Human and machine collaboration can produce better outcomes than either could achieve alone. An analytical process can be deconstructed into tasks that are better suited for humans or machines. The interface between human and machine is important to design intuitively. An example framework was presented for collaborative data exploration, with phases for data preparation and exploration. Requirements and examples were provided for different task types like category aggregation, binning, clustering, filtering and graph analysis. The key is assigning each step in the process to the agent - human or machine - best equipped to handle it.

1. HUMAN + MACHINE
COLLABORATION
FOR IMPROVED ANALYTICAL PROCESSES

2. TONY OJEDA (ME)
• Data Scientist @ Follett
• Founder @ District Data Labs
• Co-Author
• Applied Text Analysis with Python
(O’Reilly, Fall 2017)
• Practical Data Science Cookbook
(Packt, Fall 2014)
• Conference Speaker
• Data Day Seattle 2016
• PyData - Carolinas & DC 2016

3. SOME BACKGROUND…

8. RECENT AI HEADLINES
• Can The Rise Of Artificial Intelligence End Humanity?
• Will Artificial Intelligence Leave You Jobless?
• Essential Skills To Keep Your Job In The Era Of
Artificial Intelligence
• Artificial intelligence probably won't kill you, but it
could take your job

9. How can we combine
human & machine abilities
to produce better outcomes
than either could on their own?

10. Not Human vs. Machine
But Human + Machine

11. DESIGNING COLLABORATIVE
ANALYTICAL PROCESSES

12. WHAT IS AN ANALYTICAL PROCESS?
• A series of tasks for ingesting, transforming, analyzing, modeling, or
visualizing data.
Ingestion Wrangling Analysis Modeling Visualization
Data Science Pipeline

13. DECONSTRUCTING A PROCESS
Steps
Tasks
Process

14. What types of tasks are humans better at?
What types of tasks are machines better at?

15. TYPES OF TASKS HUMANS ARE GOOD AT
• Sensory Tasks
• Social/Language/Communication Tasks
• General or Domain Knowledge Tasks
• Tasks Requiring Flexibility, Adaptability, or Creativity
• Exploratory or Investigative Tasks

16. TYPES OF TASKS MACHINES ARE GOOD AT
• Tasks Where Precision is Important
• Tasks that Require Processing Vast Amounts of Information
• Memory and Recollection Tasks
• Repetitive Tasks Where Consistency is Important

17. DESIGNING COLLABORATIVE PROCESSES
• Deconstruct the process into tasks and steps.
• Determine which steps should be performed by the human and
which should be performed by the machine.
• Identify the points of interaction and ensure those are intuitive.

18. THE INTERFACE IS IMPORTANT

19. COLLABORATIVE DATA EXPLORATION

20. DATA EXPLORATION FRAMEWORK
Prep
Phase
Explore
Phase

21. CREATE: CATEGORY AGGREGATIONS
Categorical variables with a lot
of categories (ex. more than 10)
Distill down into fewer
categories

22. CATEGORY AGGREGATION REQUIREMENTS
• Identification of categorical variables and unique values
• Natural language understanding
• General and/or domain knowledge
• Similarity in meaning
• Sometimes creativity

23. CREATE: CONTINUOUS BINS
Very Low
Low
Moderate
High
Very High
Identify continuous
variables
Assign them to buckets or
bins based on how high or
low their values are.

24. BINNING REQUIREMENTS
• Identification of continuous variables
• Comparison, ordering, and segregation
• Knowing whether higher or lower values are better
• Meaningful naming of resulting categories

25. CONTINUOUS BINNING EXAMPLE
import pandas as pd
import numpy as np
numeric_cols = data.select_dtypes(include=[np.number]).columns.values
for column in numeric_cols:
quint_levels = ['Very Low', 'Low','Moderate', 'High', 'Very High']
data[column + ' Level'] = pd.qcut(data[column], 5, quint_levels)
data[column + ' Decile'] = pd.qcut(data[column], 10, range(1,11))
data[column + ' Perc'] = pd.qcut(data[column],100, range(1,101))

26. CREATE: CLUSTER CATEGORIES

27. CLUSTERING REQUIREMENTS
• Identification of numeric variables
• Clustering similar records together
• Determining quality and appropriate numbers of clusters
• Meaningful naming of resulting categories

28. DATA EXPLORATION FRAMEWORK
Prep
Phase
Explore
Phase

29. EXPLORE: FILTER + AGGREGATE

30. FILTER + AGGREGATE REQUIREMENTS
• Identifying categorical and numeric variables.
• Filtering/sub-setting the data set by categories.
• Aggregating on categories and calculation of numeric fields.
• Interpreting results and determining what is useful.

31. EXPLORE:
FIELD
RELATIONSHIPS

32. FIELD RELATIONSHIP REQUIREMENTS
• Identifying numeric fields.
• Comparing cross-distributions of values across all combinations of
numeric fields.
• Identifying existence, direction, strength, and type of relationship.
• Determining which relationships (or lack thereof) are interesting or
insightful.

33. EXPLORE: ENTITY RELATIONSHIPS

34. GRAPH ANALYSIS REQUIREMENTS
• Identifying hierarchical entity levels in the data.
• Identifying similarities and strength of similarities between entities.
• Identifying clusters, communities, sub-networks and other important
groupings within the network.
• Interpreting those relationships and what they mean in the real
world.

35. KEY TAKE-AWAYS
• Human machine collaboration is important and very useful.
• We can design these processes via deconstruction into tasks and
steps.
• Pay special attention to the interfaces.
• There is plenty of room for development and advancement in this
area, and Python already contains a lot of the tools we need to make
progress.

36. WHERE TO LEARN MORE & GET INVOLVED
• Blog: blog.districtdatalabs.com
• Cultivar: github.com/DistrictDataLabs/cultivar
• Yellowbrick: github.com/DistrictDataLabs/yellowbrick
• Twitter: @tonyojeda3
• LinkedIn: linkedin.com/in/tonyojeda

37. THANK YOU!