This 3 day course covers the fundamentals of applying statistical analysis concepts when analyzing data, the primary goal of modern data scientists. The foundations of statistical data analysis will be discussed and demonstrated using examples written in R, along with an introduction to the R programming language.
Audience
No prior knowledge of statistical data analysis or of the R language is assumed. This course is perfect for those interested using the R language to perform the work of a data scientist, answering important business questions by applying a statistical analysis approach to data.
Course Outline
Module 1: Foundations of Statistical Analysis
- The Need for Statistical Analysis of Data
- An Introduction to Statistical Concepts
- Understanding Data
- The Data Analysis Process
- Data Sampling
- The Statistical Inference Pipeline
- Regression, Prediction, and Classification
Module 2: Getting Started with R
- What is R?
- How to get R
- Packages
- Development Tools for R
- Math Operations
- Storing the Results
- Command History
- Saving your Work
Module 3: Data Basics
- Getting Data into R
- Reading a File from Disk
- Saving Data to Disk
- Dealing with Missing Values
- Using Data Objects
- Types of Data
Module 4: Working with Data
- Complex Data
- Data Frame
- Matrix
- Factor
- Adding Data
Module 5: Summarizing Data
- The str() Function
- The summary() Function
- Summarizing Data Objects
- Using Summary Tables
- Converting Objects into Tables
- The ftable() Function
- Using Cross-Tables
Module 6: Means, Medians and Modes
- What is the Mean?
- The mean() Function
- Finding Outliers
- What is a Median?
- The median() Function
- What is a Mode?
- The modeest Package
Module 7: Measuring Variation
- What is a Variation?
- Calculating Variance
- The var() Function
- Standard Deviation
- The sd() Function
Module 8: Standardizing Scores
- The Need for Standardization
- The Standard Score or Z-Score
- The scale() Function
- The T-Score
- The rank() Function
- Understanding Scores
Module 9: Distributions
- The ‘Normal’ Distribution
- Mean and Standard Distributions
- The dnorm() Function
- Plotting a Distribution
- The ggplot() Function
- The pnorm() Function
- The qnorm() Function
- The rnorm() Function
- The Standard Normal Distribution
