Microsoft Azure (1 year)
Statistics and Machine Learning: ANOVA, ARIMA and Holt-Winters, Decision Trees, Fixed Effects Estimation, GARCH, K-Means Clustering, Linear Regression Modelling (OLS and Logistic Regressions), Monte Carlo Simulations, Neural Network Modelling, Probability Distributions, Stationarity Testing and Cointegration Analysis, Time Series Decomposition
Libraries: car, corpcor, covmat, egcm, lmtest, neuralnet, pandas, matplotlib, MySQLdb, numpy, pyodbc, rpart, rvest, Scikit-learn, scipy, statsmodels, tseries
- Implement an ARIMA model using statsmodels (Python)
- Linear Models in R: OLS and Logistic Regressions
- mySQL Queries (Financial Asset Database)
- neuralnet: Train and Test Neural Networks Using R
- Normal Distributions, Monte Carlo Simulations and Random Walks
- Python-mySQL Interaction (MySQLdb): Azure ML Studio
- Quantmod: Analyse stock and forex data using R
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.J. Gariepy, Communications Manager
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.
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
Subscribe To My Mailing List Now, And Receive...
A Free Copy of My Data Science Course For R and Python
Subscribe to my mailing list, and receive my data science course: "Regression Analysis and Data Structuring Methods: R and Python". In this course, you will receive access to a 36-page manual on the use of data science techniques in R and Python. This course will teach you how to:
- Run an OLS Regression in R and test for violations
- Use statsmodels to run an OLS regression in Python
- Test a time series dataset for autocorrelation and remedy this issue
- Check for cointegration between two time series models
- Structure data effectively using data frames
- Create an ARIMA model in Python and R
Upon registering for my course, you will also gain access to the hard code for many of the data science techniques that I describe right here on my website, as well as weekly data science newsletters.