Data Futurology - Data Science, Machine Learning & Artificial Intelligence From Top Industry Leaders
Summary: Data Futurology is data from a human lens. In Data Futurology, experienced Data Science Leaders from around the world tell us their stories, challenges and the lessons learned throughout their career. We also ask them: - What makes a great data scientist? What skills are required? - How to become a great data science leader? - How should I grow and get the most out of my team? - What is a good data strategy? and how do I best implement it? - What are interesting applications of ML/AI that I should be considering in my industry? To find out more visit www.datafuturology.com
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. Also, Michael explains some software engineering challenges he faced during his time in industry and solutions he ended up needing to be successful. 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@example.com 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 firstname.lastname@example.org 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: “Challenges are a constant in a domain such as data science.” “Diversity is an attribute of the team. It’s the diversity of experiences, culture, and thought.” “The process of matching price to risk is inherently done through data.” “Data strategy is interpreted in many different ways.” “The leader needs to be able to work in a trusted way with business leaders and general managers.” 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!
Sally is the General Manager of Insights at the Australian Motoring Services. She previously spent 10 years working in banking and today she shares her story. We speak about: * Fraud analytics in big banks * End to end analytics * Importance of fast feedback loops * Shocks of early working life * Balancing speed & accuracy * 80/20 vs 95/5 * Exposures in strategy & politics * Helping the business ask the right questions * Leading with the work * Career breaks: how to * Importance of working on yourself * Advantages of medium sized companies * Creating a data strategy * Balancing tactical solutions, strategic initiatives and team development * Self service analytics * Educating business stakeholders & getting their feedback * Ability to ask anything from everyone * Data science is like medicine * Leveraging multiple dimensions for career development * Knowledge sharing sessions * Getting analytics a seat at the table Show notes: www.datafuturology.com/podcast/34 Sally is based in Melbourne, Australia 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!
Graeme started in actuarial science and developed a love for algorithms and automation. He worked in data warehousing before moving into data analytics. He spent 16 years in several Head of Data roles at The Automobile Association (AA) before joining Addison Lee as their Chief Data Officer, where he is today. We speak about: * What is actuarial science * Data warehousing & GIS systems * Overview of the Chief Data Officer role * Automation in the data space * How to build a data warehouse * The difference between a data warehouse, data lake and virtual data warehouse * Starting data work with business problems/questions * How to deliver value to the business * Balancing tactical project delivery with strategic work * Enabling self service data analytics * Prioritising & sizing up work * Modern styles of work in data * Data governance: creating a plan * Creating a data strategy * How to get to a head of role * Team building * Networking Show notes: www.datafuturology.com/podcast/33 Graeme is based in London, Greater London, United Kingdom 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!
Carole had an unusual path into data science. She's worked as a content project manager, in strategic planning and in sales before getting into data through Business Intelligence at Fyber where she eventually became their Head of Analytics. Today she is the Head of Data Science & Analytics at Tenjin. We speak about: * The strengths of being a generalist * Upskilling throughout your career * Focus on self service reporting * The skills needed in a BI team * Creating internal user groups to share knowledge * Convincing people to get training on the tools required to do their job better * The benefits of gaining a reputation internally * Setting a strategy for data teams * The importance of data modelling skills in data teams * Learning technology on the job when you're background is not technology * Monthly meeting with key departments to review all dashboards in the department * Working remotely in global companies * Metrics about user behaviour * Offering analytics for many customers with the same problem/need * How to develop consulting skills * The platinum rule - book on communication style * The leadership challenge - book recommendation * What it's like working in startups * How to recover from being a workaholic Show notes: www.datafuturology.com/podcast/32 Carole is based in Berlin Area, Germany 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!
Scott started his career pushing trolleys at Woolworths. In his career he rose to management levels in retail with Woolworths, consumer goods with Kraft Foods, Fonterra SPC and PZ Cussons, then in media with 21st Century Fox. He then became the CEO of iSelect, a role he left earlier this year to start his own AI company Wilson AI. We speak about: * Focus on customer needs * Digitising industries to access more data * Helping companies in multiple industries to begin their data analytics journey * How to differentiate your company when competitors have access to the same data * How to overcome being "data rich but insight poor" * Changing industry power dynamics through data * Creating new teams to create value from data * The importance of storytelling in data science * Defining objectives with your data analytics communication * Educating industries to use data more effectively * Understanding costs & priorities across the value chain to make better decisions * Eliminating your biases when dealing with customers * Process re-engineering & AI * How to think outside of the building * How to start an AI company * The importance of translating between business and technical * How to connect data science and the boardroom * The importance of data science education in an organisations journey * How to achieve a wider spread adoption of AI * Focusing on cost & revenue with data science for maximum impact * Resist the urge to boil the ocean * The role of a CEO in a publicly listed company * Focusing on the top 3 business priorities * Productionising AI & monitoring unintended consequences Show notes: www.datafuturology.com/podcast/31 Scott is based in Sandringham, Victoria, Australia 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!
Aaron started his career working in accounting and building management information systems (MIS). He had his own company, worked in multiple industries and then got into biology and genomics. Today he is the Chief Data Officer at the Inova Translational Medicine Institute. We speak about: * How to take research into scaled applications * The importance of sharing your knowledge and helping others understand * Why you're only as good as your team members * How to engage many different types of stakeholders * Challenges of data management in healthcare * Data governance & provenance in healthcare * Data monetization & it's stigma in healthcare * The benefits of data sharing consortiums * The potential of genomic & DNA data * Handling algorithm biases * Enabling reproducible research through data * Why "perfection is the enemy of good" * The importance of creating & sharing your mental models Show notes: www.datafuturology.com/podcast/30 Resources: Weapons of Math Destruction https://weaponsofmathdestructionbook.com Evernote https://evernote.com Real time board https://realtimeboard.com Mind jet - mind mapping https://www.mindjet.com Aaron is based in Washington DC Metro Area, USA 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!
Klaus started his career doing internships at Yahoo! and the port of Hamburg. He worked as a consultant and completed a PhD in Quantitative Marketing. Today he is the Chief Analytics Officer at YAS.life We speak about: * The importance of getting applied experience as early as possible * Defining KPIs for businesses * Using data to change organisational behaviour and increase safety * How to navigate organisations to create data definitions * Realities of consulting: positives and negatives * Why large companies require so much custom work * How to help people and organisations that don't know what they want * Helping organisations in progressing through their analytics journey * How to overcome technical challenges with creative solutions in your projects * Why honesty within yourself and others is imperative in your work * How to provide customers what they need instead of what they want * The importance of hard and soft metrics when measuring value * Applying soft skills in data science * How to find what will be valuable for your customers * Expanding your interest with a postgraduate degree * How your social surroundings affect your purchase decisions * Using soft skills for data acquisition * What is eigenvector centrality and what is it used for? * How product reviews influence your buying decisions * How to create experiments in business * Pricing models in the steel business * Data science in fitness startups Show notes: www.datafuturology.com/podcast/29 Klaus is based in the Berlin Area, Germany. 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!
Jennifer started her career as a particle physicist before becoming a data scientist. After gaining experience in many fields including high frequency algorithmic trading & advertising, she was Atlassian's first Chief Data Scientist. Today she is the VP of Machine Learning at Figure Eight and an Expert and Advisor at the International Institute for Analytics. We speak about: * How to see the results of your work sooner and faster * The importance of choosing your manager * Making data strategy decisions for companies that are very immature in their approach to data * Building data science teams from scratch * Combining impostor syndrome and leaps of faith for your benefit * The importance of making mistakes to be successful * What having a great data culture really means * How to convince peers and supervisors on the benefits and the path of data strategy * Differences between having a technical and non-technical manager * Combining technical abilities and business sense * The importance of customer contact for technical people * Focus on the impact and outcome of everything that you're building * How to keep the balance in teams * Pleasing customers vs product intuition * How to drive and create a data driven culture * How to create scale with your data science efforts * How to build your data science team * Data engineering vs Machine learning engineer * How to keep talent * How can data scientists learn the skills for business leadership * Active learning and building products for data scientists Show notes: www.datafuturology.com/podcast/28 Jennifer is based in Mountain View, California 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!