Strata + Hadoop World 2015¶

http://strataconf.com/big-data-conference-ca-2015

Average ratings¶

5.00
1. On the Computational and Statistical Interface and “Big Data” (5.00, 3 ratings)
2. Drugs, DNA, and Dinosaurs: Building High Quality Knowledge Bases with DeepDive (5.00, 3 ratings)
3. HOWTO Make Your Future Data Scientists Love You (5.00, 1 rating)
4.00 - 4.99
1. Visual Understanding Beyond Naming (4.88, 8 ratings)
2. Forecasting Space-time Events (4.83, 6 ratings)
3. Connected Cows? (4.67, 33 ratings)
4. Finding Repeated Structure in Time Series Data: Commercial and Scientific Opportunities (4.67, 6 ratings)
5. Interpretable Machine Learning in Practice (4.67, 3 ratings)
6. How to Detect Anomalies in High Cardinality Dimensions and Make Them Actionable (4.65, 23 ratings)
7. Practical Methods for Identifying Anomalies That Matter in Large Datasets (4.60, 15 ratings)
8. Beyond DNNs towards New Architectures for Deep Learning, with Applications to Large Vocabulary Continuous Speech Recognition (4.50, 4 ratings)
9. Why Spark Is the Next Top (Compute) Model (4.40, 5 ratings)
10. Charting a Path Forward: The Future of Data Visualization (4.39, 36 ratings)
11. Agile Data Profiling in the Big Data Era (4.33, 12 ratings)
12. Playing Nice in the Product Playground: Data Scientists, Engineers, and Product Managers working together to create innovative data products (4.25, 4 ratings)
13. Big Data Lessons from Our Cybernetic Past (4.23, 44 ratings)
14. Year Zero: How We’ll Run Our Lives in Ten Years’ Time (4.17, 24 ratings)
15. A Quest for Visual Intelligence in Computers (4.14, 7 ratings)
16. Data Science: Where are We Going? (4.12, 33 ratings)
17. New Directions for Spark in 2015 (4.11, 37 ratings)
18. Tensor Methods for Large-scale Unsupervised Learning: Applications to Topic and Community Modeling (4.00, 6 ratings)
19. From Academia to Data Science: Lessons Learned Founding the Insight Data Science Fellows Program (4.00, 3 ratings)
20. Couchbase to Hadoop at Linkedin: Kafka is Enabling the Big Data Pipeline (4.00, 1 rating)
21. Streaming Analytics: It’s Not The Same Game (4.00, 1 rating)
3.00 - 3.99
1. High Performance Machine Learning through Codesign and Rooflining (3.86, 7 ratings)
2. Why Julia’s Important for Data Science (3.67, 3 ratings)
3. Graph mining for log data (3.60, 5 ratings)
4. The Emerging Age of Data-Driven Policy Design: Examples from Trying to Manage the Global Climate (3.55, 22 ratings)
5. Thursday Keynote Welcome (3.12, 8 ratings)
6. Impacting Business as it Happens (3.10, 21 ratings)
2.00 - 2.99
1. Hadoop’s Impact on the Future of Data Management (2.77, 26 ratings)
2. Close Encounters with the Third Kind of Database (2.57, 21 ratings)
3. Data: Open for Good and Secure by Default (2.52, 21 ratings)
4. Friday Keynote Welcome (2.50, 4 ratings)
5. Intelligence in the Age of Plethora (2.44, 34 ratings)
6. A New Approach to Big Data (2.24, 33 ratings)
7. Intel and the Role of Open Source in Delivering on the Promise of Big Data (2.20, 25 ratings)
8. Data Applications: Speed vs Accuracy (2.00, 4 ratings)
1.00 - 1.99
1. A Bigger Lens Through which to View the World- the IBM Twitter Alliance (1.92, 25 ratings)
0.00 - 0.99
N/A
1. Top Ten Pitfalls to Avoid in a SQL-on-Hadoop Implementation

Category with Ratings¶

Ask Us Anything
Business & Industry
Connected World
Data Science
1. HOWTO Make Your Future Data Scientists Love You (5.00, 1 rating)
2. How to Detect Anomalies in High Cardinality Dimensions and Make Them Actionable (4.65, 23 ratings)
3. Agile Data Profiling in the Big Data Era (4.33, 12 ratings)
4. Playing Nice in the Product Playground: Data Scientists, Engineers, and Product Managers working together to create innovative data products (4.25, 4 ratings)
5. From Academia to Data Science: Lessons Learned Founding the Insight Data Science Fellows Program (4.00, 3 ratings)
6. Data Applications: Speed vs Accuracy (2.00, 4 ratings)
Design & Interfaces
Enterprise Adoption
Events
Hadoop & Beyond
Hadoop in Action
Hadoop Platform
1. Top Ten Pitfalls to Avoid in a SQL-on-Hadoop Implementation
Hardcore Data Science
1. On the Computational and Statistical Interface and “Big Data” (5.00, 3 ratings)
2. Drugs, DNA, and Dinosaurs: Building High Quality Knowledge Bases with DeepDive (5.00, 3 ratings)
3. Visual Understanding Beyond Naming (4.88, 8 ratings)
4. Finding Repeated Structure in Time Series Data: Commercial and Scientific Opportunities (4.67, 6 ratings)
5. Interpretable Machine Learning in Practice (4.67, 3 ratings)
6. Beyond DNNs towards New Architectures for Deep Learning, with Applications to Large Vocabulary Continuous Speech Recognition (4.50, 4 ratings)
7. A Quest for Visual Intelligence in Computers (4.14, 7 ratings)
8. Tensor Methods for Large-scale Unsupervised Learning: Applications to Topic and Community Modeling (4.00, 6 ratings)
9. High Performance Machine Learning through Codesign and Rooflining (3.86, 7 ratings)
10. Why Julia’s Important for Data Science (3.67, 3 ratings)
11. Graph mining for log data (3.60, 5 ratings)
Keynotes
1. Charting a Path Forward: The Future of Data Visualization (4.39, 36 ratings)
2. Big Data Lessons from Our Cybernetic Past (4.23, 44 ratings)
3. Year Zero: How We’ll Run Our Lives in Ten Years’ Time (4.17, 24 ratings)
4. Data Science: Where are We Going? (4.12, 33 ratings)
5. New Directions for Spark in 2015 (4.11, 37 ratings)
6. The Emerging Age of Data-Driven Policy Design: Examples from Trying to Manage the Global Climate (3.55, 22 ratings)
7. Thursday Keynote Welcome (3.12, 8 ratings)
8. Hadoop’s Impact on the Future of Data Management (2.77, 26 ratings)
9. Data: Open for Good and Secure by Default (2.52, 21 ratings)
10. Friday Keynote Welcome (2.50, 4 ratings)
11. Intelligence in the Age of Plethora (2.44, 34 ratings)
12. A New Approach to Big Data (2.24, 33 ratings)
Keynotes, Sponsored
1. Connected Cows? (4.67, 33 ratings)
2. Impacting Business as it Happens (3.10, 21 ratings)
3. Close Encounters with the Third Kind of Database (2.57, 21 ratings)
4. Intel and the Role of Open Source in Delivering on the Promise of Big Data (2.20, 25 ratings)
5. A Bigger Lens Through which to View the World- the IBM Twitter Alliance (1.92, 25 ratings)
Law, Ethics & Open Data
Machine Data / IoT
1. Forecasting Space-time Events (4.83, 6 ratings)
2. Practical Methods for Identifying Anomalies That Matter in Large Datasets (4.60, 15 ratings)
3. Streaming Analytics: It’s Not The Same Game (4.00, 1 rating)
Security
Spark in Action
1. Why Spark Is the Next Top (Compute) Model (4.40, 5 ratings)
Sponsored
1. Couchbase to Hadoop at Linkedin: Kafka is Enabling the Big Data Pipeline (4.00, 1 rating)
Training