Data Science for Professionals

Install the R environment and the R studio IDE, download CRAN base binaries, and verify with a hello world in the console.

Install the RStudio desktop open source version and set up a new R project to store code. Create a new R script, save it, and customize the appearance before coding.

Master the fundamentals of R data objects, starting with vectors and learning to create, combine, and subset them to store and manipulate information.

Learn to navigate R help pages and run examples by using double and single question marks, quotes for special characters, and the examples function from base and stats packages.

Learn to create and combine vectors, nest functions, and inspect objects with structure; generate sequences with sequence and rep, and draw random samples with rnorm and sample.

Explore vectorized operations in R, summing and comparing vectors, handling unequal lengths and Na values. Learn to subset vectors using indices, boolean masks, and negative indexing to derive targeted observations.

Explore vectors in data science by mastering boolean indexing, filtering with logical conditions, and set functions like union, intersect, and setdiff, using is element and all and any.

Load and explore data in R, plot mpg vs horsepower, fit linear models with lm, and create interactive visuals with ggplot2 and plotly.

Tie together concepts learned so far by tackling the risk dice game as a big integrated exercise. Apply r to model attacker and defender dice rules and compute battle odds.

Learn to estimate battle odds in risk by simulating dice rolls, computing attacker and defender losses, and solving with Monte Carlo simulations and modular functions.

Discover tidy data principles by using tidyverse tools like tidier, dplyr, and ggplot2 to tidy HR data, import datasets, and prepare one observation per row with one variable per column.

This lecture introduces dplyr for data manipulation, showing how to use group_by and mutate with the pipe to clean and transform data frames and tibbles.

Learn to clean and summarize data with dplyr, computing attrition rates by gender and department, age groups, distance from home mean and standard deviation, then export for graphs using ggplot2.

Explore ggplot2's layered grammar of graphics to map aesthetics, build box and density plots of hourly wage rates by attrition and department, and compare distributions.

Analyze factors like overtime and stock options driving attrition, visualize proportions with ggplot2, and compare tree and random forest models to predict who will leave.

Learn how R Markdown and Markdown workflows create reproducible, transparent reports in formats such as HTML and Word, while using Git version control to enable collaboration and efficient project management.