Data Futurology - Leadership And Strategy in Artificial Intelligence, Machine Learning, Data Science

Kevin Harrison is working as Chief Data Officer and Deputy Chief Information Officer for the City of Oakland in California. Prior to this he worked as the first ever Chief Data Officer for the State of Illinois. During that time he designed the blueprint for the State Data Practice. Operating under the new Department of Innovation and Technology agency, he implemented an enterprise approach to Business Intelligence and Data Analytics, covering all 60 State Agencies to create a collaborative and sharing environment across the state.

In This Episode:

01:55 Professional background of Kevin

06:30 Why data is important?

07:20 Evolution of Data warehousing

10:00 How organizations are utilizing the data?

11:39 As data officer, how to help organizations to improve their data capabilities?

13:00 Building trust is crucial for project success

13:30 Transition from small to bigger project

16:12 Challenges faced as data consultant

19:00 Educating about the change coming to data science

21:00 Process of data strategy for organizations

23:50 Why so many data warehousing failed?

26:00 Importance of data governance

27:10 Biggest problem in data governance

31:56 Role of data storage

35:15 Challenges faced from moving to another industry/sector

38:42 Qualities data scientist should have

41:43 Future of data science

42:30 Advice to the listeners

Now you can support Data Futurology on Patreon!  

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Thank you to our sponsors: 

UNSW Master of Data Science Online: studyonline.unsw.edu.au 

Datasource Services: datasourceservices.com.au or email Will Howard on [email protected] 

Fyrebox - Make Your Own Quiz!

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

Mike serves as Head of Data Science at Uber ATG and lecturer for UC Berkeley iSchool Data Science master’s program.  Mike has led several teams of Data Scientists in the bay area as Chief Data Scientist for InterTrust and Takt, Director of Data Sciences for MetaScale, and Chief Science Officer for Galvanize he oversaw all data science product development and created the MS in Data Science program in partnership with UNH.  Mike began his career in academia serving as a mathematics teaching fellow for Columbia University and graduate student at the University of Pittsburgh. His early research focused on developing the epsilon-anchor methodology for resolving both an inconsistency he highlighted in the dynamics of Einstein’s general relativity theory and the convergence of “large N” Monte Carlo simulations in Statistical Mechanics’ universality models of criticality phenomena.

In this episode, Michael talks about how he accidentally got into data and his work with simulation. Then, Michael discusses his background in data science product development and data science education. He reveals all the mistakes he made with his transition from academics to industry.  Later, Michael tells us what attracted him to data science education and how he balances industry projects with his teachings. Rapid growth is a challenge with technology management because your skillset will get rusty as the technology advances. Lastly, Michael talks fake news, bootstrapping, and Fake or Fact. 

In This Episode:

[00:20] Michael accidentally got into data

[02:15] About Michael Tamir

[03:40] Transition to industry

[06:40] Software engineering challenges 

[08:45] Data Science Education 

[15:15] Adaptive learning 

[17:15] Team management

[19:05] Challenges with rapid growth

[24:25] Fake news

[27:25] Toughest challenge

[28:50] Fake or Fact

[31:20] Listener questions

Mike's quotes from the episode:

“You have to be really careful about what you do and what you do not teach in order to make sure students are successful in the long-term.”

“Decisions are going to be best made by those who are closest to the ground.”

“You’re not going to be the expert in every group you are managing.”

“I take full responsibility for any failures with the algorithm.”

“Most of my time is spent on my day job.” 

“Find out what you enjoy about data science skills; find the role that is looking for those skills.”

“I enjoy the science and making sure we are asking the questions in a scientifically sound way.”

Connect:

Twitter - https://twitter.com/MikeTamir

LinkedIn – https://www.linkedin.com/in/miketamir/

Website - http://www.fakeorfact.org

Now you can support Data Futurology on Patreon!  

https://www.patreon.com/datafuturology 

Thank you to our sponsors: 

UNSW Master of Data Science Online: studyonline.unsw.edu.au 

Datasource Services: datasourceservices.com.au or email Will Howard on [email protected] 

Fyrebox - Make Your Own Quiz!

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

David Niemi is Vice President of Measurement and Evaluation at Kaplan, Inc., where he oversees efforts to improve the quality of measurement across all education units, evaluate the effectiveness of curricula and instruction, and study the impact of innovative products and strategies.

Previously he was Vice President Evaluation and Research, at K12 Inc., where he directed assessment development and validation, evaluation of products and services, and research studies used to drive curriculum development. He has been a co-principal investigator for a number of large-scale assessment research projects funded by the U.S. Department of Education and the National Science Foundation and has collaborated on Department of Defence training studies. As a researcher and professor at UCLA and the University of Missouri, respectively, he has also managed assessment research and development studies in school districts across the U.S. and has trained thousands of teachers and other professionals to design and use assessments more effectively.

David's new book is:

Learning Analytics in Education: Experts Explain How To Use Data To Understand and Increase Learner Success

New technologies, better measures and more data, all related to learning, hold the promise of helping educators increase their students’ success. The relatively new field of learning analytics has developed to help educators understand and use the increasing amounts of evidence from learners’ experiences. How can educators harness access to greater data to improve learning on a large scale?

Learning Analytics in Education is a new book written by a broad range of experts who explain their methods, describe examples, and point out new underpinnings for the field. The collected essays show how learning analytics can improve the chances of success for all learners through deeper understanding of the academic, social-emotional, motivational, identity and meta-cognitive context each learner uniquely brings.

The collection was edited by four noted educational experts including David Niemi, vice president of measurement and evaluation at Kaplan, Inc., the global educational services company well-known for using advanced learning science and learning engineering methods in its programs and products.

"At Kaplan, we've been invested in using learning science and data analytics for several years to help us design courses and refine instructional methods to help students achieve better outcomes," explains Niemi. "Educators today face accelerating change as education undergoes a fundamental transformation driven by the replacement of traditional analog tools by digital systems and expansive data inputs." He adds, "Understanding how to use these new streams of available data to best guide student learning is the essential point of the book."

Now you can support Data Futurology on Patreon! 

https://www.patreon.com/datafuturology

Thank you to our sponsors:

UNSW Master of Data Science Online: studyonline.unsw.edu.au

Datasource Services: http://www.datasourceservices.com.au/ or email Will Howard on [email protected]

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

Kjersten Moody joined State Farm in July 2017 as Vice President and Chief Data & Analytics Officer in Bloomington, Illinois. 

Previously, Kjersten led Data & Analytics and IT groups at global companies, such as FICO (Braun), Thomson Reuters and Unilever. She has a record of delivering tangible, positive business results, and a depth of experience in scaling operations, planning/executing mission-critical business initiatives, and achieving profitability objectives. 

Kjersten is a graduate of the University of Chicago and has a proven track record in modernizing and scaling operations, executing mission-critical business initiatives, and achieving profitability objectives. An energetic leader with a focus on people development, diversity, and inclusion Kjersten demonstrates the ability to effectively lead and work in highly complex environments.

In this episode, Kjersten talks about her love for data and how it compliments an understanding of human behavior. She is incredibly grateful for the chances others took on her to get her in the role she is today. Understanding how to thrive in stressful situations is one of the essential lessons Kjersten learned in her early roles. 

Her leadership style is open, honest, and collaborative while always ensuring to take time out of her day to serve others. In the healthcare industry, Kjersten gets to see her work through and enjoys the process of continuous improvement. Building teams have not changed much, some methods of work differ and where the work is performed. For example, information security has grown significantly to evolve with the ever-changing advancements in technology. Later, Kjersten explains how she builds a team, what diversity means, data strategy, data governance, and financial impacts. 

In This Episode:

• [00:20] About Kjersten Moody

• [04:45] Love for data

• [06:40] Transition to technology consulting

• [09:50] Lessons learned early on

• [13:15] Leadership took the time

• [14:40] Kjersten’s leadership style

• [15:35] Transition to healthcare

• [18:00] Lessons learned in consulting

• [20:00] Building teams

• [22:15] Qualifications for individuals

• [29:10] Data strategy 

• [33:00] Data governance

• [38:00] Understanding the business aspects 

• [45:20] Financial impacts

• [48:20] Listener questions

Some of Kjersten's quotes from the episode:

 Now you can support Data Futurology on Patreon! 

https://www.patreon.com/datafuturology

Thank you to our sponsors:

JCU Master of Data Science - Online Program

Fyrebox - Make Your Own Quiz

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

In this episode I talk to Matt Kuperholz. Matt currently works for PWC as a Partner in their Analytic Intelligence Area and is their Chief Data scientist. As a kid, Matt was fascinated by computers and while training to be an actuary started developing his computer science skills. This led to working as a data scientist and consulting with top tier companies.

In this episode Matt and I talk about his career journey, why it’s important to focus on the real world and not just the data and how data science can be integrated into businesses. We discuss the concept of responsible AI and why the exponential growth of technology is making for an interesting world.

With a background in both actuary and computer science, Matt has been working with data for over 20 years. He ran his own company in the early 2000s which included working with Deloitte Australia as they started to look at how to use data science in their business. He is now a is a partner and chief data scientist at PWC Australia. An expert in planning, executing and communicating the results of advanced analytics projects, Matt’s area of specialisation is the application of artificial intelligence and machine learning technologies to detailed and complex data.

Summary

· Matt’s love for computers and he he got to where he is now (00:12)

· How Matt’s interest in computers led to a love for data (06:28)

· Matt’s interest in martial arts and why a diversity of people matters (08:19)

· Smell-testing the quality of a number, and the importance of attention to detail (09:40)

· Working with limited time on a mainframe and how Matt coped with limited resources (12:09)

· The early days of using AI and what it was like working in a start-up in the late 90s (15:04)

· The importance of well prepared data (16:56)

· How Matt keeps up to date with data and technology (21:17)

· How Matt chooses what problems to tackle (23:26)

· What it was like working with Deloitte (26:03)

· How data can integrate into other areas of a business (28:32)

· Starting with the real world problem before focusing on the data (30:26)

· A recent project Matt has worked on exploring what trust looks like in a digital world (35:11)

· The idea of responsible AI and how we develop checks and regulation (41:41)

· How technologies are growing exponentially and causing a fast changing world (49:45)

· How Matt follows his curiosity and how this has led to opportunities (52:05)

· Why the data industry is worth getting into (54:48)

· The importance of finding what you are into and staying true to yourself (55:53)

Connect:

Twitter - https://twitter.com/datafuturology

Instagram - https://www.instagram.com/datafuturology/

Facebook - https://www.facebook.com/datafuturology

Now you can support Data Futurology on Patreon! 

https://www.patreon.com/datafuturology

Thank you to our sponsors:

JCU Master of Data Science - Online Program

Fyrebox - Make Your Own Quiz

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

In this episode, I talk about data scientists and ways you can attract the best talent to your team. Instead of telling your employees what they can do better, make them curious as to what they could do better. Then, I reveal the three things to look for when analyzing your pool of applicants. Once you have your team, now what? Once you have a decent pay settled, I explain the three things you will need to have for a capable team. Later, I tell you the elements, as a manager, you should be doing as rarely as possible.

In This Episode:

• [02:45] How to attract data scientists to your team?

• [04:45] The three things to look for from your pool of applicants

• [07:05] Adversity; test how they would react 

• [11:00] Three things needed to run an effective team

• [18:00] Managers should be doing this as rarely as possible

Creating a Data Team Session Quotes:

1. “Create a learning environment and continually challenging projects to focus on their development.”

2. “People should be open-minded and willing to learn; I test this in two different ways.”

3. “A lot of people come with technical skills from other countries.”

4. “They had to code it live with about eight people watching them, no pressure!”

5. “You know the answer, and you want to tell them to get to the outcome quickly. That’s an urge you have to roll back and fight against.” 

6. “Purpose is really what gets us out of bed every day.”

7. “Make yourself redundant as quickly as possible.”

Resources Mentioned: 

Drive: The Surprising Truth About What Motivates Us

Connect:

Twitter - https://twitter.com/datafuturology

Instagram - https://www.instagram.com/datafuturology/

Facebook - https://www.facebook.com/datafuturology

Support Data Futurology on Patreon! 

https://www.patreon.com/datafuturology

Thank you to our sponsors:

JCU Master of Data Science - Online Program

Fyrebox - Make Your Own Quiz

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!

In this episode I talk to Kristen Sosulski who is the Data Visualization Professor at NYU Stern School of Business. She has just written the book Data Visualization Made Simple: Insights Into Becoming Visual. An interest in using technology to help students learn has led to helping people to understand how to use data visualizations to communicate insights to others.

Kristen and I discuss guidelines on creating data visualizations, why presenting data visualizations is as important as creating them, and how the software needs to improve. 

Dr Kristen Sosulski is an Associate Professor of Information Systems at New York University’s Stern School of Business. She teaches MBA, undergraduate, executive, and online courses in data visualization and computer programming. She is also the Director of the Learning Science Lab for the NYU Stern where she leads teams in design immersive learning environments for professional business school education. 

Summary

• Kristen’s journey from doing her undergraduate in Information Systems at NYU Stern School of Business to being a professor there teaching Data Visualization (00:17)

• How Kristen’s love of technology led to an interest in using technology to help students learn (01:38)

• The challenges of trying to create an immersive learning environment in the late 90s (02:41)

• What led to Kristen working with data visualization (03:38)

• How Kristen thinks about data visualization and designing data graphics (06:14)

• Some guidelines and thoughts on presenting data to an audience (08:03)

• How people learn to improve their data graphics (11:15)

• The importance of showing your work and getting feedback (14:18)

• The challenges Kristen finds when consulting for companies in data visualisation (17:08)

• The value of data visualization in a data driven organisation (19:54)

• Why Kristen wrote her book on data visualization and why she included case studies (21:14)

• Some resources that Kristen created for the book (23:40)

• Her work in building NYU’s online education and the use of learning analytics (27:11)

• Why there needs to be more training in how to visualize data and to understand what it means (30:10)

• Designing a dashboard for user driven storytelling (33:41)

• How Kristen would like data visualization to evolve in the future (36:44)

• Mistakes people make when creating visualizations (38:51)

• How Kristen developed and improves her work and the value of sharing your mistakes (41:33)

• The importance of understanding what your data means in the real world (42:49)

Links

Data Visualization Made Simple: Insights into Becoming Visual by Kristen Sosulski

https://www.amazon.com/Data-Visualization-Made-Simple-Insights/dp1138503916

The Online Certificate in Visualizing Data

Taught by Kristen Sosulski via NYU Stern School of Business

https://www.stern.nyu.edu/programs-admissions/online-certificate-courses/visualizing-data

Support Data Futurology on Patreon! 

https://www.patreon.com/datafuturology

Thank you to our sponsors:

JCU Master of Data Science - Online Program

Fyrebox - Make Your Own Quiz

A lot of listeners have asked what have been my takeaways from the 30+ discussions with the guests on this podcast so far. To launch 2019 I’ve done a look back at all episodes from 2018. This is part 2 where I discuss episodes 19 to 34.

I hope you enjoy my recollection of these conversations. I’d love to hear what were your favourite takeaways!

Support Data Futurology on Patreon! 

https://www.patreon.com/datafuturology

Thank you to our sponsors:

JCU Master of Data Science - Online Program

Fyrebox - Make Your Own Quiz

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!

A lot of listeners have asked what have been my takeaway points from the 30+ discussions with the guests on this podcast so far. To launch 2019 I’ve done a look back at all episodes from 2018. This is part 1 where I discuss episodes 1 to 18.

I hope you enjoy my recollection of these conversations. I’d love to hear what were your favourite takeaways!

Support Data Futurology on Patreon! 

https://www.patreon.com/datafuturology

Thank you to our sponsors:

JCU Master of Data Science - Online Program 

Fyrebox - Make Your Own Quiz

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!