Foundational data science with R

Beginner to intermediate

Mastering basic statistical analysis, summary, and visualization using R

This course provides a firm foundation on the fundamentals of data science using R, with a focus on key statistical methods, exploratory data analysis, and visualizations.

Before worrying about advanced analytics and neural nets, it is important to master the core skills. While this is certainly not a mathematical course, we won’t shy away from giving insight into the underlying mathematical theory. This invaluable online course will give you a solid grounding in the fundamental data science skills you need.

What you’ll learn and how you can apply it

At the end of this live, online training, you’ll understand:

How to summarize data sets with key statistics
Which statistics are optimal for large data sets
The trade-off between different summary measures.
The importance of color, transparency and shape in data visualisations
Mathematical distribution, and how it relates to “real” data
How key algorithms work

And you’ll be able to:

Summarize data sets
Graphically describe data
Compare groups of data using principled statistical techniques
Describe relationships among data sets with correlation and regression models
Use insight to predict future values

This live event is for you because...

You are a:

Programmer, interested in data science but with little or no statistics or mathematical background.
Manager who wants to summarize data sets.
Someone who uses data, but doesn’t have the necessary training to analyze and summarize it.

Prerequisites

No experience with R is necessary, but participants are expected to understand basic programming via another language, e.g. python, matlab, C, or Java. The course will be taught using R, but the focus is on the methods, rather than programming.

Schedule

The time frames are only estimates and may vary according to how the class is progressing.

DAY 1

Introduction and course overview (20 minutes)

Introduction
Course overview

Condensing data with numerical summaries (90 minutes)

Measures of location

Mean, median, mode
Example
Exercise / Q&A (25 minutes)

Measures of spread

Variance, standard deviation, quartiles, range
Example
Exercise / Q&A (25 minutes)

Streaming data

Mean vs median
variance vs quartiles
Example
Exercise / Q&A (10 minutes)

Break (5 min)

What, why and how of visualisation (90 minutes)

Scatter plot Colors

Number of points–should you summarize?
Transparency
log scales
Examples
Exercise/Q&A (25 minutes)

Histogram

How do determine the number of bins
Examples
Barplot
ordinal data
Examples
Boxplot
Great for comparison
Examples
Exercises/Q&A (25 minutes)

Wrap up

DAY 2

The normal distribution-what’s the point? (30 minutes)

Why does the normal distribution come from?
Shape: the famous bell shaped curve
Key parameters
The 2 standard deviations rule
Scaling data
(Data - mean)/sd
Example
Exercise/Q&A (10 minutes) Break (5 min)

How to compare groups (60 minutes)

The t-test
- The t-distribution
- Assumptions: normality, independent
- Example:
- OK Cupid data. Are the “daters” heights different from the standard population?
- The central limit theorem (basically, don’t worry about normality too much if your data set is big enough)
Confidence intervals:
- Standard errors vs standard deviation
- Example
- Exercise/Q&A

Break (5 min)

Capturing relationships with linear regression (90 minutes)

Correlation: linear relationship between two variables
Examples
Exercise/Q&A
Simple linear regression
Assumptions
Residuals: Observed - expected
Examples
Exercise/Q&A

Wrap-up (5 min)

Your Instructor