We at Analytics University have created study packs to help students and working professionals build expertise in various fields of data analytics

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Study Packs

  /  Study Packs

Introduction to Analytics using SAS

  • Introduction to Statistics
  •  Exploratory Data Analysis
  •  Simple Data Visualization
  •  Regression Analysis
  • Logistic regression
  • Discriminant Analysis
  •  Survival Analysis
  •  Simulations
  •  Optimizations
  •  Programming with SAS/SQL
  •  Model building Case studies with SAS

2. Introduction to Analytics using R

  • Introduction to Statistics
  • Hypothesis testing
  • Data Exploration in R
  • Regression Analysis in R
  • Logistic regression
  • Cluster Analysis
  • Model building using R
  • Association Rule Mining
  • Decision Tree
  • LDA/SVM/Random Forest Model
  • Dimension Reduction – PCA

3. Credit Scorecard Development

  • Introduction to Credit Risk
  • Introduction to Credit Scorecards
  • Scorecard Project Planning
  • Development of Application/Behavioural Scorecards
  • Reject Inference
  • Model Validation
  • Model monitoring
  • Model implementation

4.Credit Risk Model Validation & Portfolio monitoring

  • Model Validation best practices
  • Model validation metrics used in BFSI
  • Portfolio monitoring using risk baselines
  • Validating PD, LGD & EAD Model
  • Implementing Models in production

5. Marketing Analytics

  • Direct Marketing Models
  • Customer Lifetime Value models
  • Loyalty models
  • Cross sell/upsell modelling
  • Market Mix modelling
  • Segmentation Modelling

6.Pricing Analytics

  • Optimal pricing of products
  • Enterprise level price optimisations
  • Revenue models
  • Introduction to SAS OR

7. Operation & SCM Analytics

  • Case studies on Shipment
  • Inventory Forecasting
  • order Management & CRM

8. HR Analytics

  • Case studies in Hiring, Retention, Performance Evaluation models

9.Time Series Forecasting

  • Introduction to time series data, data visualizations, theory of AR, MA, ARMA and ARIMA Models, Modelling ARIMA using R/SAS, Variance Forecasting (ARCH, GARCH Models using R, ECM Model