# Practitioners Course In Descriptive, Predictive And Prescriptive Analytics

IIT Kanpur, , Prof. Dr. Amandeep SinghDr. Deepu Philip

Updated On 02 Feb, 19

IIT Kanpur, , Prof. Dr. Amandeep SinghDr. Deepu Philip

Updated On 02 Feb, 19

Data analytics is a demanding field and industry is looking for potential employees who are having a practitioners approach to data analytics. This course is aimed at providing exposure to various tools and techniques along with relevant exposure to appropriate problems so that the know-how and do-how aspect of analytics, which is required by industry can be fulfilled. The course also aims at introducing various applications with the involvement of real-life practitioners so that appropriate exposure to audience who intend to build a career in this area is possible.

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4.1 ( 11 )

- Introduction to analytics
- 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, Wilcoxon signed-rank test, Kruskal-Wallis test, A/B testing
- Data infrastructure, analytics and BI, 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, spearmans rank correlation, Linear regression, logistic regression, robust regression
- Hierarchical clustering (Euclidean & Manhattan), k-means clustering,Nearest neighbor, decision trees
- Basics, customer lifetime value, customer probability model,Net promoter score, survival analysis
- Product lifecycle analysis, Ansoffs matrix, competitive map, Fundamentals of simulation, simulation types, Monte-Carlo simulation

Sam

Sep 12, 2018

Excellent course helped me understand topic that i couldn't while attendinfg my college.

Dembe

March 29, 2019

Great course. Thank you very much.