Applied Analytics: Some Yukon Examples · ★ Data munging: scraping, parsing, knitting and formatting a.k.a data wrangling ★ Visualization: tables, graphs, charts and dynamic insights

Applied Analytics: Some Yukon Examples

Yukon Science Community of Practice (SCOPe) Fall 2017 Data Science Series

14 September 2017

� �1

PaulKishchuk,MA

203– 301HawkinsStreetWhitehorse,YukonY1A1X5

[email protected]

tel:867.668.3164

Presentation Outline

1\. Brief Overview of the Field of Data Science

2\. Applied Analytics: Some Yukon Examples

3\. YG Data Science Policy Issues

� �2

1\. Brief Overview of the Field of Data Science

What is data science?

“SCOPe is defining data science as an interdisciplinary field about scientific methods, processes and systems to extract knowledge or insights from data in various forms.

Data science includes data mining, data visualization, working with big datasets, working with small datasets, data governance, open data, business analytics and much more.” 

� �3

Who are data scientists?

Reproduced from: Cathy O’Neil and Rachel Schutt. 2014. Doing Data Science: Straight Talk from the Frontline.

� �4

What do data scientists do?

★ Traditional statistical analysis: descriptive statistics, sampling, inferential methods, predictive analytics, etc.

★ Data munging: scraping, parsing, knitting and formatting a.k.a data wrangling

★ Visualization: tables, graphs, charts and dynamic insights

Adapted from Cathy O’Neil and Rachel Schutt. 2014. Doing Data Science: Straight Talk from the Frontline.

Takeaway: “data scientists” are actually “data science teams”.

� �5

Polleverywhere Poll:

What benefits can data science bring to your work?

To submit multi-word phrases, link the words with an underscore.

For example, cat_in_the_hat rather than “cat in the hat.”

� �6

2\. Applied Analytics: Some Yukon Examples

� �7

1. Static Data and Static Visualization:

YG Property Tax Roll

� �8

2. Static Data and Dynamic Visualization:

Whitehorse Food Bank

� �9

3. Dynamic Data and Static Visualization:

Poll Everywhere Poll

� �10

4. Dynamic Data and Dynamic Visualization:

Poll Everywhere Poll Round 2

� �11

3\. YG Data Science Policy Issues

Appetite for Data Science

� �12

� �13

� �14

This photo of Miles Canyon is courtesy of TripAdvisor.

Data Literacy

� �15

� �16

Property Tax Roll Data

� �17

� �18

Data Sharing

• cross-department data sharing

• internal vs. external data sharing

• confidentiality (de-identified data)

• data security (transport, encryption, access)

• data integrity (single version of the truth)

• destruction (perpetual or time-limited)

� �19

PE Poll: What strengths would you bring to a data science team?

A. hacking skills B. math and statistics knowledge C. substantive expertise

� �20

Thank you!

� �21