Introduction to Data Science

General

• What is Big-Data and why is it good

• Big-Data Characteristics & types

• Challenges and complexity

• Use cases in today's world

Methodology

• Defining the business problem or opportunity

• Defining the business objective

• Designing requirements

• Understanding the relationship between causes and consequences

• Setting up the environment and exploring the data

• Supervised vs. unsupervised vs. reinforcement learning

• Supervised learning: classification vs. regression

• Overview of model building steps: Data preparation, Model building,Model validation, Model assessment, Model implementation

Supervised learned model R-regression

• Targeting and scoring models

• What you need to develop a scoring model

• Checking model assumptions: Linearity, Normality, Equal variance

• Definition of predicted variables

• Calibration data and statistical model

• Building a predictive model in R–Concept on the basis of Churn modelfor cell-phone users.

• Overview of model building steps

Classification models

• Explaining the cluster analyses

• Visualizing the model output

• Evaluating the models

• Statistical segmentation

• Segment Strategies

• Selecting the "right" number of segments

• Segmentation variables

• Archetypical profiles

• Running a hierarchical segmentation in R

Unsupervised Learning

• Unsupervised Learning and Principal Components Analysis

• Exploring Principal Components Analysis and Proportion of VarianceExplained

• K-means Clustering

• Hierarchical Clustering

• Examples of case studies