Business Analytics with R: A Comprehensive Guide

Master the eight-step business analytics life cycle, from clear problem definitions and hypotheses to data acquisition, quality assessment, exploration, model development, and validation using methods like ARIMA.

Learn how to deploy a validated forecasting or scoring model, set up deployment and scoring environments, and monitor performance through an eight-step analytics process, including data quality and IT considerations.

Master the six-step problem solving process in business analytics, from recognizing and defining the problem to structuring, analyzing, interpreting results, and implementing solutions.

Explore major analytics software, including SAS, R, SPSS, and Excel Miner, and show how they support data import, modeling, and results interpretation in business analytics.

Learn how to download and install R via CRAN mirrors, set up the working directory and local library, and install packages like Excel for importing Excel files using R Studio.

Install the RStudio desktop open-source version and navigate its console, editor, workspace, plots, and package manager. Compare R and SAS in memory, libraries, and real-time analytics for business analytics.

Explore the basics of R, covering variables, data types, operators, vectors and factors, and learn to import and export data, manage working directories, and use RStudio for hands-on practice.

Discover basic R functions and how to use R as an interpreter and calculator, perform square roots, logs, trigonometry with pi, and assign variables to vectors.

Explore data types in R, from vectors to arrays, matrices, lists, and data frames, and see how vector operations power data analysis and text mining with corpus.

Discover how R recycles the shorter vector to match the longer one in arithmetic, with modulo and integer division, and learn subsetting with indices and value updates.

Explore how R handles numerical values like Inf, -Inf, NaN, and NA, and learn to manage them in calculations with na.rm while applying max, min, range, sum, prod, and cumsum.

Explain parallel versions of minimum and maximum across multiple vectors, applying the recycle rule and computing per-index results, including numeric and logical vectors and basic comparison operators.

Explore logical conjunctions and boolean operators in vectors, learn how and, or, not shape outputs, handle NA, and apply subsetting with ifelse; then manipulate character vectors using strings and substring.

Discover how to paste and collapse strings with custom separators in r, and understand vectors by mode, length, and zero-length initialization, including numeric, logical, and complex types.

Learn how R coerces data types—numeric, logical, and character—through implicit rules and explicit casting with as.numeric. See how naming vectors, lists, and null objects affect data structures.

Explore creating arrays and matrices in R, using direct two-dimensional arrays or lists with assigned dimensions, and learn indexing, cbind/rbind, and basic help features.

Explore how to use factors in R to manage categorical variables, inspect levels, and apply tapply to compute group means and standard errors for modeling.

Discover how CRAN repositories host R packages that enable regression, clustering, and visualization, and learn to install and load them with library for analysis.

Install and load packages in R using install.packages, require, and library, then manage your working directory and session data with getwd, setwd, save.image, and load.

Learn to import data into R from CSV and Excel, including comma separators and SAS or SPSS outputs, and validate that the data imported correctly.

Import data into R from csv, semicolon, excel, and SPSS or SAS outputs using packages. Export to csv, tab-delimited, or Excel, and inspect data with head, names, and nrow.

Explore data in R by importing sources, creating derived columns like profit percentage, subsetting data, and merging, binding, and aggregating to a single version of truth.

Learn to perform data aggregation in R with dplyr by merging datasets, creating a school data frame, and computing subject averages, student averages, total marks, and the highest per subject.

Explore data manipulation in R including slicing, sorting, reshaping, and melting, and learn basics of statistics, data exploration, and visualization with bar, pie, box, and scatter plots.

Merge data frames in r by joining on ids to add columns from another dataset, exemplifying inner and outer joins with all is equal to false and true. Reshape2 converts between long and wide formats using id variables and gps to produce pivoted score and t2 columns.

Create an R script, build v1 to v10 and a data frame, then filter by gender, sort using index and order, and demonstrate merging techniques for data manipulation.

Explore merge operations in R by performing inner, left, right, and full outer joins, creating id columns, combining data frames, and reshaping data between wide and long forms using reshape2.

Apply statistics as a method to collect, summarize, and infer data insights for business analytics. Use measures, visualizations, and regression to predict relations in large data with R and Python.

Defines nominal and ordinal as categorical data and interval and ratio as numerical data. Explains frequency and proportion, and covers mean, median, and mode as central-tendency measures and distribution shapes.

Explore how quantiles partition data into equal parts, defining percentile, decile, and quartile, and relate them to the mean, median, variance, and standard deviation.

Calculate variance by averaging squared deviations from the mean, understand standard deviation and the empirical rule, and explore covariance and correlation to quantify relationships between variables.

Learn how covariance relates to correlation, use the Pearson coefficient r between -1 and 1 to describe linear relationships, and explore central moments—variance, skewness, and kurtosis.

Learn to compute cumulative frequency and frequency tables, and visualize data with bar diagrams, pie charts, histograms, and box plots in R for effective business analytics.

Load the mass library in R, explore the painters data with composition, drawing, color, and expression, and compute frequency, relative frequency, and central-tendency measures by category using t apply.

Convert numerical data to categories to generate frequency tables and histograms, then explore relative and cumulative frequencies, line charts, and the stem-and-leaf plot to visualize distribution.

Analyze a scatter plot of duration versus waiting and fit regression line. Explore statistics including mean, median, kurtosis, quantiles, IQR, variance, standard deviation, covariance, and correlation using R and E1071.

Master data analysis in r by computing skewness and kurtosis, interpreting histograms, and building for and while loops with functions to derive basic statistics.

Explore random variables, probability distributions, and both continuous and discrete distributions, including normal and binomial, and learn how expected value guides real-world decisions with hands-on R demonstrations.

explore what a random variable is through coin tosses, dice outcomes, and continuous measures like temperature, highlighting discrete versus continuous types, equiprobable versus non-equiprobable cases, and probability distributions.

Explore probability distributions for discrete and continuous variables, including dice outcomes, pmf and pdf. Learn to compute probabilities via summing pmf and integrating pdf; note total area equals one.

Explore discrete and continuous distributions using pmf and pdf with a fair die, and read the cumulative distribution function to calculate probabilities like P(X ≤ k) and P(X ≥ k).

Explore binomial, Poisson, and normal distributions through practical ship arrivals and retail footfall examples; learn how to compute counts and probabilities in R for business analytics.

Explore continuous probability distributions through density functions and pdfs, including the negative exponential, and use the area under the curve to compute range probabilities, cdf values, and uniform distributions.

Apply the exponential distribution to estimate survival beyond one year for cancer patients, use the CDF to compute probability, and relate it to expected value and variance in normal distributions.

Compute the expected value as the weighted average of a random variable for discrete and continuous cases. Empirical mean is a case; a lottery example shows how it guides decisions.

Analyze expected value and distributions in gambling, using the roulette example to show how the house earns a 5% margin, while highlighting constants, the additive rule, and variance.

Explore how expected value, variance, and standard deviation guide decisions under risk using deal or no deal scenarios. Learn covariance and independence, with preview of binomial, Poisson, and normal distributions.

Explore the binomial, Poisson, and normal distributions, grounded in Bernoulli trials, and compute binomial probabilities with heads counts in five tosses using combinatorics like 5Ck.

Explore the binomial distribution with n independent trials, a constant probability p, and k successes, using the formula C(n,k) p^k (1-p)^{n-k}.

Learn how to compute the binomial expected value as n p, and how the Poisson distribution emerges (mu = n p) for large n, with practical examples.

Explore the uniform distribution on [0,1] with its equal-probability subintervals, and learn how to compute the expected value, variance, and the basics of the normal distribution.

Explore binomial distribution with a twelve-question test and five choices, calculating exactly four correct and at least four using probability mass and cumulative distributions; also introduce Poisson for bridge traffic.

Explore probability distributions in R, including normal, exponential, and uniform; compute area under the curve and lower and upper tail probabilities using p norm, d norm, and related options.

Explore the normal distribution and standard normal, connect to sampling and central limit theorem, and introduce estimation and hypothesis testing with z and t statistics in R.

Explain the normal distribution, defined by mean and standard deviation (variance is sigma squared), with area under the curve equal to one, and distinguish normal from non-normal data.

Explore the normal distribution, mu and sigma, and the area under the curve through the empirical rule, highlighting 68%, 95%, and 99.7% coverage.

Analyze how SAT scores follow a normal distribution with mean 500 and standard deviation 50, giving 68% within 450–550 and 95% within 400–600, and introduce z-scores.

Apply normal distribution to birth weights, with mean 109 and standard deviation 13, calculating the chances of 141 or heavier and 121 or lighter using z-scores and R.

Learn to compute normal distribution probabilities in R with d norm, p norm, and q norm, using z statistics to find areas under the curve and relate to inferential statistics.

Explore how estimation uses a sample to infer the population parameter. Use the sample mean as the point estimate and confidence intervals for interval estimation, with hypothesis testing for significance.

Explore properties of good estimators, including unbiasedness and efficiency, and learn to construct confidence intervals for means and proportions using standard error and margin of error.

Explore how the central limit theorem turns any distribution into a normal curve by averaging samples, and see larger sample sizes produce a closer normal with the same mean.

explain kurtosis and skewness in small samples, and how the central limit theorem yields normality by averaging; compute mean, standard error of the mean, and related z-score probabilities.

Explore how the central limit theorem connects sample means to population parameters, and construct confidence intervals using the sampling distribution of the mean and standard error.

Build the confidence interval for the mean from a roughly normal distribution, using mu ± 1.96 sigma for 95% confidence. Apply step-by-step calculations with alpha, z scores, and standard error.

Demonstrates building confidence intervals with z and t transformations, using a 36-sample example to estimate a population mean and compute a 100–124 g interval, yielding 0.92 probability.

From a 250-student survey, compute the proportion who think the computer lab is important and form a 95% confidence interval around 0.56. The example shows larger samples narrow this interval.

Explore the t-distribution for small samples and unknown population sd, which differs from the z distribution with heavier tails (leptokurtic) and underpins the student t-test and confidence intervals.

Learn to compute a 99% confidence interval for a mean using the t-distribution with seven samples, estimating mu and sigma, and applying R functions for critical values and interval construction.

apply the standard normal (z) distribution and confidence intervals in R, using the mass package, with a survey dataset to demonstrate sampling, the central limit theorem, and point estimation.

Learn to compute the standard error of the mean and build a 95% confidence interval using the z value from qnorm in R, relating x̄ and sigma in interval estimation.

Learn how to download and load an R package, compare z and t distributions for confidence intervals, and determine required sample size for a 95% margin of error.

Calculate the required sample size to achieve a 1.2 margin of error at 95% confidence using standard deviation, standard error, and z values around 1.96 for proportion estimates.

Generate and validate business hypotheses using statistical testing, from defining null and alternative hypotheses and sampling concepts to computing test statistics and p-values via z-tests or t-tests.

Frame null and alternative hypotheses, distinguish type I and II errors, and use z or t statistics and p-values to assess drug efficacy in one or two-sided tests.

Interpret p values to determine whether to reject the null hypothesis, using z and t statistics, one-sided and two-sided tests, and alpha thresholds.

balance type one and type two errors to achieve power in hypothesis testing, using power formula 1 minus beta for z tests and sample size calculations with mu_naught, mu, sigma.

Demonstrate hypothesis testing with R through problem-based examples, calculating z statistics, critical values, and p-values at alpha 0.05. Learn to reject or not reject null hypotheses in practical scenarios.

Calculate z and t statistics for hypothesis tests, compare to 0.05 significance with two-tailed and one-tailed approaches, and interpret p-values using penguin, bulb, and cookie examples.

Explore hypothesis testing for population proportions in R, using z tests and prop.test to interpret p-values with real examples like female proportion, rotten apples, and coin toss.

Explore time series forecasting with R, covering simple and weighted averages, qualitative methods, advanced techniques like univariate exponential smoothing, ARIMA, and GARCH, and time series fundamentals.

Explore time series forecasting from data warehouse to data mart, including data preparation and parameter settings. Validate forecasts with training and testing data, compare models, and visualize results in R.

Explore self projecting and cause-and-effect forecasting, comparing univariate, multivariate, and qualitative methods, from moving averages and exponential smoothing to ARIMA and regression-based models.

Identify level, trend, and seasonality as the systematic components of a time series, separating them from the random part. Use R to decompose, visualize, and apply ARIMA for forecasting.

Explore traditional time series methods, including holt's, winters, and arima, with a focus on smoothing, exponential smoothing, moving averages, trend, seasonality, and their identification and validation drawbacks.

Explore the ARIMA steps, define p, d, and q, and apply differencing to remove trend and seasonality. Follow the Box-Jenkins cycle—identification, estimation, diagnostic checking, and forecasting.

Assess forecast performance using mean forecast error, mean absolute deviation, and mean absolute percentage error to gauge model accuracy; explore ARIMA, auto ARIMA, moving average, Holt's, and Winters.

Explore univariate ARIMA basics, including lag, differencing for stationarity, and ACF and PACF to determine p, d, and q, with notes on unit roots and GARCH for volatility.

Explore R visualization techniques for data exploration, from bar charts and heat maps to correlation maps and radar charts, using ggplot2, 3D plots, and maps.

Visualization reveals patterns missed by summaries, enabling fast data comparison. Learn to use bar plots, pie charts, and line graphs in R to compare distributions and track time series.

Overlay Dell and Intel stock data on a single plot using lines, differentiate with line types and colors, and add a legend.

Explore histograms, density plots, frequency polygons, and box plots in R to visualize data distributions, compare shapes, and interpret skewness, mean, and standard deviation.

Box plots reveal outliers and differences between distributions, while histograms show details. Scatter plots display relationships between two variables and can use a kernel density estimate for smoothed density visualization.

Explore advanced graphing in R using ggplot2 and qplot to create univariate, bivariate, and multivariate plots with aesthetics, facets, and layered geoms; learn tk plots and igraph network diagrams.

Explore bubble charts to compare three variables at once by plotting x, y, and bubble size, illustrated with sales, expenditure, and profit across states, plus 3D and heat map visuals.

Explore analysis of variance (ANOVA) in R, comparing mean weights or healing times across multiple groups, assessing between-group and within-group variance, and interpreting the F-statistic and p-value.

Explore partitioning variance into betweenness and within ness to estimate the overall effect, compute means and sums of squares, and interpret the F statistic and p-values in ANOVA with R.

Learn how Tukey's method supplements ANOVA for multiple comparisons, evaluating group means (A, B, C) with p-values and confidence intervals to decide which differences are significant.

Learn to perform factorial anova in R by modeling product as a function of variety and pesticide, interpret the AOV table, and use two k hsd for multiple comparisons.

Learn regression basics and the mechanics of linear regression in R, including statistical significance, linking house price to size, area, and type, and estimating intercept and slope for prediction.

Explore simple and multiple regression using least squares to predict house prices from size, with parameter estimation, residuals, and hypothesis testing. Estimate beta naught, beta one, and evaluate with p-values.

Explore how to assess a linear regression model's strength by examining SST, SSR, and SSE, and use R-squared and adjusted R-squared to gauge explanatory power while guarding against inflated values.

Explore how regression models assess relationships between variables using ANOVA, F statistics, and p values, interpret R-squared and adjusted R-squared, and build predictive equations with confidence intervals.