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.