This document provides an overview of analyzing data using open source tools and techniques to cut costs and improve metrics. It demonstrates tools like R, Python, and Spark that can be used for tasks like data exploration, predictive modeling, and clustering. Common techniques are discussed like examining median, mode, and standard deviation instead of just means. The document also gives examples of use cases like churn prediction, conversion propensity, and web/social network analytics. It concludes by encouraging the systematic collection and use of data to make decisions and that visualizing data through graphs is very helpful.

1. •Analyze this!

2. What this is?
● Expose you to use your OWN data better
● Cut your costs and improve metrics
– Tools (open source and free)
– Technqiues (easy to learn and deploy)
– Use Cases ( depends on YOU)
● Target Audience

3. What this is not?
● Advanced Analytics class
● Shameless self promotion
● Data Science career advice

4. Tools Demo
● R – GUI – Rattle R Commander
● Python – Ipython
● Spark – PySpark -- SparkR

8. Techniques Demo
● Means can be deceptive
– Medians and Modes
– Standard Deviations
– Plotting - Anscombe

11. Techniques Demo
● Decision Trees
– Segment
● Cluster Analysis
– Data Reduction

15. Use Cases
● Churn- who is leaving us
● Propensity -who is going to convert
● Web Analytics -Oye !
● Social Network – What does SNA do
● LTV ( LIfe Time Value)
● RFM ( Recency Frequency Monetization)

16. Some free advice
● Dont throw the baby out with the data
– Collect Data Systematically Please
● Data driven decisionmaking will beat your gut
● Statistics will beat your gut
● A picture is worth a thousand excel sheets
● Social media helps in LONG TERM

17. Thanks
● This is a monthly knowledge sharing free
volunteer initiative
● Guruji will accept free beer !