Data Science in Practice

At the end of Data Science in Practice training course, participants will be able to:

• Summarize the distinguishing characteristics of each Pivotal product and tool, and be able to describe the most beneficial naspects from a Data Science perspective;
• Evaluate and demonstrate hands-on practical skills with each nproduct and tool;
• Investigate, assess, and apply their knowledge to practical data nscience problems;
• Practice Data Science problem solving techniques to their nrespective endeavors.

As a result of attending the course, the Data Scientist will be able nto confidently utilize the Pivotal product set and related technologies to analyze large data sets.

• Willingness to participate in a demanding, high-intensity training experience.
• Comfort with data analytic technologies a plus n(Statistics, mathematics, machine learning, SQL, R, Python)
• Have a basic understanding of virtualization and nmassive parallel processing concepts.

• Experienced data analysts and data engineers willing nto work hard to achieve superior Pivotal Data Science nskills.
• Anyone else who wants to learn about data science nusing the Pivotal product stack.

1. Introduction

2. Data science overview
• Data Science: The Big Picture
• Driving Forces
• What Does a Data Scientist Do
• The Process of Data Science
• What Does Pivotal bring to the Story
3. Pivotal overview
• Pivotal Corporate Overview
• The Pivotal Big Data Suite - Pivotal Greenplum DB - tPivotal GPText - tMADlib - tPivotal HD - tPivotal on Virtualized Hardware - tPivotal HAWQ - tPivotal eXtension Framework (PXF) - tPivotal Analytics Workbench - tPivotal GemFire - tPivotal GemFireXD - tSpring by Pivotal - tSpring XD - tPivotal Labs and Pivotal Data Labs –
4. Pivotal greenplum DB review including inline labs
• Essentials
• Getting Started and Inline Lab Exercise
• Intro to pSQL and Inline Lab Exercises - Creating Tables - tDistributions and Partitioning - tIndexes - tExternal Tables and Loading Data -
• Unloading Data
• Analyze
• Explain and Analyze
• Vacuum
• Monitoring
5. Advanced SQL
• Explore and Inline Lab Exercise
• Joins and Inline Lab Exercise
• Arrays and Array Aggregates and Inline Lab Exercise
• Window Functions and Inline Lab Exercise
• Other Functions and Inline Lab Exercise
• User Defined Functions (UDF's)
• User Defined Aggregates (UDA's)
• Data Science Exercise
6. MADLIB including inline labs
• MADlib Basics
• Advanced MADlib
• Data Science Exercise
7. TEXT including inline labs
• NLP: Practical Examples
• NLP: Practical Examples with NLTK
• Putting it all together
• Data Science Exercise
Apache hadoop and the hadoop ecosystem including inline labs
• Apache Hadoop Overview
• - Core Component: HDFS
• - Core Component: MapReduce
• - Map Reduce: Writing a Job
• Hadoop Ecosystem
• - Hadoop Streaming
• - Pig
Pivotal HD and HAWQ including inline labs
• Intro to Pivotal HD and HAWQ
• Getting Started with HAWQ
• Working with HAWQ
• External Tables: file, gpfdist, web
• External Tables: PXF
• Loading and Unloading Data and Inline Lab Exercises
• Loading and Unloading using Copy
• Loading and Unloading using Insert
• Loading and Unloading using gpfdist / gpload / external tables
• Data Science Exercise
Gemfire (optional)
• Gemfire
R and python
• PivotalR
• PL/R
• pyMADlib
• PL/Python
• Data Science Exercise
Visualization
• Tableau
• R
• Python
• Exercises
HAWQ text analytics exercise airline price optimization exercise gene sequencing
• HAWQ Text Analytics Exercise
• Airline Price Optimization Exercise
• Gene Sequencing Exercise