Subjects

Foundations of Data Science

The mathematical and statistical underpinnings of the analysis of data and the machine learning algorithms powering the data science and AI revolution.

Probability — Discrete, Random Variables, Stochastic Processes, Measure Theory.

Statistics - Descriptive and Inferential, Parametric and Nonparametric Methods, Bayesian Methods, Maximum Likelihood, Estimator Theory, Statistical Learning Theory, Sampling Methods, Markov Chain Monte Carlo.

Linear Algebra - Vector and Normed Spaces, Matrix Decompositions

Optimization - Linear, Convex, Integer, Mixed Integer, Stochastic

Applied Data Science and Machine Learning

The main tools of the practicing 21st century data scientist — statistical/machine learning algorithms and their implementations and the Python and R data science stacks,

Python — Base, Numpy, Scipy, Pandas, Matplotlib, Seaborn, Bokeh, Statsmodels, Scikit-learn, Hyperopt, Keras, Tensorflow, Pytorch

R - Base, GLM, Tidyverse (Dplyr, GGplot, Tidyr, Readr, Tibble), Tidymodels, Shiny

Machine Learning - Linear Regression, General Linear Models, Supervised, Unsupervised and Ensemble Learning.

Deep Learning - Vanilla Deep Neural Networks, Convolutional, Recurrent, LSTMs.

Ingestion and Storage - Streaming, Scraping, SQL, NoSQL

Preparation and Exploration - Cleaning and Preprocessing, Wrangling, Exploratory Data Analysis, Visualization, Applied Descriptive and Inferential Statistics.

Modeling - Preprocessing, Feature Selection, Feature Engineering, Fitting, Hyperparameter Tuning, Ensembling

Reporting - RStudio, Quarto, Jupyter Notebooks, Jupyter Lab, Markdown, HTML/CSS, LaTeX

Mathematics

The universal language of science, both pure and applied. From rigorous proofs to long derivations to gruesome calculations, help is available in most major areas.

Calculus — Single, Multivariable

Foundations - Mathematical Logic, Set Theory, Category Theory

Analysis - Real, Complex

Algebra - Linear, Abstract

Geometry - Formal, Differential, Algebraic

Topology - Algebraic, Differential

“Think deeply about simple things”

— John Baez, mathematical physicist

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If you need help in any of the subjects above, or are just interested in them, please don’t hesitate to reach out for a free 30 min consultation.