Course Overview: Extracting Value from Data Analytics

This course focused on extracting value from data analytics, with an emphasis on reading, comprehension, critical thinking, research, and presentation skills. It aimed to optimize accuracy, power, speed, ease of use, and cost in data analysis. Various topics covered included dealing with unstructured data, scalable analytics, visual analytics, new interfaces, data science, ML, and collaborative query processing. The course also explored crowd-powered algorithms, systems, and new forms of data, providing insights into handling more data effectively and efficiently.

• Data Analytics
• Value Extraction
• Optimization
• Data Science
• Critical Thinking

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Presentation Transcript

1. So, what was this course about?

2. Ingredients Data Analytics Analyzing & extracting value from data Humans As analysts extracting value As workers helping the analysis

3. Course Objectives Reading and Comprehension Skills You read ~25 papers Critical Thinking and Discussion Skills Active engaging in critically analyzing papers flaws and insights Research Skills Semester-long meaty project Presentation Skills Present the key ideas of a database style paper

4. Optimization Objectives Accuracy Better, more complete results Power Enabler of more interesting analyses Speed Want results quickly Ease of use For both novice and expert users Cost Crowds, resources

5. Topics Covered A. Dealing with Unstructured/Noisy Data 1) Crowd-Powered Algorithms 2) Crowd-Powered Systems B. Dealing with More Data 1) Scalable Data Analytics 2) Approximate Data Analytics C. Dealing with Novice Analysts 1) Visual Analytics Systems 2) New Interfaces & Usability D. Dealing with New Scenarios 1) Data Science, ML, and Graph Processing 2) Collaborative Query Processing For each, we covered a A) system or an algorithm + B) connections to other (sometimes old) database topics

6. Topics Covered A. New forms of data 1) Crowd-Powered Algorithms a. CrowdScreen: Filtering data with humans: cost/latency/accuracy; probabilistic reasoning b. So Who Won: Max Graph-based maximum-likelihood reasoning c. Sorts and Joins: Sorting and joins with humans New types of interfaces (hybrid), batching 2) Crowd-Powered Systems a. CrowdDB: DB + Crowds Data model (CNULL), query constructs, query processing b. Deco: DB + Crowds A more complete language

7. Topics Covered B. Dealing with more data 1) Scalable Data Analytics a. Dremel: Google s parallel column-store system distributed query processing, column stores b. SparkSQL: DB layer on Spark Translation from SQL to Spark queries, ..

8. Topics Covered B. Dealing with more data 2) Approximate Analytics: tradeoff between c/l/a a. BlinkDB: Approximate Query Answering System stratified samples help! Query column sets b. Sample+Seek Importance-biased sampling can help

9. Topics Covered C. Dealing with novice analysts 1) Visual Analytics Systems a. Trust me, I m partially right: approximate vis online aggregation b. I ve seen enough ideas of incremental visualization c. Immens in-situ data cube plus brushing and linking d. Polaris: Basis for tableau Idea of a data cube, visualizations = cube aggregates! e. Zenvisage: visual data exploration scalable grouped query execution techniques

10. Topics Covered C. Dealing with novice analysts 2) New Interfaces and Usability a. DBTouch touch-based querying of data: pinch+zoom b. Gestural Query Specification completeness of operators; user study! c. Making Database Systems Usable natural language interface types: forms, keyword search, QBE d. DataPlay building on visual query builders with feedback e. DataSpread spreadsheets+databases

11. Topics Covered D. Dealing with new settings 1) Machine Learning and Graph Processing a. MLBase: Wrapper on ML algorithms parameter tuning for ML is a pain b. Laura Haas s lecture Data Science as a Service c. MADSkills: Wrapper on traditional database kinds of ML-based analyses of interest d. Graphlab: Distributed Graph Analytics tools graph analytics systems, thinking like a vertex

12. Topics Covered D. Dealing with new settings 2) Collaborative Analyses a. Orpheus: data collaboration collaboration as a versioning problem

13. Historical Takeaways Examples: Storage layer: column stores, data compression, data sampling Processing layer: noSQL, adaptive QP, parallel QP c-l-a tradeoff in crowdsourcing, interfaces, batching Usability layer: forms, keyword search, QBE data integration, data cleaning Visualization layer: binning, aggregation, data cubes, online aggregation Applications layer: graph processing machine learning primitives

14. Mix of Papers: Vision vs. Details Visionary, examples Database usability DBTouch/GestureDB MLBase Detail-Oriented, examples CrowdScreen GraphLab Dremel, SparkSQL

15. Mix of Papers: Algorithmic vs. Systems Algorithmic: probably 30-35% Systems-oriented: the majority Not surprising given that this is a database systems course .

16. (Hopefully) Lessons Learned Don t solve non-problems! Importance of thinking about users Interface Language careful systems architecture Generalizable Efficient / Powerful Tailored to use-cases Data analytics involves: Usability Careful, Scalable system architecture (Systems) Principled algorithms design (Algorithms)