Data Analytics with R from Scratch

Gain hands-on exposure to R for basic exploratory data analysis, learn alongside Python concepts, and build in-demand skills in the R ecosystem and installation through practical case studies.

Explore data science and analytics through real-world use cases, data types, and the analytics lifecycle, from exploratory data analysis to predictive modeling and product recommendations.

Analyze structured data in data analytics with r from scratch, focusing on the four levels of information—nominal, ordinal, interval, and ratio—while exploring data sources, scraping, and primary versus secondary data.

Navigate the data analytics project cycle from collecting raw data through cleaning and exploratory data analysis. Build predictive models, visualize insights with dashboards, and communicate findings to stakeholders.

Explore the R environment from scratch, compare data types and statistics with Python, and learn to manipulate, visualize, and predict insights using dedicated R packages.

Learn core R data types and data structures, create and access vectors of different types, handle missing data, and work with data frames, factors, and matrices in Jupyter.

Learn how to assign variables in R, check value types with is.numeric, is.character, and is.logical, and create vectors using c to explore data analytics with R from scratch.

Explore object attribute functions like names, dimensions, and class to inspect data frames, count units with length, and measure the number of characters, guiding exploratory data analytics.

Learn to create and manipulate matrices in R using matrix(), fill by column or by row, assign row and column names, and subset by rows or columns.

Create and explore data frames in R using the data frame function, inspect dimensions, structure, and names, then merge datasets with join and left join.

Develop data manipulation, cleaning, loading, and visualization skills in R via a real estate assets case study. Import data from multiple sources using RStudio or Jupyter Notebook with packages.

Learn to import data into R from flat files, Excel, and databases, using read functions and working directory setup, and handle SAS, SPSS, and Stata outputs.

Learn to read dot tab delimited data in R with specialized functions and header and separator options. Explore loading CSP text and Excel files via utils and tidyverse.

Learn to load data with the readr and utils packages, reading csv files, whitespace-delimited formats, and excel files, and tailor imports with header, separators, and column-type arguments.

Learn to load multiple datasets into RStudio, set the working directory, and read data from text and Excel formats using the utils package.

Load data into R from text files using the utils package, explore datasets such as fast food and vegetables, and verify data loading with the us retort table.

Explore loading data into R using the tidyverse and its read functions. Load text and excel formats, inspect data types such as character, double, and integer.

Load data into R by invoking the read Excel package to read urban populations and urban population two datasets, and install and use the read table package for larger datasets.

Master data management with dplyr by mutating GDP per capita to total GDP, filtering for 2007, and arranging results to reveal country rankings by GDP.

Explore data visualization in R with ggplot-like plotting, building scatter, bar, and line graphs from a government dataset, displaying GDP per capita on the x-axis and life expectancy on y-axis.

Learn to create basic scatterplots in R using ggplot2, mapping GDP per capita to x, life expectancy to y, color by continent, and size by population, with faceted visuals.

Explore the tidyverse for data manipulation and visualization using the commander dataset in a case study; learn to filter, arrange, summarize, and mutate via the pipe operator.

Learn to use the tidyverse to inspect a six-variable dataset, perform basic exploratory analysis on life expectancy, population, and GDP per capita, and filter by continent with dplyr.

Explore tidyverse data tools to filter the Gap dataset for 2007, group by continent, and summarize mean life expectancy across Africa, America, Europe, and Oceania.

Learn to perform bivariate analysis using histograms and scatter plots, assess correlations between numerical variables, identify independent variables, and prepare data for machine learning with iris data.

Explore correlation plots to visualize relationships between variables using scatter plots by species, identify high and low correlations, and classify data points.

Explore the iris dataset through an exploratory data analysis project in a Jupyter notebook, using box plots to visualize distributions of five variables and compare species.

Explore exploratory data analysis and data visualization to gain insights from datasets. Describe the iris data attributes and relationships, preparing analysis in R and Jupyter Notebook.

Subset the iris dataset by species to create a focused data frame, then inspect its variables with str to understand the spread of sample data for each species.

Perform univariate analysis by examining data distributions and medians with box plots and histograms to distinguish iris species and guide exploratory analysis before modeling.

Explore linear regression to predict mpg from automobile attributes such as weight and displacement. Learn to interpret regression results in a supervised learning context.

This lecture analyzes how weight affects car mileage using the empty cars dataset, employing scatterplots, correlations, and simple and multiple linear regression in R, including auto versus manual transmission.

Explore k-means clustering using the iris dataset, focusing on application aspects of unsupervised learning, including pattern discovery for customer segmentation, pricing, and market research.

Load the iris dataset and install the Geechee plot to package and tidyverse to create visualizations, then examine sepal and petal measurements and plot scatterplots to reveal clusters.

Explore k-means clustering on the iris dataset, using petal length and width to identify three species, compare results, and derive insights from visualizations and outputs.

Apply the knn classification technique to a cancer dataset to predict malignant versus benign cases. Train on labeled data, test predictions, and assess accuracy in this supervised learning workflow.

Explore how the k-nearest neighbors algorithm is applied to breast cancer diagnosis, covering dataset details, classification results, and the method's scaling limits and computation concerns.

Apply KNN to a UCI breast cancer dataset, prepare data (rename attributes, numeric conversion, missing-value handling, class encoding), perform train-test split, and evaluate with cross-tabulation.