Career advancement into hell

Okay, as long as I’m talking about Meetups, and specifically tech-focused Meetups here in the Valley They Call Silicon, a couple of weeks ago I went to one called “Career Advancement into Analytics” at the Hacker Dojo in Mountain View.

It should have been the ultimate in bleeding edge trendiness.

But to cut to the chase, I left after the speaker—never introduced—had been talking for 50 minutes and hadn’t made a lick of sense to me. (And I was not the first to bail out; that started happening at the 30 minute mark.)

I understood the individual words fine. It was when he strung them together in formations approximating sentences that I couldn’t follow. He started out by asking (or possibly stating), “What isn’t analytics(?)” and it went downhill from there.

Here’s another example: “But, the process of finding applied value with self-communication comes in very handy with presentation and developing skills.”

Anybody? Bueller?

However, it became vaguely clear that the intention of this session—which had originally been slated to cost $20 to attend, but became free after they allegedly found a corporate sponsor—was supposed to attract the attendees to a two-day “training” course ($750 for early birds; $1500 for late ones) to be held in July.

Here’s what that’s supposed to cover

Day 1 / Part 1

Basic Statistics and Mathematical Concepts
descriptive and inferential statistics
populations and samples
parameters and statistics
uses of variables: independent and dependent
types of variables: quantitative, qualitative and categorical
scales of measurement (nominal, ordinal, interval, and ratio)
Review of Basic Matrix Algebra and applications.

Descriptive Statistics
describing your data
measures of location
percentiles
measures of variability
histogram
normal distribution
assessing normality
measures of shape: skewness
measures of shape: kurtosis
comparing distributions

Confidence Intervals for the Mean
point estimators, variability, and standard error
distribution of sample means
interval estimators
confidence intervals
normality and the central limit theorem

Data Science: Hypothesis Testing
decision-making process
steps in hypothesis testing
types of errors and power
the p-value, effect size, and sample size
statistical hypothesis test
the t statistic, t distribution, one and two-sided t-test

Introduction to Statistics and Data Science
examining data distributions
obtaining and interpreting sample statistics
evaluating data distributions
constructing confidence intervals
performing simple tests of hypothesis

Day 2 / Part 2

Prerequisite Basic Concepts
descriptive statistics
inferential statistics
steps for conducting a hypothesis test
basics of Matrix Algebra

t Tests and Analysis of Variance
performing tests of differences between two group means
performing one-way ANOVA with the General Linear Model (GLM)
performing post-hoc multiple comparisons tests in GLM
performing two-way ANOVA with and without interactions

Linear Regression
producing correlations
fitting a simple linear regression model
understanding the concepts of multiple regression
model selection techniques in Regression to choose from among several candidate models
interpreting models

Linear Regression Diagnostics
examining residuals
investigating influential observations
assessing collinearity

Categorical Data Analysis (CAD)
producing frequency tables for CAD
examining tests for general and linear association
understanding exact tests.

Forecasting
Forecasting exercise by moving averages.

(That's a cut and paste, so [sic] to it all.)

I lost the will to live just looking at that on their Meetup site. So no, I’m not spending a weekend in July with this crowd. If they couldn’t explain what analytics is, they aren’t going to be able to advance my career into it.