Differentiating descriptive, predictive, and prescriptive analytics, data mining vs data analytics
Industrial problem solving process
Decision needs and analytics, stakeholders and analytics, SWOT analysis
Model and modeling process, modeling pitfalls, good modelers, decision models and business expectations,
Different types of models – overview of context diagrams, mathematical models, network models, control systems models, workflow models, capability models
Data and its types, phases of data analysis
Hypothesis and data Scales, relations, similarity and dissimilarity measures
Sampling process
Types of sampling, sampling strategies, error mitigation
Visualization of numeric data
Visualization of non-numeric data
Tools available for visualizations Hypothesis testing, pairwise comparisons, t-test, ANOVA
Data sources, data warehouse, data stewardship, meta data management Data and forecasting, super-forecasting, S-curve (lifecycle), moving average, exponential smoothing, error in forecasting Linear correlation, correlation and causality
Spearman’s rank correlation, Linear regression, logistic regression, robust regression