#56 Every Business is an AI Business with Dr. Eric Daimler – Serial Entrepreneur, Technology Executive, Investor and Policy Advisor

Eric Daimler.jpg

Dr. Eric Daimler is an authority in Artificial Intelligence & Robotics with over 20 years of experience in the field as an entrepreneur, executive, investor, technologist, and policy advisor. Daimler has co-founded six technology companies that have done pioneering work in fields ranging from software systems to statistical arbitrage. Daimler is the author of the forthcoming book Every Business is an AI Business, a guidebook for entrepreneurs, engineers, policymakers, and citizens on how to understand—and benefit from—the unfolding revolution in AI & Robotics. A frequent speaker, lecturer, and commentator, he works to empower communities and citizens to leverage AI & Robotics. For a more sustainable, secure, and prosperous future.

In this episode, Eric explains how he has a vivid memory of getting a computer at the age of nine. He loves the machine, and even at such a young age saw the freedom a computer allows. Early in his career, Eric knew he wanted to work with brilliant and motivated people. When he was in New York, he saw the Netscape browser and instantly recognized the world was going to change. This inspired him to get out and find opportunities on the west coast.

Eric’s most significant failure as an investor was with a sports company. It was an idea of aggregating the worldwide demand for niche sports into an audience on the web that would allow for more significant marketing dollars. It was a fantastic idea and seemed like the appropriate time to go for it. One of his biggest takeaways from being an investor is that timing matters a lot. The bandwidth wasn’t there, so the experience ended up being quite weak.

There is a great deal of money looking to chase the next big thing. If you are looking for a house in a new city, and someone outbids you, you will lose the house. That doesn’t mean you aren’t going to move; it just means you need to start looking for a new home. If you receive a pitch and then some name brand firm takes it away from you, that doesn’t mean you can’t work in that field anymore. Now you have done your due diligence, you understand the market better and can look for other investment opportunities in the area.

AI is a system. We have to embrace this technology in its totality, the survival of our species depends on it. We have famously been able to survive to 2019.  There was a prediction back in the 1800s that we would have starved by now because the population was growing faster than our food production. Increase in productivity comes from technology and automation. When Eric is speaking, he likes to ask the audience what comes to mind when he mentions the term AI. People just don’t know what the word means. One of the critical issues that need to be addressed when companies employ AI is the recognition that our understanding of technology may change. Even the meaning of data has changed over the last year. Later, Eric explains how people have a long way to go regarding embracing AI, how technology is making driving easier, and AI in the medical field.


Enjoy the show!


We speak about:

  • [02:10] How Eric started in the technology space

  • [05:15] Moving from one career path to another

  • [09:50] Eric’s most significant failure as an investor

  • [13:30] Picking the timing  

  • [18:15] AI is larger than what currently exists  

  • [21:30] Embracing the technology behind AI

  • [29:45] Hurdles for companies who are adopting AI  

  • [41:30] Reactions from people learning about AI

  • [48:40] Shortage of truck drivers + how technology is making driving easier

  • [54:00] AI in the medical field

  • [61:30] Using a categorical approach  


Resources:

Eric’s LinkedIn: https://www.linkedin.com/in/ericdaimler/

Eric’s Twitter: https://twitter.com/ead

Website: http://conexus.ai/


Quotes:

  • “Most people get the timing very wrong.”

  • “I am starting to look at investments where others are not looking.”

  • “I wanted to change the Hollywood narrative around AI.”

  • “We were naive about our privacy in 2016.”


Now you can support Data Futurology on Patreon!  

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Thank you to our sponsors: 

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Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au 

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Dr. Eric Daimler is based in San Francisco, California.


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



#55 Martin Ford – Author and Futurist

Martin Ford.jpg

Martin Ford is a prominent futurist, New York Times bestselling author, and leading expert on artificial intelligence and robotics and their potential impact on the job market, economy and society. His 2015 book, "Rise of the Robots: Technology and the Threat of a Jobless Future" won the Financial Times and McKinsey Business Book of the Year Award and has been translated into more than 20 languages.

In this episode, Martin discusses his best-selling books and describes some of the themes he writes about. For instance, in Rise of the Robots he talks about “The Triple Revolution” which was a report presented to U.S. President Lyndon B. Johnson fifty years ago that argued this would be a dramatic change to the economy; however, it never really panned out. Martin’s argument for artificial intelligence started back in 2009 after writing his first book titled The Lights in the Tunnel. Ultimately, artificial intelligence will become so powerful that it can have a significant impact on employment that will compete with a large fraction of the workforce.

Then, Martin discusses the impact of jobs, the economy, privacy, and democracy with the influx of automation. There was a recent announcement from OpenAI stating they have created a sophisticated deep-learning system that was able to generate narrative content. In other words, the system can create reviews, articles, and poetry proving AI can be creative. The company withheld the technology because of fear people would use the system to turn the internet into garbage full of fake news and fake reviews.

Later, Felipe and Martin discuss the most common occupational error, driving. Self-driving cars will threaten Uber drivers, taxi drivers, and truck drivers. It may take a bit longer than some people are saying, the technology will be coming within ten to fifteen years. Martin believes the hardest jobs to automate are in three categories: genuine creativity, relationship-building, and skilled trade jobs. We put so much emphasis on going to college; however, the safest jobs are going to be the electrician and plumber. Many people do not thrive at university, half of them are not finding jobs that leverage their education after graduation. Then, Martin explains universal income; giving everyone a minimal level of income to allow them to survive in the absence of a traditional job that would provide money. Stay tuned to hear Martin discuss deep learning, data banks, and the negative implications of artificial intelligence.

Enjoy the show!

We speak about:

  • [02:50] Martin’s background

  • [05:45] The themes behind Martin’s writing

  • [08:35] Machine learning is when algorithms can make decisions  

  • [12:00] Amazon is susceptible to automation

  • [16:45] The most common occupation error is driving some kind of vehicle   

  • [18:15] The type of work that will be left for humans  

  • [21:45] Universal basic income  

  • [28:55] Building explicit incentives to earn more income; paying people more to pursue education

  • [33:25] Artificial intelligence will be the primary force shaping our futures

  • [38:35] The solution is not to teach everyone how to code

  • [41:30] Architects of Intelligence: The truth about AI from the people building it

  • [46:00] Deep learning is the biggest thing to happen to artificial intelligence  

  • [52:20] Controlling data and an entirely new industry called data banks

  • [53:15] Negative implications of artificial intelligence

  • [64:40] You do not want to be doing something predictable

Resources:

Martin’s Website: https://mfordfuture.com/about/

Martin’s LinkedIn: https://www.linkedin.com/in/martin-ford-5a70428/

Martin’s Twitter: https://twitter.com/MFordFuture

TED Talk: https://www.ted.com/talks/martin_ford

Books:

Rise of the Robots: Technology and the Threat of a Jobless Future

Architects of Intelligence: The truth about AI from the people building it

The Lights in the Tunnel: Automation, Accelerating Technology and the Economy of the Future

Quotes:

  • “It would be a huge mistake to assume only blue-collar workers will be impacted.”

  • “Education is not going to be enough in the long run.”

  • “The last thing we need is a dumbed down population and less informed voters.”

  • “I believe artificial intelligence will be the best thing to happen to humanity.”

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 will@datasourceservices.com.au 

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Martin Ford is based in Sunnyvale, California.

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


#54 Annie South – General Manager, Data

Annie South.jpg

Annie South is the General Manager of Data at ME Bank. She is an Information Management professional with twenty years’ experience of complex information environments spanning the full spectrum of structured data to unstructured information. Annie has in-depth technical knowledge of various specialisms, including metadata, data warehousing, data governance, data quality, enterprise architecture, data lineage, Big Data, data analytics, and regulatory requirements.

In this episode, Annie explains the things she does to ensure her career is future ready because nobody can predict what jobs will look like years from now. Do not specialize in a particular technology but specialize in a capability. The technologies that you are using today will not be the technologies they are using tomorrow. If you specialize in a particular technology set, and it is decreasing in popularity, you will end up with fewer opportunities in the market. Annie tells people wanting career advice that when people look at your resume, they are looking for a consistent arc. That could mean staying consistent in an industry or constant engagement in the workforce. Another thing Annie looks for in applicants is kindness, this quality is something that cannot be taught.

To have any sort of success in an organization, you need to have immersed yourself in their environment for a significant amount of time. It has taken Annie a lot of time to grasp her organization’s culture and be able to manage her team efficiently at ME Bank. Annie refers to her team as the librarians, and data scientists are the people who come and read the books. Data is all about human beings being paired with information and knowledge; it helps people live better lives by pairing them with relevant and essential tools. Annie strongly encourages data scientists to follow their passions and continue to look at different opportunities in the field. Later, Annie discusses diversity in the workforce, overcoming discrimination in the workplace, and explains the importance of LinkedIn for social networking.

Enjoy the show!

We speak about:

  • [01:20] How Annie got into the world of data

  • [10:00] Insight for people starting in the data space

  • [12:50] Organizations are not predictable  

  • [14:50] Annie’s team at ME Bank

  • [27:50] Turning recruitment on its head

  • [33:20] Transitioning from teaching to general manager

  • [39:05] Sort out your personality and experiment with leadership

  • [46:30] Imposter syndrome  

  • [49:10] Experimenting with diversity in the workforce

  • [53:30] Challenges with discrimination in the workplace   

  • [61:10] Define yourself; do not be defined by others

Resources:

Annie’s LinkedIn: https://www.linkedin.com/in/annesouth/

ME Bank: https://www.mebank.com.au

IT Jobs Watch: https://www.itjobswatch.co.uk


Quotes:

  • “You are not going to be able to predict the roles that will exist in twelve months.”

  • “Sometimes, a job is better than no job.”

  • “Organizations are extremely complex, quite random chaotic things.”

  • “You will never get ahead by being excellent at what everyone else is doing.”

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 will@datasourceservices.com.au 

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Annie South is based in Melbourne, Australia.

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


#53 Pavel Pleskov – Data Scientist & Kaggle Grandmaster

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Pavel Pleskov is a data scientist at Point API (NLP startup) and currently ranks number 3 out of 109,624 on Kaggle, making him a Grandmaster. Pavel has started companies in the past and has worked in many different industries before becoming a data scientist and Kaggle Grandmaster.

In this episode, Pavel explains his background and how he started in the data science space. When Pavel’s girlfriend went to pursue her master’s degree in London, Pavel began interviewing for a quantitative research job nearby. Turns out, the company was a rival of his current employer, causing him to get fired from his job the next day. Former employees of this job contacted Pavel to ask if they would join their new trading firm and be head of their research team. After doing his job for two years, Pavel knew he was capable of doing it on his own. The company works remotely, and after spending time in bitter Russian winters, Pavel looked to work elsewhere. The ideal country turned out to be Vietnam and was Pavel’s first time outside of Russia.

Then, Pavel started taking machine learning courses and found the platform Kaggle to practice what he has learned. Practically, Kaggle is much more helpful than homework or any online course. The most significant part of his success was spending full-time on Kaggle rather than working another job. Then, Pavel explains the difference between machine learning engineers and researcher data scientists. Later, Pavel reveals his intention behind becoming the very best at his work. The key to success is to find something you like and become the very best at what you are doing. If you are doing something that you do not enjoy, then there is no way you will become the very best. The only way to overcome your obstacles is to enjoy what you are doing.


Enjoy the show!


We speak about:

  • [01:50] How Pavel started in the data space

  • [09:50] Vietnam is an ideal space for working remotely and teaching English

  • [21:40] The moment Pavel found Kaggle

  • [24:20] How Pavel became a data scientist

  • [28:00] Difference between machine learning engineers and researcher data scientists

  • [31:50] Why is it essential to be the very best?

  • [34:20] Machine learning and mathematics

  • [36:45] The early days of Pavel’s Kaggle journey

  • [40:00] Pavel’s favorite part of Kaggle

  • [47:20] The role of automation in Kaggle   

  • [49:40] The steps when approaching a new Kaggle competition

  • [52:55] Think twice before you commit to data science


Resources:

Pavel’s LinkedIn: https://www.linkedin.com/in/ppleskov/?originalSubdomain=ru

Pavel’s Kaggle: https://www.kaggle.com/ppleskov

Pavel’s Twitter: https://twitter.com/ppleskov

Quotes:

  • “It is tricky to work remotely as a data scientist.”

  • “It takes a lot of time to find your passion in life. People spend too much time on things they do not enjoy.”

  • “When I first started Kaggle, I entered any competition and tried to improve my score on the leaderboards.”


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 will@datasourceservices.com.au 

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Pavel Pleskov is based in Moscow, Russia.


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



#52 Influencing Marketing with Data Science with Danielle Timmins – Chief Data Analytics Officer

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Danielle Timmins is the Chief Data Analytics Officer for Free Range Creatives. Free Range Creatives is a digital marketing agency that is deeply rooted in data and analytics. They have a different view on agency life and challenge the existing ways of working. They believe that work should be fun (well, at least most days) and that our work must be insightful, inspirational and effective.

In this episode, Danielle tells us how she did not start in the data space but initially wanted to be a doctor. Danielle ended up getting a Master’s in Economic Psychology, during which she concentrated on the digital side of marketing. This is where Danielle got her exposure to data and started to understand it. Danielle got her first start at an NGO in a marketing position. She would shoot mini-documentaries for television and then moved into a more traditional marketing role. Danielle’s first job as a strategist was down in South Africa where she worked with several different clients. This is when she would start to work with data and incorporate it with strategy.

Danielle says that her most significant learning curve has been communicating related data information to people that do not understand data. She recalls how she was in a meeting and wondered what to do with the numbers that were presented and challenged people on what the data meant and how it was presented. Danielle says that these questions were not really asked at the beginning and said that they only came about once she got into a higher position. She loved sharing how she got to the data and the process, but she learned that most people do not. This opened her eyes to make sure that data is broken down into something exciting and easy to understand.

Later, Danielle explains how her style is more visual than number driven or a standard PowerPoint, she uses a TED Talk presentation. This method utilizes visuals that create excitement; the brain works better with visuals. Usually, there are specific questions that we are trying to answer, and we can help answer these questions with visual stories. There are multiple presentations for different audiences, and you must know this to tell the visual story.

Enjoy the show!

We speak about:

•    [01:45] How Danielle started in the data space

•    [03:20] Background and career  

•    [06:20] Deciding what problems to tackle first on the job

•    [08:35] Evolution of marketing   

•    [13:35] Favorite failure

•    [16:50] How to communicate data

•    [18:30] Visual presentation style

•    [19:45] How Danielle creates a story

•    [21:30] How do you structure visuals for executives?

•    [23:10] How do you think people can get better at this skill?

•    [24:45] What is a strategist for data?

•    [27:40] What is the role outside of data?

•    [29:00] The main challenges for Danielle’s clients

•    [32:30] Working with clients on case-by-case basis

•    [33:30] Qualities of a great data scientist  

•    [35:30] What do you think makes a good data leader?

•    [36:15] Current challenges in the data space

•    [37:40] Future challenges for the data space

•    [42:40] Advice for future data scientists and leaders

Resources:

Sexy Little Numbers

Free Range Creatives: https://www.freerangecreatives.co.za/

Danielle’s LinkedIn: https://www.linkedin.com/in/danielletimmins/

Quotes:

•    “There are many different audiences, and you must adapt your story for each one of them.”

•    “Data can sometimes be abused during storytelling, but I hope that over time, this will change and only be used to help support someone.”

•    “All audiences need to be comfortable with what we are doing as data analysists and sharing their work.”

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 will@datasourceservices.com.au 

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Danielle Timmins is based in Essen Area, Germany.

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


#51 Peter Elger – Founder and CEO

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Peter Elger is the founder and CEO of four Theorem; his focus is on delivering business value to his clients through the application of cutting edge serverless cloud architectures and machine learning technology. His experience covers everything from architecting large-scale distributed software systems, to leading the internationally-based teams that built them.

In this episode, Peter tells us how his first real passion was in physics. After graduating with a BSc in Physics and a master’s degree in Computer Science, he worked for several years at the Joint European Torus (JET), the world's largest operational magnetically confined plasma physics / nuclear fusion experiment. They were doing big data, but at the time they did not refer to it as such; they dealt with around four to five terabytes of scientific data. Peter then transitioned to Indigo Stone as a Senior Technical Architect. Indigo Stone was a software disaster recovery firm which exited in 2007 to EMC.

Peter explains how it is essential to keep your technical skills up-to-date and why some of his favorite days are when he gets to code despite being the CEO of his company. If you can actually be the bridge between the business and the technology, you are an invaluable asset to any company. The freedom to innovate is what led Peter to his entrepreneurial ventures; previously, he had no real experience being his own boss. Peter says it is dangerous to think you can do everything; you have might a broad skill set, but you need to recognize that you have gaps. This is why Peter has always started businesses with co-founders. Currently, his co-founder is a world-class technologist and someone who understands the human dimension. All of his current co-founders and people Peter has worked with previously.

Later, Peter explains why he wrote about applying AI to existing platforms in his book, AI as a Service. He also recently wrote a chapter on how to apply machine learning to existing systems and the patterns that arise within two systems. Then, Peter describes why you do not need knowledge of AI to use AI as a service. In the past, everyone wanted their own security department; however, people already have the same technology that is vastly better than your own. This applies to how you can utilize AI within your existing business systems. Peter wants people to be interested in machine learning, but only a basic understanding is essential to use it within a business context. Finally, Peter discusses his approach to finding the most capable team members, being open and upfront with all staff members, and removing the ego to allow his team to perform their best work.


Enjoy the show!


We speak about:

  • [01:45] How Peter started in the data space

  • [06:50] Transition to disaster recovery  

  • [08:55] Interactive radio and marketing applications

  • [13:40] Maintaining a grip with technical skills

  • [16:20] The entrepreneurial bug came organically to Peter

  • [18:40] Transition to entrepreneurship  

  • [21:45] What to look for in a co-founder

  • [26:00] Building analytics with machine learning

  • [29:00] A tale of two technologies

  • [33:00] Applying AI to existing platforms

  • [35:10] Knowledge of AI is not necessary to use AI as a service  

  • [37:50] Capable team members are difficult to find

  • [40:10] Sharing management meetings with all staff members  

  • [44:05] Experiences with handling politics in organizations

  • [48:50] Removing ego + allowing the team to do their best work

  • [50:30] Scheduling work to maximize the impact


Resources:

Peter’s LinkedIn: https://www.linkedin.com/in/peterelger/?originalSubdomain=ie

Peter’s Twitter: https://twitter.com/pelger

Peter’s Book: AI as a Service


Quotes:

  • “Computers don’t lie, and they do what you tell them.”

  • “In order to sell to someone, you need to understand what their problems are.”

  • “Business is all based on delivering value and building relationships.”

  • “Good software engineers have been doing things before they had a name.”


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 will@datasourceservices.com.au 

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Peter Elger is based in Ireland.



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



#50 Prakash Baskar – Founder and President

Prakash Baskar.jpg

Prakash Baskar is the Founder and President of Khyanafi. He helps data leaders to rapidly transition and accelerate the success of data, analytics, and digital initiatives. Previously, Prakash was the Chief Data Officer at Santander Consumer USA where he led enterprise data governance, risk infrastructure & information (risk data aggregation), data quality, business data strategy & solutions, and business & reporting analysis functions.

In this episode, Prakash tells us how he started in the data space at his university. His role was to determine how students were performing. If they are not performing well, he needed to identify why. The graduation rates were low at the school, so Prakash was tasked with finding out what was the problem. Then, Prakash discusses starting a new job and having little direction about what to do. With everchanging technology, the description of your job will always be changing too. As a person going into any role, understand that you do not have to ask permission all the time. Have a clear idea of what you can do and what you cannot do, then do what you feel is right for the organization. Look for where the opportunities for expansion are and find a way to get results.

If you ask ten people what the role of a Chief Data Officer is, you will get ten different answers. Whatever the CDO does will ultimately be to enable others to receive real benefits out of the data. Just because something is not broken, does not mean it cannot be improved. There are many different routes a person can take to become a CDO; however, you need someone with knowledge in multiple aspects of business, technology, and people management. A CDO needs to create value for the organization; learn the company you are supporting to anticipate the problems they may run into.

Later, Prakash explains how in business, any change is hard. How you embrace the change after it is made is what will differentiate yourself from others. If the change is too complicated, people will shut off. Start off by telling the client what the change will do for them rather than the steps it will take to get there. Some other tips when presenting a significant change is to be realistic with what it will take and make sure not to overpromise. It is imperative to select things that you can quickly do with minimal engagement from their people. Plus, make sure you have updates for the company each month, so they understand what is being revealed from the data. Finally, Prakash discusses how essential it is to move around the organization in order to understand different departments and he reveals the inspiration behind his latest business venture.

Enjoy the show!

We speak about:

  • [01:30] How Prakash started in the data space

  • [03:55] The transition into consulting  

  • [07:50] Deciding what problems to tackle first on the job

  • [10:10] The role of Chief Data Officer  

  • [17:20] Creating value for the organization  

  • [22:10] Businesses getting the maximum benefit from analytical work

  • [31:25] How to determine what to work on first with a company

  • [39:00] Data science conferences are full of CDOs

  • [45:45] Actively moving around the organization

  • [50:20] The inspiration behind Khyanafi

  • [53:50] What do you think makes a great leader in the data space?

  • [55:40] Advice for data scientists   

Resources:

Prakash’s LinkedIn: https://www.linkedin.com/in/prakashbaskar/

Khyanafi: http://www.khyanafi.com

Khyanafi’s LinkedIn: https://www.linkedin.com/company/khyanafi/about/

Set up a discovery call with Prakash: https://calendly.com/prakashbaskar/discoverycall

Quotes:

  • “Your job description is only as good as the time it was written.”

  • “I am not a big believer in five or ten-year plans; I make plans six months at a time.”

  • “CDOs have to know the business! Try and learn how the company operates; you will gain more respect when you take the effort to understand the business.”

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 will@datasourceservices.com.au 

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Prakash Baskar is based in Columbus, Ohio.


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

#49 Becoming a Kaggle Competition Master with Valeriy Babushkin – Head of Data Science, Kaggle Competition Master (Top 60)

valeriy babushkin.jpg

Valeriy Babushkin is the Head of Data Science at X5 Retail Group where he leads a team of 50+ people (4 departments: Machine Learning, Data Analysis, Computer Vision, R&D) and increases profit in a 25+ billion USD company. Also, Valeriy is a Kaggle competition master; ranking globally in the top 60.

In this episode, Valeriy explains his background and how he started in the data science field. At one point, he received an offer for a senior position at a bank; it was the largest privately owned bank at that time in Russia. Valeriy did not find out that he was doing machine learning until working on it for two years. What someone is doing right now could be pretty close to machine learning, and they don't even know. Then, Valeriy speaks on how trust is essential to the job of a data scientist; not only between you and your boss but between you and other departments. Trust will make your job easier when explaining the data, the results, and how reliable they are for the company. However, if there is an existing data science department in the company, you will not have to work as hard to earn the trust of others because it already exists. Sometimes when data scientists join a company, they think their job will just be to code all day. That is not always the case, you will have to talk to many people and often be a business analyst.

Next, Valeriy discusses setting up teams in the data science space and how many people really need to be involved. For instance, if an algorithm is your product, you will need not only data scientists but product managers, project managers, and software engineers. If you are building the data science department, what do you need to grow? You will need to build a roadmap for the product and know how you want the company to improve. Then, Valeriy explains why reliability is one of the most essential qualities of an employee. For instance, if you have a critical task would you rather give it to an employee with a fifty percent chance of completing it in two weeks or someone who has a ninety-nine percent chance of handling it within six weeks? You will give it to the person that is more reliable, despite the fact it may take them a little longer to complete it. Later, Valeriy reveals the story behind his Kaggle journey and discusses some ethical challenges in the data science industry.

Enjoy the show!

We speak about:

  • [01:45] How Valeriy started in the data space

  • [06:10] Transiting to working at a bank

  • [11:30] Understanding the business process

  • [15:10] Gaining trust from clients

  • [20:20] Data scientists are business analysts

  • [24:10] Expectations from the job interview

  • [25:50] Starting data science teams

  • [31:40] The type of mindsets to look for in a team member

  • [37:30] Different teams complement each other

  • [40:20] Valeriy’s journey with Kaggle

  • [47:40] Ethical challenges in the industry

  • [51:20] Persistence is key

Resources:

Valeriy’s LinkedIn: https://www.linkedin.com/in/venheads/

Valeriy’s Kaggle: https://www.kaggle.com/venheads

Quotes:

  • “It makes sense to have a job interview which is pretty close to your daily routine, your daily work.”

  • “Before hiring new people, you have to make a path.”

  • “One of the most important qualities for employees to have is reliability.”

  • “It is impossible to work directly with two people if you just spend five minutes a day with each of them.”


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 will@datasourceservices.com.au 

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Valeriy Babushkin is based in Zug, Canton of Zug, Switzerland.



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



#48 Leverage What You Know to Get Your Foot in the Door with Jay Liu - Chief Data Scientist at Digital-Dandelion

jay liu.jpg

Jay Liu is the Chief Data Scientist at Digital-Dandelion specializing in helping insurance, and medical organizations innovate by integrating the latest in Artificial Intelligence (AI), machine learning and big data into their systems. Knowing the best way to learn is by putting your money where your mouth is, Digital-Dandelion launched an online brand and built a customer AI to promote it. There were numerous technical and modeling challenges that were overcome, but in the end, they sold all their stock within three months. They had proven to themselves that customer AI worked. Organizations can have great depth and breadth of customer data from their long-term relationships of selling high-value products and services.

In this episode, Jay explains how he found himself in advertising and started getting fat because of all the Michelin star restaurants his potential clients would treat him to. His data science career began with loyalty cards and being incredibility confident. When someone uses a loyalty card, the company is collecting data. They will know exactly what you purchased and how much you purchased of each item. The customer will be rewarded with monthly coupons. Jay was in charge of coming up with the coupons that were designed to make the customer spend more money in the store. Knowing at least one data programming language will leverage what you have and give you one foot in the door. The best way to get into data science is to know how it will improve the current industry or business you are working for.

Later, Jay explains why QA is a lost skill and the idea that great data scientists have internal discipline. However, there is a race to push the boundaries and become more automated. For example, Facebook collects as much data as possible and thinks about the consequences later. Data is data and people are people. Understanding data is the starting point. Before Jay starts a job, he dives deep and analyzes what every number means to the business with their data collection. Also, Jay considers how to make his bosses job as easy as possible. Overall, the success of his boss will create the most significant impact on his business. If someone has been working at the same job for ten years, they are scared to grow and try something new. Finding a data scientist who has worked at multiple different sizes and types of organizations is the key to finding a well-rounded employee.

Enjoy the show!

We speak about:

  • [01:40] How Jay started in the data space

  • [06:15] Loyalty cards

  • [08:50] QA is a lost skill

  • [10:25] You are your own police

  • [13:30] Ethical considerations

  • [15:10] Transition from marketing to data science

  • [20:40] Putting yourself on the line for the benefit of the company

  • [22:15] Creating change in organizations

  • [27:30] Learning new applications + algorithms

  • [33:50] What makes a great data scientist?

  • [38:15] Delivering results to alleviate pressure

Resources:

Jay’s LinkedIn: https://uk.linkedin.com/in/jay-liu-76ab2b8a

Digital-Dandelion: https://www.digital-dandelion.com

Quotes:

  • “If you want to get into data science, the best way to do it is to understand how data science can help your current industry.”

  • “I have actually never worked as a full-time employee for more than two years.”

  • “If you want to change, you need to be ready to fight for it.”

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 will@datasourceservices.com.au 

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Jay Liu is based in London, United Kingdom.


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

#47 Transforming Government Organisations with Data Science with Marek Rucinski – Deputy Commissioner, Smarter Data Program

Marek Rucinski 1.jpg

Marek Rucinski is the Deputy Commisioner leading the Smarter Data Program at the Australian Taxation Office (ATO). Marek has taken part and driven the evolution and transformation of Marketing, Analytics, Data and Digital capabilities for over 20 years. This has been done in both industry roles and consulting services capacity, across Australian, Asian and Global clients, across Retail, Telco, Consumer Goods, Financial Services, Mining & Utilities sectors. His passion centers on helping clients change the role of Marketing & Analytics capabilities in Digital and Data age, from activating the capability through acting on insights, to transforming customer experience and the whole business via delivering value across business functions. Prior to ATO & Accenture, Marek lead and created analytics functions and teams in a Retail industry, and developed global corporate strategy frameworks and analytics in a multinational organizations.

In this episode, Marek tells us about how he was always interested in the science behind marketing. Marketing as a discipline has been completely transformed due to the emergence of data as a driver for engagement with the customer. Marek is not a classically trained data scientist; he is a data strategist and can dive deep into the organization’s needs in order to drive value to the customer. Marek tells us how some businesses can struggle with how to handle the findings of research from data scientists. It is essential to translate the potential into targets to create the prize. Leave the ego at the door and find the ability to be critiqued.

Later, Marek tells us how educating businesses on analytics as a mechanical process is essential for them to perceive how the whole thing works. He then explains his transition from consulting to government and how his excitement lies in the play with analytics at an enormous scale. Then, Marek describes how to have each section of the value chain working with purpose and precision. Data has to be trusted, organized, and accessible for the company. A data strategist must consider how the data is being delivered to their client. You want to create products and interactive experiences for the business as opposed to simple spreadsheets. Finally, Marek answers the audience’s questions including what makes a good data scientist and current challenges in the data science industry.

Enjoy the show!

We speak about:

  • [02:10] How Marek started in the data space

  • [05:25] Activating the value from data

  • [08:30] POCs are essential  

  • [09:45] Find people who can create the vision

  • [12:00] Educating businesses on analytics

  • [16:30] Artificial intelligence + automation

  • [18:50] Transition from consulting into government

  • [20:20] Motivations for government work

  • [22:00] Future of ATO

  • [26:15] Continuous production of insights

  • [29:30] Audience questions  

Resources:

Marek’s LinkedIn: https://www.linkedin.com/in/rucinskimarek/

Quotes:

  • “Good results create more interest which in turn creates traction for new products.”

  • “If you engage the business regarding the value, but then you cannot deliver on the promise, it creates dissonance.”

  • “What separates great data scientists is their ability to communicate what the results actually mean.”

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 will@datasourceservices.com.au 

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Marek Rucinski is based in Sydney, Australia.


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



#46 Today is the Best Time to be a Data Scientist with Jonny Bentwood – Global Head of Data & Analytics

Jonny Bentwood.jpg

Jonny Bentwood – Global Head of Data & Analytics

Jonny Bentwood is the Global Head of Data & Analytics at Golin. Jonny is an innovative leader with 15+ years of experience in communications - winning, retaining and working for Fortune 100 clients such as Facebook, Unilever, Heineken, Barclays, HP and Microsoft. He has a proven record as a creator of pioneering solutions with ability to transform business to radically impact bottom line. Jonny presents complex information in an engaging and informative style and is a strategic consultant to executives using data to provide guidance on reputational and crisis issues and maximising marketing campaigns.

In this episode, Jonny tells a story about how MTV got in touch with him to apply data in figuring out who would most likely win The Apprentice. After being in the industry for over twenty years, he believes this is the best time to be in data. CMOS are spending more of their money than ever before on analytics. How do data scientist prove their value? People use data purely in a descriptive way. To succeed and bring value to clients, one needs to switch from describing the data to telling the customer what they need to do with the data. Set the goals of who, what, and why to figure out which message will be most useful before you even start. Take it a step further by using prescriptive data and make it predictive. This is where you study what will happen in the future. We are continually absorbing and understanding what things could happen and will happen. This opportunity is essential to identify issues before they occur and fix them.

Later, Jonny explains how understanding the customer requires a customer journey approach to increase marketing efficacy. Instead of doing random stuff, focus objectives with specific tactics and strategies. Something that gets on Jonny’s nerves is when people say it isn’t rocket science. Jonny wants people who do the research and figure out the information that counts. Then, we learn why organizations need to be data-driven. It is essential to train people and give them the technology to improve their jobs and become more efficient. Jonny challenges the status quo in his business. For instance, they have unlimited holiday, and their gender pay gap is positive to women.

Enjoy the show!

We speak about:

  • [01:30] How Jonny started in the data space

  • [04:50] Public relations

  • [06:00] Descriptive, prescriptive, and predictive

  • [08:15] Difference between interesting and useful

  • [10:00] Understanding the customer

  • [15:25] Cultural shift of data in organizations

  • [19:10] Challenging the status quo  

  • [22:40] Shiny object syndrome

  • [26:45] The twenty percent time

  • [30:00] Bringing data application to the masses

  • [34:30] Each stage of the customer journey  

  • [39:30] Getting value for money

  • [42:45] Return on investment

  • [44:15] Data + creativity  

Resources:

Jonny’s LinkedIn: https://uk.linkedin.com/in/jonnybentwood

Jonny’s Twitter https://twitter.com/jonnybentwood?lang=en

Quotes:

  • “To be truly smart you need to go from descriptive to prescriptive.”

  • “For a data scientist, the word interesting is one of the worst insults you can get. It has to be useful, what is the point you are trying to make?”

  • “There’s always going to be something else. What you need to do is focus on what you have.”

  • “Some of the best stuff has data infused with creativity.”



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 will@datasourceservices.com.au 

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Jonny Bentwood is based in London, United Kingdom.


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




#45 Mastering the Domain of Your Work Before Becoming a Data Scientist with Warwick Graco - Senior Director Data Science

Warwick Graco.jpg

Warwick Graco is the Senior Director of Data Science at the Australian Taxation Office (ATO). He has worked in defence, health, and taxation and has been involved in analytics for 25 years. He is a practicing analytics professional and is currently convenor of the Whole of Government Data Analytics Centre of Excellence and is a senior data scientist in Data Science and Special Acquisition Group of the Smarter Data Program of the ATO. He has a BSc from the University of New South Wales and a Ph.D. from the University of New England Australia. His professional interests include organisational innovation and learning, organisational decision making and analytics.

In this episode, Warwick tells us how he got started in data research the skills gained that led him to his successes today. Warwick explains why transparency is a business requirement for software and tools in the data science field. People with more analytical backgrounds will be more willing to accept an opaque solution over a transparent solution. When analytics was in the early stages, some organisations pushed back from data science; feeling they were on top of their portfolio and did not need any outside resources. No matter what results Warwick would come up with for these organisations, they would continue to have the same attitudes. Since 2010, there has been a shift in attitudes because data science has shifted from the background to the foreground.


Then, Warwick tells us the difference between good support and lousy support in the workplace. While Warwick was working with organisations, instead of providing results, he did the reverse. Ask the organisation what they want rather than telling them the findings. Providing the outputs clients wish to see led to incremental improvements built into their business intelligence reports. Warwick also explains why you can no longer be a data scientist; you will need to learn and master the domain of your work. For instance, Warwick learned everything about ophthalmology while working on data science with an ophthalmologist. Later, Warwick explains his process of publishing research, improving privacy concerns, and automated supports.

Enjoy the show!

We speak about:

• [02:20] How Warwick started in data science

• [05:55] Aptitude for research

• [08:40] Purpose-built software + decision trees

• [12:20] Accepting opaque solutions vs. transparent solutions

• [16:45] Pushback of data analytics

• [21:15] Difference between good support and bad support on the job

• [25:25] Necessity to learn the domain first

• [29:00] How to learn on the job

• [32:20] Process of publishing research

• [41:50] Improving legal and privacy concerns

• [44:25] Automated support + decision-making operations

• [52:40] Developing an analytical + practical mindset

• [58:10] Hyperspecialized

• [64:30] Moving toward data + analytics as a service

• [66:25] Advice from Warwick

Resources:

Datasource Services

University of New South Wales

Warwick’s LinkedIn: https://www.linkedin.com/in/warwick-graco-4a27044/


Quotes:

  • “Unless you have the support of those whom you are working for, your chances of success are probably fairly slim.”

  • “You can no longer just be a data scientist; you have to work in a particular area and build up the knowledge first.”

  • “I define an expert as someone who has profound knowledge in an area. They are very good at coming up with solutions to solve problems quicker than those without that deep knowledge.”

  • “Talent identification means identifying what people have a gift for, giving them the right experiences to bring their gifts to sharp focus, and having them use it in the best possible way to benefit everyone.”

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 will@datasourceservices.com.au 

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Warwick Graco is based in Canberra, Australia.

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

#44 Using Data Science to Actually Solve Problems with Caroline Worboys - Data Expert, Investor, Advisor, COO & Vice Chair

Caroline Worboys.jpg

Caroline Worboys is a data expert, investor, advisor, COO at Outra & Vice Chair at DMA Group. She has been working in the data industry for over 30 years. In this time, she’s had a fascinating journey. She has worked, created, mentored and consulted through many data driven organisations. She’s played all the different roles: technical lead, a business lead, a founder and investor.

While Caroline doesn’t describe herself as a data scientist and didn’t go to university, she has always worked with data and has a wealth of experience. She started in the field by working with consumer data for direct marketing and progressed to the point where she founded and sold several successful data related start-ups. Currently, she is the founder and COO of Outra.

In this episode, we talk about what it was like being a woman in technology in the 80’s, how the use of data has progressed over the years and how she keeps her team focused on the goal of doing things faster than other companies.

We speak about:

  • How Caroline got started in data (03:02)

  • What she learnt from observing senior colleagues and what it was like being a woman in technology in the 80s (05:38)

  • Using customer data in order to target people at the right time (07:46)

  • The principles of working with consumer data hasn’t changed (10:04)

  • How the care and attention required for direct mail has now been lost with email and digital marketing (11:09)

  • The importance of being curious and learning (12:31)

  • Starting her own business and finding a different way to charge customers (13:46)

  • Advice for young people and why it’s important to seek people for advice (21:34)

  • Personal drivers to start her business (23:35)

  • How her business innovated as technology changed (25:10)

  • The challenge of using data to actually solve problems (30:29)

  • Considerations when choosing her team (35:48)

  • The recruitment process is like for Caroline’s company (39:00)

  • How Caroline keeps her team focused on the goal of doing things faster than other companies (41:40)

  • The difficulties of work/ life balance (44:16)

  • Considerations for being a leader in the data space (47:03)

  • The importance of thinking about the type of data you want to work with (51:43)


Quotes:

  • “Seek out people who have really, honestly read the book and seen the movie and been there. Because they can stop you from going down a whole bunch of dead ends.”

  • “You can’t scale and have thousands of relationships with thousands of people. But you can create a culture, and processes below that culture, that are scalable.”

Links:

Outra

https://outra.co.uk

Actico

https://www.actico.com/

SmartFocus

https://www.smartfocus.com/


News International

https://en.wikipedia.org/wiki/News_UK

Transunion (Formerly Call Credit)

https://www.transunion.co.uk/

Barclays

https://www.barclays.co.uk/

Monzo

https://monzo.com/

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 will@datasourceservices.com.au 

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Kevin Harrison is based in Concord, California, USA.

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





#43 Increasing Market Share and Improving Operations Using Data Science with Kevin Harrison - Chief Data Officer and Deputy Chief Information Officer

Kevin Harrison - Chief Data Officer and Deputy Chief Information Office.jpg

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. Having worked with multiple organisations, Kevin has been able to handle different types of challenges in our industry. In today’s episode, Kevin shares the strategies he applied to move from smaller projects to bigger ones. How he has been able to help organisations increase their market share and improve operations. Kevin also shares why he thinks changing the perception of organisations about data and educating them about tools in the space is so important. He further talks about data governance and possible changes in role of the data scientist role in future. 

We speak about:

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!  

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 will@datasourceservices.com.au 

Fyrebox - Make Your Own Quiz!

Kevin Harrison is based in Concord, California, USA.

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

#42 Maintaining an Updated Skillset Despite Rapid Technological Advances with Michael Tamir - Head of Data Science & Data Science Lecturer

Michael Tamir- Head of Data Science.jpg

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.

We speak about:

[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!  

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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 will@datasourceservices.com.au 

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Michael Tamir is based in San Francisco Bay Area, USA.

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

#41 Improving Learning on a Large Scale Through Data Science with David Niemi - VP Measurement and Evaluation

David Niemi - VP Measurement and Evaluation

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!  

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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 will@datasourceservices.com.au 

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David Niemi is based in Valencia, California, USA.

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

#40 How to Build a Diverse Data Science Team with Kjersten Moody - Chief Data and Analytics Officer

Kjersten Moody - Chief Data and Analytics Officer

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.


We speak about:

• [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:

  1. “Challenges are a constant in a domain such as data science.”

  2. “Diversity is an attribute of the team. It’s the diversity of experiences, culture, and thought.”

  3. “The process of matching price to risk is inherently done through data.”

  4. “Data strategy is interpreted in many different ways.” 

  5. “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: 

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

Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au 

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Kjersten is based in Chicago, Illinois

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

 

#39 Communicating the Results of Advanced Analytics Projects with Matt Kuperholz - Partner and Chief Data Scientist

Matt Kuperholz - Partner and Chief Data Scientist

Matt Kuperholz. Matt currently works for PWC as a Partner in their Analytic Intelligence Area and is their Chief Data scientist. 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.

We speak about:

· 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: 

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

Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au 

Fyrebox - Make Your Own Quiz!

 

Matt is based in Melbourne, Australia.

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

#38 How To Build a World Class Data Science Team

Felipe Flores-How To Build a World Class Data Science Team.jpg

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

 

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 will@datasourceservices.com.au 

Fyrebox - Make Your Own Quiz!

Felipe 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!

 

#37 Dr. Kristen Sosulski - Associate Professor of Data Visualization NYU Stern; Director, Learning Science Lab; Author of Data Visualization Made Simple and Consultant

Dr. Kristen Sosulski - Data Visualization

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. 

We speak about:

• 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

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: 

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

Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au 

Fyrebox - Make Your Own Quiz!

Kristen is based in New York, 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!