Michael Grogan: Data Scientist
My name is Michael Grogan, I am a data scientist with a passion for statistics and programming. 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 work has been published on leading data sources such as Data Science Central and Sitepoint. Moreover, I currently offer consulting services to a range of clients from individuals to startup firms to implement dynamic data science solutions. I have worked on projects across a range of industries, including finance, sales and marketing, healthcare, and government policy.
You can get in touch with me at email@example.com. 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
Ben Larson, Analytics4all
“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.”
“This is a superb effort, and we certainly want to publish your R-Shiny article. To be honest, I don’t get many article drafts as well written, structures and crafted as this, so special kudos for that.”
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.