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• 1: Conferences, Training and Webcasts
• 1.1: Conferences, Training and Webcasts
• 1.2: Explorable Explanations
• 1.3: Training
• 2: Uses Cases
• 2.1: How metadata can help
• 2.2: Customized data analytics process for PDU
• 2.3: New insights through visualization
• 2.4: Engaging members with data science
• 3: External Resources
• 3.1: Data for Use in R&D
• 3.2: Data Analytic Tools, Tips & Cheat Sheets

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Conferences, Training and Webcasts

Resources for getting started and learning about AI.

Use Cases

Explore various data science EPRI use cases to further here.

External Resources

Additional data science resources.

1 - Conferences, Training and Webcasts

1.1 - Conferences, Training and Webcasts

Conference Series

Predictive Analytics World The “premier machine learning conference.” Tracks in industry, deep learning, business, and more. Includes speakers, workshops, and networking opportunities.

Machine Learning and Data Mining (MLDM) Brings together researchers on machine learning and data mining. Conference includes talks, workshops, tutorials and exhibition.

AI & Big Data Expo Expo showcases next gen technologies and strategies from AI and big data. Tracks include Enterprise AI & Digital Transformation, Data Analytics for AI & IoT, Big Data Strategies and more.

1.2 - Explorable Explanations

Explorable Explanations - Examples and Resources

Michael O' Connor

Explorable Explanations project (Bret Victor’s brainchild) description. http://worrydream.com/ExplorableExplanations/.

There are several really good videos on YouTube and other media sites of Bret Victor giving presentations on this topic.

One of my favorites is his presentation on Media for Thinking the Unthinkable (https://vimeo.com/67076984).

Additional Links

Algorithms relating to sorting, sampling, and graphs (AMAZING)

Fully Interactive and Dynamic Linear Algebra textbook

Have you ever wanted to see an algorithm’s code run? It’s cooler than you would expect.

Added Bonus: Not only does it have an impressive collection of pre-built algorithms to choose from, but it also allows you to input your own code!

Machine Learning Algorithms

Momentum and Gradient Descent

Nearly all of Digital Signal Processing using Fourier Transforms explained visually

Neural Networks

Powerful Math algorithms explained visually (Regression, Eigenvectors, Markov Chains, Probability, etc.)

Probability and Statistics (2017 Kantar Information is Beautiful Winner)

The full data science workflow that is embedded in StitchFix’s business process (really cool)

1.3 - Training

Massive Open Online Courses

Coursera, Udacity, and codeacademy are good start points.

Datacamp is an example of an interactive web environment with lots of lessons for non-programmers to get started without ever speaking one of the languages.

Data Science Central is a good resource for staying at forefront of industry trends in data science.

Kaggle hosts data science competitions to practice, hone in skills with messy, real world data, and tackle actual business problems. Employers take Kaggle rankings seriously, participation is seen as relevant, hands-on project work.

Certifications

KDnuggets has compiled an extensive list.

Cheat Sheets

[http://www.kdnuggets.com/2015/07/good-data-science-machine-learning-cheat-sheets.html]

Free Data Science Education

Data Science CheatSheets are there to help.

2 - Uses Cases

Find more information on how to Leverage Data Science Smartly here.

2.1 - How metadata can help

2.2 - Customized data analytics process for PDU

Unique to Electric Distribution & Utilization

Ingest-to-store

• Decisions that consumers make have a huge impact in aggregate
• The network of intelligent devices is growing with different protocols and data formats, where the same event is measured in different ways
• At ingestion, it is critical not to throw out any data

Cleanse & Data Prep

• Data cleanse and prep takes a huge investment in time to curate and normalize. Expect information gaps in the data records
• Time series is not a snapshot in time. It changes by day, week, month, and year
• Need to determine how sensors are collecting information: constant monitoring or event based monitoring
• Given the massive amounts of data, start with a portfolio of prototype projects that can be scaled up

Visualize-to-Analyze

• Visualize the data to make it easier to see patterns, identify important variables and develop a plan for modeling
• Select manageable analytic data sets to use for data modeling
• Data at different resolutions and sampling may be a useful approach when the data is sparse
• Build several analytic models to help characterize and understand end-use load profiles and extract insights

Find more information here.

2.3 - New insights through visualization

Visualizations help to identify patterns in data to discover and understand stories.

Relationship between input and output

Target = FeS in Ash (% weight)

These charts show a mixed relationship with FeS in Ash (% weight). There may be two (or more) patterns.

Carbon is an important predictor in the model

• Red plots are histograms.
• Blue plots show the scatter relationship at this intersection in the grid. For example, in the bottom row, second column, the relationship between Fe2O3 and FeS in Ash is plotted.
• The histograms for each of these parameters suggest that there might be two groups in each dimension instead of one, but the scatterplot shows a fairly strong correlation.

Cluster Comparison - Scale

Box plots show the median surrounded by the interquartile range (25th percentile to 75th percentile. The 2 clusters are well-separated.)

Dashboard example 1

Dashboard example 2

Learn more here.

2.4 - Engaging members with data science

Opportunity to use data science share with members a different point of view of their data.

Multivariate analysis: Provide members with more sophisticated understanding of the benchmark data by applying multivariate analysis. Discover the interrelationships between multiple variables in the study.

Predictive analysis: Members are interested in the interrelationship of the sustainability efforts. There is an opportunity to understand the causation that may be correlated to sustainability variables in the study.

External data: Provide members with greater context for the metrics by incorporating external data to understand climate, demographics, GIS/mapping and plant operations data may affect sustainability results. For example, the EPA has emissions and water flow data at a high level of detail that can inform the member’s sustainability strategy.

Facilitate a workshop with members

Engaging members with data science

Member engagement: The TI project gave the Sustainability team an opportunity to start a dialogue with members about their hypothesis and the metrics that are of greatest importance to them. The workshop with members generated 12-15 hypothesis to explore.

Predictive variables: Through our analysis, we identified variables that might be better predictors of future performance and tagged them as valid and invalid variables.

SPSS Training: Provided Morgan Scott and a her analyst with training on using SPSS Modeler so that they can continue to explore her data without restrictions and pursue the issue of interests that the members identified during the workshop.

3 - External Resources

3.1 - Data for Use in R&D

Air Quality Measures on the National Environmental Health Tracking Network - CKAN

data.gov

Energy Data Resources | Energy.duke.edu

duke.edu

Air Quality System (AQS) | US EPA

The Air Quality System (AQS) is EPA’s repository of ambient air quality data. AQS stores data from over 10,000 monitors, 5000 of which are currently active.

YouTube

Atmospheric Deposition and Critical Loads Data

NADP Maps and Data

National Atmospheric Deposition Program

wisc.edu

Share

EPA’s Critical Loads Mapper Tool enables access to information on atmospheric deposition, critical loads, and their exceedances to better understand vulnerability to atmospheric pollution.

BlueRaster, epa.gov

CLAD Database Access

National Atmospheric Deposition Program

YouTube

Echo.epa.gov

epa.gov

Emissions

National Emissions Inventory (NEI) | US EPA

Detailed estimate of air emissions of both criteria and hazardous air pollutants from all air emissions sources.

US EPA, OAR, US EPA

Toxics Release Inventory (TRI) Program | US EPA

The Toxics Release Inventory tracks the management of certain toxic chemicals that may pose a threat to human health and the environment.

US EPA, OCSPP, US EPA

Air Markets Program Data | Clean Air Markets | US EPA

YouTube

Satellite Air Quality Measurements

https://earthdata.nasa.gov/earth-observation-data/near-real-time/hazards-and-disasters/air-quality

https://www.usgs.gov/centers/eros/science/national-land-cover-database

https://www.nodc.noaa.gov/ocads/oceans/

https://www.ncdc.noaa.gov/cdo-web/datatools/lcd

Meteorology

https://www.ncdc.noaa.gov/data-access/land-based-station-data/land-based-datasets/automated-surface-observing-system-asos

Overall Link to NOAA Data Sources

https://www.nrel.gov/analysis/jedi/index.html

Using JEDI, you can analyze the energy impacts of wind, biofuels, concentrating solar power, geothermal, marine and hydrokinetic power, coal, and natural gas power plants.

https://openei.org/wiki/Utility_Rate_Database

Rate structure information from utilities in the U.S. maintained by the U.S. Department of Energy

3.2 - Data Analytic Tools, Tips & Cheat Sheets

JavaScript

Cheat Sheets for Java

Linux

Linux Cheat Sheet

Python

Cheat Sheets for Python

NumPy, SciPy and Pandas

R

Cheat Sheets for R

Short Reference Card

R Functions for Regression Analytics

Time Series

Data Mining

Quandl

SQL

SQL Joins

SQL and Hive

Cross Reference between

R, Python (and Matlab)