Upon completion of this course, you will be able to:

• Incorporate text analytics over corresponding areas of commercial and retail production.

• Enhance the efficiency of customer behavioural modeling.

• Understand Text Analytics and Data Science.

• Grow the value-add of operations for higher management.

The following items describe the outline of the course:

Day 1:

The Business Case for Data Analytics

• Predicting customer behavior with greater accuracy and precision

• Cost improvements by avoiding manual data processing

• Innovation improvements

• Analytics Maturity Curve

Data Analytics

• Introduction to Data Analysis

• Data Processing

• Data Quality

• Working with SQL vs. NoSQL

Case Study

• Product recommendation and churn modeling cases will be discussed

Hands-on Excercise

• Participants will be asked to assess their Analytics Maturity

• Participants will be asked to define their problem statements for their business segments

• Participants will be asked to discuss organizational and cultural limitations within their companies to broad analytics adoption

Day 2:

Exploratory Data Analysis

• Distributions – Binomial, Power Law (Benford’s Law and Zipf’s Law), Normal, Etc.

• Descriptive statistics

• Fitting linear models with lm

• Fitting linear models with multiple predictors

• Overfitting

Predictive Analytics with Python & R

• What is a prediction?

• Sampling, training, and test sets

• Constructing a decision tree for Purchasing Decision

• Collaborative Filtering for Recommendation Analysis

• Association Rule Mining for Recommendation Analysis

• Logistic Regression for Churn modeling

• Neural Network for Churn modeling

Customer Segmentation with with Python and R

• Clustering using k-means and other techniques

• Classification using naïve Bayesian and random Forest

• Identifying Potential Customers

Hands-on Exercise

• Participants will be asked to perform customer segmentation on a customer data set

• Participants will be asked to create a recommender on a customer/product data set

Day 3:

Data Visualization with Tableau  

• Presenting Data

• Creating Dashboards and Storyboards

• Identifying measures

Setting up a Data Architecture

• Data Ingestion tools – Nifi, Kylo

• Data Lakes – Hadoop & S3

• Data Transformation and ELT

• Data Storage (SQL vs. NoSQL)

• Message Storage (Redis Pub-Sub)

• Streaming Data Storage and Processing (Spark)

• Search Database (ElasticSearch)

• Graph Database (Neo4J)

Hands-in Exercise

• Participants will be asked to create a simple dashboard in Tableau

• Participants will be asked to identify roles and responsibilities that are either lacking or not adequately skilled for Data Analytics