Data Analytics

Part 1

The Basics

• Sources of data, data sampling, data accuracy, data completeness, simple representations, and handling practical challenges.

Fundamental Statistics

• Mean, average, median, mode, rank, variance, covariance, standard deviation, "lies, more lies and statistics," adjustments for small sample sizes, descriptive statistics, and insensitive measures.

Part 2

Basics of Data Mining and Representation

• Single, two, and multi-dimensional data visualization, trend analysis, determining what to visualize, box and whisker charts, common errors and issues.

Data Comparison

• Correlation analysis, autocorrelation function, practical considerations of data set dimensionality, multivariate and non-linear correlation.

Part 3

Histograms and Frequency of Occurrence

• Histograms, Pareto analysis (sorted histogram), cumulative percentage analysis, law of diminishing returns, percentile analysis.

Part 4

Frequency Analysis

• Fourier transform periodic and aperiodic data, inverse transformation, practical implications of sample rate, dynamic range, and amplitude resolution.

Regression Analysis and Curve Fitting

• Linear and non-linear regression, order; best fit; minimum variance, maximum likelihood, least squares fit, curve fitting theory, linear, exponential, and polynomial curve fits, predictive methods.

Part 5

Probability and Confidence

• Probability theory, properties of distributions, expected values, setting confidence limits, risk and uncertainty, ANOVA (analysis of variance).

Some More Advanced Ideas

• Pivot tables, the Data Analysis Tool Pack, online-based analysis tools, macros, dynamic spreadsheets, sand ensitivity analysis.