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Week 2 8-29 Analytics introduction

Week 2 8/29/19 Operations Management
What is Business Analytics
Many terms are often used interchangeably
IE Big Data, Data Science, Machine Learning, Artificial Intelligence, Deep learning
Draws upon multiple disciplines – Business, data management, statistics, math, programming
The process of Business Analytics
Business Understanding –
Data Understanding –
Data preparation
Core Ideas
Data Exploration
Association Rules and Recommender Systems
Data Reduction
Business Intelligence vs Business Analytics
BI refers to data visualization and reporting for understanding “what happened and what is
BA now typically includes BI as well as sophisticated data analysis methods, such as statistical
models and data mining algorithms used for exploring data, quantifying and explaining
relationships between measurements, and predicting new records.
Examples of BA
Credit Scoring – long established use of predictive modeling techniques for business prediction
is credit scoring.
Future Purchases –
The business Analytics toolkit also includes statistical experiments the common of which is
known to marketers as A-B testing. These are often used for pricing decisions
Orbitz, found that it could price hotel options higher for mac user than Windows users.
Steps in BA Process
Data Preparation, Exploration, and Data Reduction – Visualize the data, make sure data is correct, take
care of outliers, remove dummy variables ect… step 1
Prediction, Classification, Time Series Forecasting, what goes together, Segmentation - Machine learning
also called modeling, step 2
Model Evaluation and Selection – run algorithm to determine who is providing the correct result step 3
Model Deployment – send results to the real world. Step 4
Data Exploration
Data sets are large, complex and messy. Review data to help refine task, employ reduction and
Visualization techniques
Data Visualization
Helps to clean the data, Interactive graphs and plots of data
Supervised learning
The process of providing and algorithm with records in which an output variable of interest is
known and the algorithm “learns” how to predict this value with new records where the output
in unknown.
Prediction and Classification steps
Unsupervised learning
An analysis in which one attempts to learn patterns in the data other than predicting an output
value of interest.
Deep learning - learning from the present
What Goes together and Segmentation Steps, no target variable
Robot takes decision based on what occurred or occurs
Association Rules and Collaborative Filtering
Exercised by Netflix, if X was purchase, Y was also purchased, this produces results that define
“what goes with what”, unsupervised learning
Data Reduction and Dimension Reduction
Taking complex/large data into simpler/smaller data is called data reduction
Reducing the number of variables/columns is called Dimension Reduction
Reducing the number of records/rows
The forging steps encompass the steps, a methodology developed
By the software company SAS:, Sample Take a sample from the dataset, partition into training,
validation and test datasets.
Classification and Prediction
Classification comes into picure when you are trying to identify the customer will buy product or
not. Target variable.