2.business analytics development
three components
(1) a methodology to guide the analytics process
(2) the data scientists who build models
(3) a set of tools and techniques

environment
DataRobot (an automated machine learning platform), the programming language R, and SAS Visual Analytics (SAS VA)

LO
Describe the steps in the process of analytics development
Describe the leading analytics methodologies
Identify common data science techniques used in business
Create a skills profile for an analytics team
Explain how A/B testing can be used to validate actions taken on the basis of analytics
Establish and implement a decision framework for analytics toolset selection.

the analytics process
1.Define the business objectives
2.Collect data
3.Prepare and explore data
4.Create training and test datasets
test: verify the accuracy of the model’s output
training: model building

5.Build and improve the model
6.Deploy the model

analytics methodologies
provides a framework that is used to structure, plan, and control the process of developing an analytics solution

Evidence:A/B testing

modelling techniques
-supervised and unsupervised learning
-regression and classification models
-deep learning

Model-building techniques
-Technique
-Definition and usage
-Unsupervised learning
- - k-means clustering
- - PCA
-Supervised learning
- - linear regression
- - logistic regression
- - artificial neural networks(ANNs) and deep learning
- - support vector machines (SVMs)
- - Classification and regression trees (CARTs)
- - Gradient boosting
- - Naive Bayes
- - Bayesian networks
- - k-nearest neighbours (kNN)
- - Association rules
- - Genetic algorithms
- - Time-series analysis
- - Ensemble models
-Text analysis
-Other

The data scientist
needs programming skills, mathematics and statistics expertise, and domain knowledge to be effective
Is skeptical, curious. Has inquisitive mind. Knows machine learning, statistics, probability.

analytics toolsets

Automated machine learning