Master in Data Visualization and Analysis with R in 2023

Explore variables and data types in R, including numeric, integer, complex, logical, and character data, and learn vectors, matrices, arrays, lists, factors, and data frames, with field naming rules.

Learn to create and manipulate vectors in R using c(), access elements by index, delete vectors, and compute basics like length, mean, and max while exploring v1, v2, and v3.

Explore matrices and arrays in R, learn to define two-dimensional data structures, arrange data into rows and columns, access elements, and perform arithmetic operations.

We explore factors as categorical variables with limited values, using gender and north south east west examples, and convert a vector to a factor with factor() to view its levels.

Create data frames in R from vectors, use data.frame, inspect structure with str and class, and extract, filter, and modify columns.

Explore how to create heterogeneous lists in R with list(), name elements, access by index or name, remove elements with null, and combine lists with c().

Explore data handling basics through arithmetic and relational operators, including vectors and matrices. Learn operations like addition, subtraction, multiplication, division, exponent, modulus, and integer division.

Master relational and logical operators in R, including <, <=, >, >=, ==, !=, and, or, not; explore vector comparisons, membership tests, assignment, and the colon and transpose-based matrix operations.

Learn to implement decision making in R with conditional statements like if and nested checks, guided by user input and type casting, with switch-based decisions and practical discount examples.

Master looping in R with for, while, and repeat loops; compare when to use each, and explore examples that print even numbers and handle user input with break.

Explore functions in R, including inbuilt and user-defined types, with syntax, default arguments, and examples like mean and addition.

Learn to import csv data into R, set and verify the working directory, read csv files with headers, create data frames, and export results to csv.

Learn to read and write Excel files in R using the Excel package, including loading the library, reading a data frame with headers, and writing a dataset to a sheet.

Explore how XML uses extensible markup language to describe and display data, and learn to parse XML in R, extracting student records into a data frame.

Explore web data scraping to transform unstructured web content into structured data, using regular expressions and fixed pattern matching for sentiment analysis, reviews, and demand analysis.

Explore how to create and customize pie charts in R, including setting data vectors, labels, colors, titles, and explode parameters, and compare with bar charts as part of data visualization.

Learn to create bar charts with the barplot function in R, including vertical and horizontal bars, labeled axes, colors, and stacked or grouped comparisons for quarterly revenue data.

Explore bar charts using ggplot and the grammar of graphics, learning aesthetics, geoms, scales, and facets to build meaningful, layered data visualizations.

Boxplots visualize data distribution by highlighting minimum, maximum, median, and first and third quartiles, and reveal outliers beyond the whiskers.

Explore histograms in data visualization by using bins to show frequency distributions, construct bars from data vectors, label axes, and interpret tumor growth over time.

Plot a line chart to compare revenue for 2017 and 2018 by linking monthly data points with lines, adjusting color, and toggling points in R.

Learn how to use a scatter plot to explore the relationship between two variables, with a weight vs. mpg example from the empty cars dataset, and preview regression analysis.

Explore mean, median, and mode in data science with R, including when to use each measure, guided by descriptive, predictive, and prescriptive analytics.

Learn how linear regression links a predictor to a dependent variable, estimates slope and intercept, fits a regression line, and evaluates with p-values and R-squared.

Explore multiple regression, linking a dependent variable to two or more independent variables, estimate intercept and coefficients, and build a predictive model from data using marketing spend.

Explore the normal distribution, its mean and standard deviation, and how data can be discrete or continuous; use the curve and normal probability to plot and interpret scores.

Study the binomial distribution for a fixed number of trials with two outcomes and constant probability of success. The binomial random variable X counts successes in n trials.

Explore hypothesis testing, correlation, and feature selection to understand data relationships, apply chi-square tests, and interpret p-values using a gender and ice cream flavor example.