Math Methods for Data Science:

Review of basic math concepts: polynomials, vectors, matrix, probabilities

• • Polynomials (revisiting algebra)

  • Vectors (concepts, components, vector algebra, representation, operations, dimensions, coordinate system, basis)

  • matrices (operations, scaling, product, inverse, trace, determinant)

• Eigenvectors, Matrix Decomposition

  • Linearity

  • Row reduction techniques

  • Eigenvalues and eigenvectors

  • Matrix decomposition

• Basic Optimization Techniques

  • General concepts of optimization

  • Canonical formulations in optimization

  • Loss functions

  • Gradient descent

• Bayes Rule, Maximum likelihood

  • Probability (distributions, events, expectations, variance, conditional probability, probability rules, independence)

  • Bayes' Rule/Bayes' Theorem

  • Likelihood, maximum likelihood

Introduction to epidemiology:

Wayne State University:

• Unit 1

  • Course Overview, What is epidemiology?  History and basic principles, Introduction to population science, disease transmission, outbreak

  • Surveillance, measures of morbidity, mortality and disease impact

  • Screening, Validity and Reliability

  • Randomized Control Trials

  • Cohort Studies and Cross Sectional studies

  • Case-control and ecologic

• Unit 2

  • Risk and Association

  • Causality and studies

  • Bias, Confounding

  • Effect Modification

  • Prognosis and Survival

  • Special Topic - TBD

SAS programming for public health:

Wayne State University:

• Introduction to SAS: Introductory topics and reading simple data files

• Reading and exporting data files, dates

• Creating new variables: Numeric and character functions

• Conditional and iterative processing

• Array processing, variable shortcuts, and formatting variables

• Simple statics using MEANS, FREQ, CORR and UNIVARIATE

• Creating PDF output via ODS

• Reporting using TABULATE and REPORT

• Creation of charts and graphs for data summary and display

• Enhanced output using the ODS

• Merging, subsetting, and transposing files with one observation per subject

• Merging, subsetting, and transposing files with multiple observations per subject

• Introduction to the SAS macro language. 

• Using global macro variables and creating and calling macro functions

• Using iterative processes within macro functions

• Linear regression, ANOVA, and logistic regression modeling

Introductory Statistics:

Washtenaw Community College:

• Unit 1: Basic concepts, visualization, numerical summarization:

  • Basic Concepts:

  • Graphical summaries of data:

  • Numeric summaries of data

  • Simple linear regression

• Unit 2: Probability, discrete probability distributions, normal distributions

  • Probability

  • Discrete probability distributions and binomial

  • Normal distributions and sampling distributions

• Unit 3: Confidence intervals and hypothesis tests

  • One population confidence intervals

  • One population hypothesis tests

  • Two population confidence intervals

  • Two population hypothesis tests