HR Analytics using MS Excel (Excel Analytics)

Learn to enable and use the data analysis toolpak in Excel to perform basic analytics for business problems. Explore the available tools and when to apply them.

Identify training priorities by applying rank and percentile in Excel to evaluate employee performance changes, using descriptive statistics to summarize data and target those below productivity benchmarks.

Explore descriptive statistics in Excel to analyze promotion data, calculating mean, median, mode, range, variance, standard deviation, quartiles, skewness, and histogram interpretations for HR analytics.

Learn to build histograms in Excel to inspect the distribution of time since last promoted and time in current role, determine min and max, and identify outliers.

Explore inferential statistics and hypothesis testing with real business cases to estimate population parameters from samples, form null and alternate hypotheses, and apply chi-square, z, t, and anova tests.

Examine how covariance and correlation uncover relationships between salary, promotions, and manager tenure, with Excel-based computation to assess compensation bias.

Use a paired t-test in Excel to evaluate training impact by comparing productivity before and after training, a dependent-sample analysis with hypotheses and p-values indicating significance.

Evaluate training effectiveness by comparing old and new training using an unequal-variance t-test, analyzing before and after productivity data in Excel to determine significance.

Apply a single-factor ANOVA in Excel to compare promotion durations across three groups (A, B, C), test equal means, and interpret the non-significant p-value 0.79.

Explore a two-factor anova with replication to evaluate department and month effects on productivity, test interaction, and interpret p-values to assess promotion policy concerns.

Apply ANOVA two-factor without replication in Excel to assess whether sales differ by employee and by month, using the data analysis toolpak and p-values at 0.05.

Compare two college-hire groups' productivity using a two-sample z-test in Excel, testing the null hypothesis of equal means and identifying significant differences in sales productivity.

Learn predictive statistics, or predictive analytics, using historical data and methods like linear and logistic regression and time series to forecast outcomes with Excel's data analysis tool pack.

Learn how linear regression uses scatter plots to reveal relationships between variables. Apply simple and multiple regression in Excel with ordinary least squares and explore r squared.

Explore factors impacting productivity and apply linear regression in Excel to identify drivers of low performance, using a provided dataset and step-by-step model building.

Explore how to use excel regression to link productivity with four factors—excess, salaries, incentives, and courses. Interpret p-values and r-squared to judge model strength and validity.

Explore forecasting sales and staffing with moving average and exponential smoothing in Excel using five-year data. Compare models with MAE and RMSE to pick the best.

Explore how to create moving averages in Excel using the data analysis tab to forecast sales, compare intervals of 3, 4, and 5, and assess accuracy with rmse and mape.

Explore exponential smoothing in Excel, select alpha values between 0 and 1, apply damping, compare forecasts to actual sales, and predict next month's demand and workforce needs.

Conclude the course by tracing the analytics journey from descriptive to predictive analytics, and highlight hands-on learning with Excel, R, and Python through HR cases.

Master count and frequency in Excel for HR analytics, using count, count if, count blank, and frequency with array formulas to analyze departments, salaries, and missing values.

Explore how to use max, min, large, and small functions in Excel to find the largest and smallest values, including the kth largest or smallest, across data ranges.

Learn to compute and compare averages, median, and mode for salary data, and apply geometric, harmonic, and trimmed means in Excel to handle outliers and analyze growth rates.

Explore confidence intervals with a practical Excel approach, using a sample to estimate the population mean within a 95 percent confidence interval.

Explore how to use percentiles, quartiles, and rank in Excel to analyze data distribution, identify the median and key percentile values, and rank sales figures for employees.

Learn how to compute deviation and variance from data, including population vs. sample measures. Apply standard deviation and sum of squares using Excel formulas for clear, actionable hr analytics.

Learn to compute covariance between salary hike and manager satisfaction, compare sample and population estimates, and interpret near-zero versus stronger correlations using Excel.

Explore trend line functions in Excel to perform linear regression, calculating intercept and slope, and using forecast and trend to predict sales from input variables.