Analytics & Data Science for managers & humanitarians

Identify three main scales of data—nominal, ordinal, and interval—and explore how each scale classifies objects, measures informativity, and enables different analysis methods.

Learn how hypotheses function across fields—from marketing campaigns to investments and investigations—testing assumptions with data, accepting or rejecting them, and using probability to assess relationships.

An introduction to probability, p-values, and hypothesis testing, framing the null and alternative hypotheses, significance level alpha, and the decision rules for p<0.05 and p>0.05.

Explore probability concepts, null and alternative hypotheses, and how error probability determines statistical significance. Learn to evaluate whether sample findings generalize to the population using a 5% threshold.

Use descriptive statistics to summarize data and analytical statistics reveal regularities for prediction. Compare samples to populations, track variables on nominal, ordinal, and interval scales, and test hypotheses with p<0.05.

PSPP offers a free alternative to SPSS for analysis, with no expiration or artificial limits, supports importing data from spreadsheets, text files, and databases, and highlights cloud-free options like jasp-stats.org.

Descriptive statistics compress large data into a single representative value using central tendency and variability measures such as mean, median, mode, min, max, range, skewness, kurtosis, and standard deviation.

Learn how frequency distributions describe counts and percentages, visualize them with pivot charts and histograms, and compute frequencies in Excel using recoding and the frequency function.

Explore how analytical methods use probabilities, significance of differences, and relationships among multiple variables to classify cases and make predictions in socio-economical reality.

Explore descriptive statistics by variable and simultaneous analysis to reduce dimensions and classify objects. Build predictive models to test differences between groups and forecast future outcomes.

Identify statistically significant differences between groups and assess the probability that findings are due to chance in the sample or general population.

Differentiate dependent (paired) samples from independent samples using before-and-after measurements and group comparisons. Apply these concepts to training data to assess significant improvements in metrics across groups.

Compare groups to understand differences and their significance, using independent and paired samples, parametric and non-parametric criteria, with tests like t-Student, Wilcoxson, median and sign tests, significance less than 0.05.

Explore regression and correlation as core tools of predictive analytics, using linear regression to predict loyalty from service quality and assortment while assessing model quality with R square and significance.

Cluster analysis groups objects with similar variable values into clusters, using nearest-neighbor merges and a chosen or auto-determined number of clusters, demonstrated with a PSPP k-means shopping data example.

Explore how video, audio, text and other data types drive real-time analytics. Learn how online storage, powerful computing, and high-level languages empower regression, correlations, forecasting, and classification.

Explore how computing power, data warehouses, and networked devices enable big data, including unstructured data, and how analytics relates to this evolving data landscape.

Explore how big data and ensembles of analytical algorithms yield artificial intelligence, clarify its real-world nature, and connect machine learning with analytics and human-machine interaction.