The document describes a corporate training program in data science offered by Hudson Data Corp. It includes an overview of the types of algorithms and skills covered in the training, such as text mining, recommendation engines, and clustering. It then describes the training process, which involves understanding business needs, developing use cases, measuring impact, and providing pre- and post-training support. Sample agendas are provided for 3-day and 5-day corporate training programs that include lectures, case studies, and assignments applying different algorithms and tools like decision trees, random forests, and Hadoop.

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Algorithms
The Brains
– Introduction to Data Science
– Data Munging & Fusion
– Text Mining
• Naïve Bayes
– Recommendation Engines
– Principal Component Analysis
– Classification
• Decision Trees
• Random Forest
• Gradient Boosting Machines
– Generalized Linear Models
– Clustering
• KNN
• K-Means
– Graph Theory
– Stable Marriage
Hadoop
Big Data
Core
Engineering
Our Training Offerings
Skills you need

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Corporate Training
Our Process
1 3 5 7
2 4 6
Deliver TrainingDevelop Use Case Measure ImpactUnderstand
Business Needs
.
Proposal /
Contract
.
Pre Training Support
• Reading Materials
• Environment Setup
Post Training Support
• Emails
• Private discussion boards

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Sample Corporate Training
3 Day Corporate Training in Data Science
Day 1
Core
Engineering
– Introduction to Data
Science
– Recommendation
Engine
– Classifications
• Decision Trees
• Random Forest
Day 2
Algorithms
The Brains
– Business Problem
– Ext. Data Dictionary
– Univariate Analysis
– Random Forest
– Model Validation
– Results
Day 3
Use Case I
Practice
For Data Science

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Sample Corporate Training
5 Day Corporate Training in Data Science
Day 1 Day 2
– Introduction to Data
Science
– Recommendation
Engine
– Classifications
• Decision Trees
• Random Forest
• Gradient Boosting
Machines (GBM )
Day 3
– Business Problem
– Ext. Data Dictionary
– Univariate Analysis
– Random Forest
– Model Validation
– Results
Day 4 Day 5
Core
Engineering
Hadoop
Big Data
Algorithms
The Brains
Use Case I
Practice
Use Case II
Practice
– Business Problem
– Ext. Data Dictionary
– Univariate Analysis
– GBM
– Model Validation
– Results
For Data Science

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Day 1
Introductions
• Motivation for Big Data
• Unix for Data Science
• Pushing and Pulling data from remote servers
• Columnar Compressions
• Extended Data Dictionary
Morning Afternoon
Python for Data Science
• Thinking in Python
• Python design patterns for data analytics
• Pandas
• Data Frames
• Aggregations
• Python with Parallel powers
Unix Assignments
• Process data in parallel
• Working with remote Machines
Python Assignments
Sample Day Breakdown
Data Set Used
• Google N-Gram
• 100 Million Records
• Data Processing in Python
• Python scripts and automation

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