Agile Data Science - Speaker Deck

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2 What we do?

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3 Heart of Vegas in charts iPad rankings, US iPad rankings, Australia

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4 Data Impact Team ● Ad-hoc analytics and daily fires ● Deep dive analysis; Predictive analytics ● Data Engineering; R&D Team of 6

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5 Few Examples A B A/B Tests Customer Lifetime Value days $ value Segmentation group 1 group 2 group 3 group 4

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6 Technology Stack C++ ETL orchestration Transformation & Aggregation SQL Data Products Reports Dashboards +

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7 Lessons

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8 Lesson 1: Agile Philosophy for Data Science 1

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9 Agile Manifesto Individuals and interactions over processes and tools Working software over comprehensive documentation Customer collaboration over contract negotiation Responding to change over following a plan * agilemanifesto.org

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10 Agile Data Science Manifesto Individuals and interactions over processes and tools Actionable insights over comprehensive reports Customer collaboration over project negotiation Responding to change over following a plan

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11 “If a building doesn’t encourage [collaboration], you’ll lose a lot of innovation and the magic that’s sparked by serendipity” - Steve Jobs Individuals and interactions over processes and tools

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12 Individuals and interactions over processes and tools Standing Desks + Easily Available Whiteboard

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13 Agile Principles Iterative, incremental and evolutionary Efficient and face-to-face communication Very short feedback loop and adaptation cycle Quality focus - iterations, timeboxed estimates - no to tasks by email (with no face-to-face) - daily standups, pair analysis - verifiable, reproducible findings

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14 Data Science Board

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15 Scrum-Ban in Data Science @ProductMadness ● Weekly cycle ● Daily standup meeting @10am ● ToDo/WIP/Waiting buckets are kept small ● Disruptions to weekly plan are expected ● On-demand planning

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16 Lesson 1: Agile methods in Data Science 1. co-location matter; whiteboard next to your desk 2. Work with decision maker; share preliminary findings 3. Make a research plan; pivot early 4. Book “Findings” meeting before project start 5. MVP for Data Products 6. Do Daily Stand-ups !

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17 Lesson 2: Agile Velocity vs. Acceleration 2

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18 What is Agile Acceleration Waterfall Scrum Units of Work Time Interval Velocity = ΔVelocity = Acceleration* ΔTime VS.

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19 a = F m I run SQL, copy- paste data to Excel and send it by email I created a deep neural network to predict high spenders

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20 Case Study: to Git or not to Git Scripts (ruby, bash, python) Python Apps Python Modules IPython Notebooks Research Documents (word) Presentations (powerpoint) Spreadsheets (excel)

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21 Case Study: Git or not to Git Scripts (ruby, bash, python) Python Apps Python Modules IPython Notebooks ? Research Documents (word) Slides (powerpoint) Spreadsheets (excel)

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22 Case Study: Git or not to Git Scripts (ruby, bash, python) Python Apps Python Modules IPython Notebooks Research Documents (word) Slides (powerpoint) Spreadsheets (excel)

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23 Remove unnecessary weight

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24 Lesson 2: find the lightest suitable tool 1. IPython notebooks: Dropbox over Git 2. Google Slides over Powerpoint Google Slides over Email with images (>2 images) 3. Google Spreadsheets over Excel (for analytics) 4. Podio over Jira (for analytics) 5. Data Transformations in DWH in SQL over Hadoop 6. Don’t copy-paste code in IPython notebooks; use functions; don’t copy-paste functions in notebooks, use modules

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25 Lesson 3: Focus on Closing the Loop 3

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26 Analytics Loop Spot Opportunity Ask the Right Question Make Decision Improve the Business Data Science @work

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27 Analytics Spiral Ideas & Questions Data Analysis Insights Impact

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28 Data Science Value Pyramid Store & Query Reports Descriptive Analytics Predictive Analytics Data Products * inspired by Agile Data Science, Russell Jurney, O'Reilly Media 2013 Record what Happened Was it good or bad? Why did it happen? What will happen? Affect the outcome complexity value

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29 Data Science Value Loop Record what Happened Was it good or bad? Why did it happen? What will happen? Affect the outcome

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30 Limit the number of Open Loops 90% 90% 75% 80% 80% 60% 100% 100% 100% 100% 0% 0% Always prefer to have: 90% of tasks are 100% complete over 100% of tasks are 90% complete VS.

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31 Lesson 3: Focus on Closing the Loop 1. Don’t build predictive models that you can’t act upon. Don’t analyse stuff that does not help to make a decision 2. The best way to deal with Analytics Spiral is to avoid the spiral. Practise Crack a Case and “what if” method. 3. Climb the Data Value Pyramid fast. Once climbed - optimise the Data Value Loop. 4. Limit the number of “open loops”

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32 Lesson 4: Reproducibility Matters 4

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33 To the and back!

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34 Why? Boss: “Great! Can you run this for all monthly cohorts?” Because:

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35 Why? Because: Boss: “Sam is on holiday. Can you re-run his analysis?”

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36 Few IPython Tips

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37 Import all commonly used tools in one line. All access and security is abstracted away. Focus on SQL, not data access formatting and publishing a .png in one line of code PyCharm has great SQL editor

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38 Lesson 4: Reproducibility ● Get rid of Windows and you get rid of Excel ● ipynb are always shared and versioned; Prefer simple cloud sharing to VCS ● Streamline data access functions ● Cache long-running code and queries ● Develop a common library

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39 In Summary...

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40 Summary ● Agile approach works well in Data Science ● Find the lightest suitable tool for a task ● Reproducibility is not negotiable ● Focus on closing the loop(s)

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41 Questions? We are Hiring ! volodymyrk [email protected]