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Introduction of Data Science

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My class presentation at USC. It gives an introduction about what is data science, machine learning, applications, recommendation system and infrastructure.

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Introduction of Data Science

  1. 1. Data Science Introduction Jason Geng jason@datalaus.com jason@datascienceassociations.org
  2. 2. Agenda What is big data What is data science Data science applications System infrastructure Case study – recommendation system
  3. 3. 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
  4. 4. Data Science Applications Recommendation System Self-driving Text Cognition Spam Filtering
  5. 5. https://en.wikipedia.org/wiki/Data_science#/media/File:Data_visualization_process_v1.png
  6. 6. Machine Learning Algorithm Supervised learning Regression Classification Neural network, deep learning Unsupervised learning Clustering
  7. 7. 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
  8. 8. Case Study
  9. 9. Algorithms Collaborative filtering Content-based recommendation Learning to rank Context-aware recommendation Social network recommendation
  10. 10. 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
  11. 11. User-based Filtering User user- item rating matrix Make user- to-user correlations Find highly correlated users Recommend items to
  12. 12. Item-based Filtering User user-item ratings matrix Make item-to-item correlations Find items that are highly corated Recommend items with highest correlation
  13. 13. Steps in item-based CF Predicted rating for item 2 for user 1
  14. 14. 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

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