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Michael Grogan's user avatar
Michael Grogan's user avatar
Michael Grogan's user avatar
Michael Grogan
  • Member for 7 years, 8 months
  • Last seen more than a month ago

I help companies unleash the statistical capabilities of Python and R to conduct effective business analysis - offering both training and consultation services.

With a background in economics and statistics, I have devised numerous training courses in Python and R for major educational outlets, including O'Reilly Media and Manning Publications. I have also delivered various training seminars at major data conferences, including Big Data Europe and ML Conference Munich.

As both a data science consultant and financial writer, I have implemented statistical solutions in Python and R to help companies solve a range of business issues. This has included the use of regression analysis to allow a company to identify optimal levels of marketing spend while maximising revenue, as well as creating reactive, web-based visualization dashboards to analyse pricing and occupancy data of a major hotel chain to infer future growth patterns.

Teaching Experience

Time Series Forecasting with Bayesian Modeling. LiveProject series produced for Manning Publications.

Devised Python-based liveProject series to illustrate modelling of time series shocks with Bayesian Dynamic Linear Modeling, modeling of posterior distributions with PyMC3, MCMC sampling with Prophet, and Structural Time Series Modeling with TensorFlow Probability.

TensorFlow 2.0 Essentials: What’s New. Video seminar produced for O’Reilly Media.

Conducted live training of TensorFlow 2.0 using Python - illustrated to students the use of eager execution and AutoGraph, as well as tf.keras for neural network modelling across classification, regression, and time series datasets.

Business Analytics with R — Statistics and Machine Learning. Video series produced for O’Reilly Media.

Created extensive video series in the instruction of R illustrating data manipulation techniques, regression analysis and hypothesis testing, along with classification and regression-based machine learning techniques.

Technical Skills

Cloud: AWS, Azure, Render

Languages: Python, R, SQL

Libraries: InterpretML, PyMC3, scikit-learn, statsmodels, TensorFlow

Platforms and relevant tools: PyCharm, Jupyter Notebook, pgAdmin4, RStudio, Git, Docker, Linux

Visualization libraries: Dash, geopandas, ggplot2, matplotlib, plotly, pyplot, seaborn, Shiny

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