Business Analytics Complete Course

Explore how statistics turn numbers into actionable insights using social media metrics and internet usage to guide business decisions and distinguish statistics from analytics.

Distinguish population from sample and see how sampling lowers cost; learn descriptive and inferential statistics and the four variable types: numeric continuous, numeric discrete, categorical nominal, and categorical ordinal.

Explore central tendency and dispersion as descriptive statistics, covering mean, median, mode, range, standard deviation, and weighted means, with outlier impact and examples.

Explore percentile concepts and dispersion in business analytics by comparing medians, means, variance, and standard deviation, and learn how z-scores standardize performance across tests.

Practice computing mean, median, standard deviation, and standard score in Excel, using average and median functions, and interpreting how far observations lie from the mean in standard deviations.

Explore normal, skewed, and uniform distributions, learn to read data shapes, and identify mean, median, and mode while understanding standard deviation and the 68%, 95%, and 99.7% rules.

Organize and visualize data with Excel charts and pivots to convey trends clearly, using line, column, bar, pie, and scatter charts to convert numbers into actionable insights.

Explore how bubble charts add a third dimension via bubble size for market share. Observe how the x-axis shows number of products and the y-axis shows sales.

Practice central tendency and decision making alongside charts and visualization using Excel; complete hands-on assignments with mean, median, standard deviation, outliers, scatterplots, and line and pie charts.

Master pivot tables in Excel to summarize large data and extract insights. Using a fruit and vegetable export example, compute totals, shares, and identify top exporters like bananas.

Use Excel pivots to analyze exports by country and product, identify top markets, and propose growth via broader product ranges, new markets, and value-added exports.

Analyze company sales data with pivot tables in Microsoft Excel to draw insights from monthly sales by region, product, and salesman, including net sales and profit.

Interpret the correlation coefficient and r-squared via covariance and variance, assess linear relationships with scatterplots and trend lines, and beware outliers and sample size; remember correlation does not imply causation.

Master correlation by examining the linear relationship between two variables using scatter plots, recognizing positive and negative trends, and interpreting the correlation coefficient to gauge strength.

Master regression concepts, including independent and dependent variables, the regression equation, correlation, slope, intercept, and predicting Y from X.

This lecture explains how total variance divides into explained and unexplained parts, defines R-squared, and shows interpretation from a regression line while noting linearity, outliers, and homoscedasticity.

Explore two assignments: analyze monthly IBM, Intel, and Microsoft prices and volumes to answer five questions on correlation and integration; and predict SAT scores using MOX-based science and math data.

Explore hypothesis testing by revisiting the normal distribution and z-scores, and learn to compare means using standard deviation as a measure of dispersion.

Explore how to formulate and test hypotheses, distinguishing null from alternative hypotheses, and interpret evidence through examples like the judiciary's innocent until proven guilty principle, dropouts, and drug trials.

Present hypothesis testing concepts, including null and alternative hypotheses, alpha and type I and type II errors, one- and two-tailed tests, and a TV sets example with critical values.

Explain how multiple linear regression uses several independent variables to predict a dependent variable, interpret r-squared and f-statistics, and perform regression in Excel.

Learn to interpret p-values and t-statistics, assess null and alternative hypotheses, and evaluate R-squared and F-statistics in single and multiple regression to identify significant predictors.

Explore multicollinearity in multiple regression by examining the correlation among independent variables, R-squared, P-value, the correlation matrix, and the variance inflation factor.

Build a multiple linear regression model to predict gasoline consumption across states using tax, income, family size, road coverage, and population share as predictors.

Discover how logistic regression predicts binary outcomes by modeling log odds with the logistic function, unlike linear regression's continuous targets. Apply to yes/no decisions like purchase or default.

Explore logistic regression for binary outcomes in SAS, using proc logistic with descending, encoding gender, interpreting coefficients and p-values, and assessing model with concordance and chi-square.

Explore how segmentation divides the population into subgroups to tailor products, pricing, and marketing, as Bank B demonstrates risk-based differential pricing across demographic, geographic, behavioral, and psychographic dimensions.

Compare subjective and objective segmentation, illustrated by a video rental case. Learn how cluster analysis uses Euclidean distance to form within-cluster similarity and between-cluster dissimilarity, including k-means and hierarchical methods.

Explore segmentation techniques using cluster analysis, including choosing the number of clusters, standardizing data, and applying k-means and hierarchical methods, with validation and case-driven variable selection.

Learn time series analysis and forecasting by examining trend, seasonality, cyclicality, and irregular variations to predict monthly sales and guide production planning.

Compare forecasting methods—simple moving average, weighted moving average, and single or double exponential smoothing—using mean absolute deviation and mean squared error to select the best method.

Explore moving averages and exponential smoothing for forecasting, noting moving averages' limits with sharp trends and how single exponential smoothing uses past data and alpha to forecast.

Explore double exponential smoothing with alpha and beta to model level and trend for forecasts, then extend to triple exponential smoothing with gamma for seasonality.

Master double and triple exponential smoothing to forecast seasonal time series, identify seasonality, compute seasonal indices, and integrate trend with base level for accurate forecasts.

Explore assignment solutions on central tendency and dispersion, compute means and medians for English and science scores, analyze standard deviations and standard scores, identify outliers with scatterplots, and compare performance.

Learn to select and create Excel charts for wardship party data, forex trends, and quarterly sales, using line, pie, bubble, and stacked column charts, plus outlier treatment.

Use pivot tables to compute net sales, profit, and margin by region and product; identify top salespeople, highest regions, and assess averages, proportions, and seasonality across time.

Analyze correlation concepts in business analytics using IBM, Intel, and Microsoft stock data. Examine scatterplots and trends, noting positive correlations and that correlation does not imply causation.

Perform correlation analysis on five variables to predict gasoline consumption, identify gasoline tax and license-related measures as the strongest predictors, and build a two-variable regression model with significant p-values.