Earth Science Data Analytics/2014-03-20 Telecon

ESDA Telecom notes – 3/20/14

Known Attendees:

ESIP Host (Erin), Bamshad Mobasher, Steve Kempler, Seung Hee Kim, John Schnase, Joan Aron, Helen Conover, Robert Downs, Ari Posner, Emily Law, Fritz Vanwijngaarden, Chung-Lin Shie, Jennifer Davis, Rama, Bruce Caron, Brand Niemann, Anjanette Hawk, Rudy Husar, Thomas Huang, Deborah Smith, Smiley, John Farley, Sara Graves, Beth Huffer

Agenda:

1 Topics to better understand, so far:

- Dr. Brand Niemann, Director and Senior Data Scientist, Semantic Community: Sorting out Data Science and Data Analytics

2 Two Guest Speakers – What are people doing with Data Analytics

- Dr. John Schnase, NASA/GSFC: Hands on Experience: Big Data Challenges

- Prof. Bamshad Mobasher, Professor of Data Analytics, DePaul Univeristy: Data Analytics Masters Degree Overview

3 ESDA Activities Discussion: These are solid activities that have been suggested so far:

- Compile use cases (include producer/supplier and data user analytics utilization)

- Compile analytics tools (internal and external to ESIP)

- Do gap analysis

Referenced Material:

Presentations:

Notes:

Good News: We had 3 excellent speakers to discuss: Data Science/Data Analytics (Brand Niemann); An application of Data Analytics on the MERRA (data assimilation) dataset (John), and; Data Analytics Master Program Approach/Overview at DePaul University (Bamshad)

Bad News: The Telecom Convener did not plan enough time for what the presentations deservingly used, thus we did not get to Agenda Item 3. (More on item 3 later)

The cluster started with some thoughts for Cluster objectives and direction based on February’s telecom ideas (see notes from February’s telecom). Basically, It seems that this Cluster can serve multiple purposes to address the various levels of members understanding and interests regarding Data Analytics. This includes:

- ‘Academic’ discussions that allow all of us to be better educated and on the same page in understanding the various aspects of Data Analytics

- Bringing in guest speakers to describe overviews of external efforts and further teach us about the broader use of Data Analytics. (We can always invite speakers back to learn more)

- Activities that ESIP members can actually address and tackle

As a start, this will lay groundwork for our understanding, as the field evolves, and the individual and collective interests of this cluster evolve, in turn, the cluster objectives can evolve. This will be put out as the basis of the ESDA cluster mission/objectives. Please take a look at tit at the top of our Wiki ‘ESDA Home Page’. Please provide comments on what you think of it, does it address your expectations, and/or what else we should include.

Take a look at Brand’s presentation. It provides a real breadth of information regarding Data Science, Data Analytics, what Data Scientists do, current activities in the field, more. Remember: ‘…try to make a story out of the data’.

John’s presentation was equally interesting, describing how he applies analytics (MapReduce) to the MERRA datasets.

Not to be outdone, Bamshad gave a great overview of DePaul University’s Data Analytics program, the types of course taught, a little philosophy behind the program, and the domain areas on which the program focus.

BTW, here is my new favorite predictive analytics figure describing the CRISP-DM process found in both, Brand and Bamshad’s presentations. Only I would substitute ‘Business Understanding’ with ‘Domain Expertise’, to make it more generic.

 Time ran out to discuss the third agenda item. This will be discussed at the next telecom (April 17), and provided here for your contemplation: ESDA Activity

Compile use cases (include producer/supplier and data user analytics utilization) - Need 2 to 4 owners
Compile analytics tools (internal and external to ESIP) – Need 2 to 4 owners (preferably different)
Do gap analysis – Need to 2 to 4 owners (different or some from above groups)

And Potential Future Activities (as of today) - Examine project long case studies to determine successfulness of using data analytics in the project (i.e., lessons learned) - Oh yeah: Create a Cluster Mission Statement and Objectives - Report out to the Federation All

For reference, I repeat some of the key ideas that came out of the February telecom.

▪ We can define the analytics toolset (focusing on Earth science)

▪ We can assemble end-to-end team(s) that together address various aspects of data analytics (and, more broadly, Data Science. This would also surface gaps in our expertise.

▪ We can better understand and provide potential ESIP expertise to NIST activities

▪ Data Supplier vs. Data User perspectives. We can surface/organize the analytics needs and use cases from both perspectives

▪ Another dimension of delineating Data Scientist and Data Analytics is along the Data Creator/Provider < --- > Data End User axis. -- The perspectives and the needs of Data Science and Data Analytics are very different where you are along that axis. -- Typically a real gap exists between the two perspectives

▪ Idea: We can consider focusing on the collection of case studies where organizations have implemented big data solutions to problems, carried out analytics, quality assurance, and have allowed policy makers to make informed decisions based on the end products of data science. From this body of work, which can highlight both successes and failures, I think that the group can begin to form recommendations on how organizations should proceed in data science based on their particular goals. It can also serve as a bed of research for data scientists and IT staff to consider alternatives to their own approaches.

Next Telecon:

April 17, 3:00 EST (third Thursday of each month)
Agenda (as of now)

- Analytics related topic to better understand. DOES ANYBODY HAVE A TOPIC THEY WISH TO BETTER UNDERSTAND

- Listen and Learn - We will have 2 guest speakers to discuss their Analytics activities

- ESDA Activities