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Data Science Introduction
Jason Geng
jason@datalaus.com
jason@datascienceassociations.org
Agenda
What is big data
What is data science
Data science applications
System infrastructure
Case study – recommendation system
Data Scientist
Analytics
Artificial
Intelligence
Statistics
Natural Language
ProcessingFeature Engineering
Scientific
Method
Simulation
Data & Text Mining
Machine Learning
Predictive
Modeling
Graph
Analytics
Data
Management
Data Warehousing
Mashups
Databases
Business Intelligence
Big Data
Information Retrieval
Art & Design
Business
Mindset
Computer
Science
Visualization
Communication
Data Product Design
Domain Knowledge
Ethics
Privacy & Security
Programming
Cloud Computing Distributed Systems
Technology & Infrastructure
Growth
Hacking
Social network
Public Relation
Online ToolsResource
Data Science Applications
Recommendation System
Self-driving
Text Cognition
Spam Filtering
https://en.wikipedia.org/wiki/Data_science#/media/File:Data_visualization_process_v1.png
Machine Learning Algorithm
Supervised
learning
Regression
Classification
Neural
network,
deep learning
Unsupervised
learning
Clustering
Recommendation System
Are a subclass of information filtering system that
seek to predict the “rating” or “preference” that a
user would give to an item ---- Wikipedia
Case Study
Algorithms
Collaborative filtering
Content-based recommendation
Learning to rank
Context-aware recommendation
Social network recommendation
Collaborative Filtering
Basic Assumption
• Users with similar interests have common
preference
• Sufficiently large number of user preferences are
available
Main Approaches
• User-based
• Item-based
User-based Filtering
User user-
item rating
matrix
Make user-
to-user
correlations
Find highly
correlated
users
Recommend
items to
Item-based Filtering
User user-item
ratings matrix
Make item-to-item
correlations
Find items that are
highly corated
Recommend items
with highest
correlation
Steps in item-based CF
Predicted rating for item 2 for user 1
Problem with Collaborative Filtering
New user cold start problem
New item cold start problem
Popularity bias: tend to recommend only popular items
Sparsity problem: if there are many items to be recommended, user/rating
matrix is sparse and it hard to find the users who have rated the same item
Introduction of Data Science

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