DevSci: Better Software Through Data #KCDC2018
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1 DevSci: Better Software Through Data #KCDC2018
2
3 What is data science? Why is it important? How do I get started?
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8 Job Postings for Data Scientists
9 Top-paying Tech Skills Skill 2016 Change Skill 2016 Change Source: Dice Salary Survey 2017
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13 About Me Data Science Consultant Education B.S. in Computer Science (ISU) B.A. in Philosophy (ISU) Community Keynote speaker Pluralsight author DataCamp author Microsoft MVP AI ASPInsider
14 About Me Data Science Consultant Education B.S. in Computer Science (ISU) B.A. in Philosophy (ISU) Community Keynote speaker Pluralsight author DataCamp author Microsoft MVP AI ASPInsider
15 About Me Data Science Consultant Education B.S. in Computer Science (ISU) B.A. in Philosophy (ISU) Community Keynote speaker Pluralsight author DataCamp author Microsoft MVP AI ASPInsider
16 What is data science?
17 Computer Science Data Science Math and Statistics Domain Knowledge
18 Data Knowledge Decision Action
19 What Is a Data Scientist? Performs data science More than a scientist More than an analyst More than a developer
20 What skills are necessary?
21 Data Science Skills Programming Working with data Descriptive statistics Data visualization
22 Data Science Skills Programming Working with data Descriptive statistics Data visualization Statistical modeling Handling Big Data Machine learning Deploying to production
23 What tools are used?
24 SQL Excel Python R MySQL Python tools ggplot SQL Server Tableau JavaScript Matplotlib Java PostgreSQL Oracle D3 Homegrown Hive Spark Cloudera Visual Basic MongoDB Hadoop SAS C++ PowerPivot Scala SQLite C Pig RedShift Weka Hbase (EMR) Perl SPSS Teradata Share of Respondents 70% 60% Data Science Tools 50% 40% 30% 20% 10% 0% Tool: language, platform, analytics Source: O Reilly 2015 Data Science Salary Survey
25 SQL Excel Python R MySQL Python tools ggplot SQL Server Tableau JavaScript Matplotlib Java PostgreSQL Oracle D3 Homegrown Hive Spark Cloudera Visual Basic MongoDB Hadoop SAS C++ PowerPivot Scala SQLite C Pig RedShift Weka Hbase (EMR) Perl SPSS Teradata Share of Respondents 70% 60% Data Science Tools 50% 40% 30% 20% 10% 0% Tool: language, platform, analytics Source: O Reilly 2015 Data Science Salary Survey
26 SQL Excel Python R MySQL Python tools ggplot SQL Server Tableau JavaScript Matplotlib Java PostgreSQL Oracle D3 Homegrown Hive Spark Cloudera Visual Basic MongoDB Hadoop SAS C++ PowerPivot Scala SQLite C Pig RedShift Weka Hbase (EMR) Perl SPSS Teradata Share of Respondents 70% 60% Data Science Tools 50% 40% 30% 20% 10% 0% Tool: language, platform, analytics Source: O Reilly 2015 Data Science Salary Survey
27 SQL Excel Python R MySQL Python tools ggplot SQL Server Tableau JavaScript Matplotlib Java PostgreSQL Oracle D3 Homegrown Hive Spark Cloudera Visual Basic MongoDB Hadoop SAS C++ PowerPivot Scala SQLite C Pig RedShift Weka Hbase (EMR) Perl SPSS Teradata Share of Respondents 70% 60% Data Science Tools 50% 40% 30% 20% 10% 0% Tool: language, platform, analytics Source: O Reilly 2015 Data Science Salary Survey
28 SQL Excel Python R MySQL Python tools ggplot SQL Server Tableau JavaScript Matplotlib Java PostgreSQL Oracle D3 Homegrown Hive Spark Cloudera Visual Basic MongoDB Hadoop SAS C++ PowerPivot Scala SQLite C Pig RedShift Weka Hbase (EMR) Perl SPSS Teradata Share of Respondents 70% 60% Data Science Tools 50% 40% 30% 20% 10% 0% Tool: language, platform, analytics Source: O Reilly 2015 Data Science Salary Survey
29 How is data science performed?
30 The Data Science Process Data
31 The Data Science Process Find a question Data
32 The Data Science Process Find a question Collect the data Data
33 The Data Science Process Find a question Collect the data Data Prepare the data
34 The Data Science Process Find a question Collect the data Data Prepare the data Create a model
35 The Data Science Process Find a question Collect the data Evaluate the model Data Prepare the data Create a model
36 The Data Science Process Find a question Deploy the model Collect the data Evaluate the model Data Prepare the data Create a model
37 The Data Science Process Find a question Deploy the model Collect the data Evaluate the model Data Prepare the data Create a model
38 The Data Science Process Find a question Iterative process Deploy the model Explore the data Evaluate the model Data Prepare the data Create a model
39 The Data Science Process Find a question Iterative process Non-sequential Deploy the model Explore the data Evaluate the model Data Prepare the data Create a model
40 The Data Science Process Find a question Iterative process Deploy the model Explore the data Non-sequential Early termination Evaluate the model Data Prepare the data Create a model
41 Why is data science important?
42 Two Main Approaches Build intelligent software Improve development practices
43 Two Main Approaches Build intelligent software
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45 Internet Sales Show me sales by gender and marital status. Displaying sum of sales by gender and marital status Marital Status: Married Single Show me sales by gender and marital status. Male Female $0k $5k $10k $15k
46 Machine Learning Human Cat Dog Car
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51 Anticipatory Design Collect Data Create Algorithm Anticipate Choices
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55 Two Main Approaches Improve development practices
56 Data-Driven Decision Making Build Learn Measure
57 Hypothesis-Driven Development Hypothesis Analysis Experiment
58 Hypothesis-Driven Development Hypothesis Hypothesis: Users will prefer feature A over feature B Analysis Experiment
59 Hypothesis-Driven Development Hypothesis Hypothesis: Users will prefer feature A over feature B Analysis Experiment Experiment: Survey 100 users and ask for their preference
60 Hypothesis-Driven Development Hypothesis Hypothesis: Users will prefer feature A over feature B Analysis: 80% of users prefer feature A Analysis Experiment Experiment: Survey 100 users and ask for their preference
61 Hypothesis-Driven Development Hypothesis Hypothesis: Pair programming will increase our long-term velocity Analysis Experiment
62 Hypothesis-Driven Development Hypothesis Hypothesis: Pair programming will increase our long-term velocity Analysis Experiment Experiment: Pair for 4 sprints and track velocity
63 Hypothesis-Driven Development Hypothesis Hypothesis: Pair programming will increase our long-term velocity Analysis: Velocity increased by 20% per sprint Analysis Experiment Experiment: Pair for 4 sprints and track velocity
64 Hypothesis Stories <Hypothesis> We assume that <hypothesis> Will result in<outcome> We will have succeeded when <measurable result>
65 Hypothesis Stories Pair Programming Hypothesis We assume that pair programming Will result in higher long-term velocity We will have succeeded when we have seen a 10% or greater increase in velocity after 4 sprints.
66 A/B Testing
67 A/B Testing
68 Feature Toggles New Feature Feature Toggles User Groups
69 Feature Toggles New Feature Feature Toggles User Groups
70 DevOps Pipeline Code Source Control Build Q/A Deploy Prod
71 DevOps Pipeline Code Source Control Build Q/A Deploy Prod
72 Code Quality Metrics Source: NDepend
73 Source Control Metrics
74 Build Metrics Source: Visual Studio Team Services
75 Q/A Metrics
76 Deployment Metrics Source: Octopus Deploy
77 Software Telemetry
78 DevOps Pipeline Code Source Control Build Q/A Deploy Prod
79 How do I get started?
80 What are the ingredients of a data-driven enterprise?
81 Strategy Culture People Technology Data
82 Strategy
83 People
84 Data
85 Technology
86 Culture
87 What is the process of becoming a data-driven enterprise?
88 AI Predict Analyze Organize Measure
89 1. Measure Transactions Instrumentation Logging Surveys Digitization External data Measure
90 2. Organize Transform Clean Store Data ETL Data Warehouse Data Lake Organize Measure
91 3. Analyze Reports Dashboards KPI monitors Decision support Descriptive analytics Diagnostic analytics Analyze Organize Measure
92 4. Predict Predict Predictive analytics Prescriptive analytics Machine learning Hypothesis testing Experimentation Analyze Organize Measure
93 5. Automate AI Predict Artificial intelligence Expert systems Deep learning Analyze Organize Measure
94 AI Predict Analyze Organize Measure
95 Advice for Success Get buy-in from leadership Focus on low-hanging fruit Don t silo data science teams Democratize your data
96 Advice for Success Embrace smart failure Focus on feedback Embed data collection Avoid the Observer Effect
97 Where to Go Next?
98 Where to Go Next Data Camp: Pluralsight: Coursera:
99 Pluralsight Courses Data Science: The Big Picture Data Science with R Exploratory Data Analysis with R Data Visualization with R (3-part) Deep Learning: The Big Picture
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101 Feedback Very important to me! What did you like? What could I improve?
102 Conclusion
103 What data science is Why it is important How to get started
104
105 Are you prepared? Is your organization? Is our world prepared?
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107 Thank You! Matthew Renze Data Science Consultant Renze Consulting Website:
108
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