17 episodes

Welcome to the DataCafé: a special-interest Data Science podcast with Dr Jason Byrne and Dr Jeremy Bradley, interviewing leading data science researchers and domain experts in all things business, stats, maths, science and tech.

DataCaf‪é‬ Jason & Jeremy

    • Science

Welcome to the DataCafé: a special-interest Data Science podcast with Dr Jason Byrne and Dr Jeremy Bradley, interviewing leading data science researchers and domain experts in all things business, stats, maths, science and tech.

    [Bite] Why Data Science projects fail

    [Bite] Why Data Science projects fail

    Data Science in a commercial setting should be a no-brainer, right? Firstly, data is becoming ubiquitous, with gigabytes being generated and collected every second. And secondly, there are new and more powerful data science tools and algorithms being developed and published every week. Surely just bringing the two together will deliver success...

    In this episode, we explore why so many Data Science projects fail to live up to their initial potential. In a recent Gartner report, it is anticipated that 85% of Data Science projects will fail to deliver the value they should due to "bias in data, algorithms or the teams responsible for managing them". There are many reasons why data science projects stutter even aside from the data, the algorithms and the people.
    We discuss six key technical reasons why Data Science projects typically don't succeed based on our experience and one big non-technical reason!

    And being 'on the air' for a year now we'd like to give a big Thank You to all our brilliant guests and listeners  - we really could not have done this without you! It's been great getting feedback and comments on episodes. Do get in touch jeremy@datacafe.uk or jason@datacafe.uk if you would like to tell us your experiences of successful or unsuccessful data science projects and share your ideas for future episodes.

    Further Reading and Resources
    Article: "Why Big Data Science & Data Analytics Projects Fail" (https://bit.ly/3dfPzoH via Data Science Project Management) Article: "10 reasons why data science projects fail" (https://bit.ly/3gIuhSL via Fast Data Science) Press Release: "Gartner Says Nearly Half of CIOs Are Planning to Deploy Artificial Intelligence" (https://gtnr.it/2TTYDZa via Gartner) Article: "6 Reasons Why Data Science Projects Fail" (https://bit.ly/2TN3sDK via ODSC Open Data Science) Blog: "Reasons Why Data Projects Fail" (https://bit.ly/3zJrFeA via KDnuggets)

    Some links above may require payment or login. We are not endorsing them or receiving any payment for mentioning them. They are provided as is. Often free versions of papers are available and we would encourage you to investigate.
    Recording date: 18 June 2021
    Intro music by Music 4 Video Library (Patreon supporter)

    • 19 min
    Data Science for Good

    Data Science for Good

    What's the difference between a commercial data science project and a Data Science project for social benefit? Often so-called Data Science for Good projects involve a throwing together of many people from different backgrounds under a common motivation to have a positive effect.

    We talk to a Data Science team that was formed to tackle the unemployment crisis that is coming out of the pandemic and help people to find excellent jobs in different industries for which they have a good skills match.

    We interview Erika Gravina, Rajwinder Bhatoe and Dehaja Senanayake about their story helping to create the Job Finder Machine with the Emergent Alliance, DataSparQ, Reed and Google.

    Further Information
    Project: Job Finder Machine Project Group: Emergent Alliance and DataSparQShout out: Code First Girls for fantastic courses, mentoring and support for women in tech and data scienceSome links above may require payment or login. We are not endorsing them or receiving any payment for mentioning them. They are provided as is. Often free versions of papers are available and we would encourage you to investigate.
    Interview date: 25 March 2021
    Recording date: 13 May 2021
    Intro audio Music 4 Video Library (Patreon supporter)

    • 36 min
    [Bite] Data Science and the Scientific Method

    [Bite] Data Science and the Scientific Method

    The scientific method consists of systematic observation, measurement, and experiment, and the formulation, testing, and modification of hypotheses. But what does this mean in the context of Data Science, where a wealth of unstructured data and variety of computational models can be used to deduce an insight and inform a stakeholder's decision?

    In this bite episode we discuss the importance of the scientific method for data scientists. Data science is, after all, the application of scientific techniques and processes to large data sets to obtain impact in a given application area.  So we ask how the scientific method can be harnessed efficiently and effectively when there is so much uncertainty in the design and interpretation of an experiment or model.

    Further Reading and Resources
    Paper: "Defining the scientific method" via Nature https://www.nature.com/articles/nmeth0409-237Paper: "Big data: the end of the scientific method" via The Royal Society https://royalsocietypublishing.org/doi/10.1098/rsta.2018.0145Article: "The Data Scientific Method" via Medium https://towardsdatascience.com/a-data-scientific-method-80caa190dbd4Article: "The scientific method of machine learning" via Datascience.aero https://datascience.aero/scientific-method-machine-learning/Article: "Putting the 'Science' Back in Data Science" via KDnuggets https://www.kdnuggets.com/2017/09/science-data-science.htmlPodcast: "In Our Time: The Scientific Method" via BBC Radio 4 https://www.bbc.co.uk/programmes/b01b1ljmPodcast: "The end of the scientific method" via The Economist https://www.economist.com/podcasts/2019/11/27/the-end-of-the-scientific-methodVideo: "The Scientific Method" via Coursera https://www.coursera.org/lecture/data-science-fundamentals-for-data-analysts/the-scientific-method-Ha5hqCartoon: "Machine Learning" via xkcd https://xkcd.com/1838/Some links above may require payment or login. We are not endorsing them or receiving any payment for mentioning them. They are provided as is. Often free versions of papers are available and we would encourage you to investigate.
    Recording date: 30 April 2021
    Intro music by Music 4 Video Library (Patreon supporter)

    • 17 min
    Data Science on Mars

    Data Science on Mars

    On 30 July 2020 NASA launched the Mars 2020 mission from Earth carrying a rover called Perseverance, and rotorcraft called Ingenuity, to land on and study Mars. The mission so far has been a resounding success, touching down in Jezero Crater on 18 February 2021, and sending back data and imagery of the Martian landscape since then.

    The aim of the mission is to advance NASA's scientific goals of establishing if there was ever life on Mars, what its climate and geology are, and to pave the way for human exploration of the red planet in the future. Ingenuity will also demonstrate the first air flight on another world, in the low-density atmosphere of Mars approximately 1% of the density of Earth's atmosphere.

    The efforts involved are an impressive demonstration of the advances and expertise of the science, engineering, and project teams. Data from the mission will drive new scientific insights as well as prove the technical abilities demonstrated throughout. Of particular interest is the Terrain Relative Navigation (TRN) system that enables autonomous landing of missions on planetary bodies like Mars, being so far away that we cannot have ground communications on Earth in the loop.

    We talk with Prof. Paul Byrne, a planetary geologist from North Carolina State University, about the advances in planetary science and what the Mars 2020 mission means for him, his field of research, and for humankind.

    Further Reading and Resources
    Website: Profile page for Prof. Paul Byrne at the Center for Geospatial Analytics at NCSU (https://bit.ly/3gkP4vD via ncsu.edu)Website: Mars 2020 (https://mars.nasa.gov/mars2020/)Paper: Mars 2020 Science Definition Team Report (https://go.nasa.gov/3x5d6AF via nasa.gov)Video: Perseverance Rover's Descent and Touchdown on Mars (https://bit.ly/32o6248 via youtube)Website: Lunar rocks and soils from Apollo missions (https://curator.jsc.nasa.gov/lunar/)Article: Terrain Relative Navigation (https://go.nasa.gov/2RMd9RZ via nasa.gov)Paper: A General Approach to Terrain Relative Navigation for Planetary Landing (https://bit.ly/3mXCN1z via aiaa.org)Video: Terrain Relative Navigation, NASA JPL (https://bit.ly/2QCcTEB via youtube)Video: Studying Alien Worlds to Understand Earth (https://bit.ly/3tpZ1f3 via youtube)Some links above may require payment or login. We are not endorsing them or receiving any payment for mentioning them. They are provided as is. Often free versions of papers are available and we would encourage you to investigate.
    Interview date: 25 March 2021
    Recording date: 13 April 2021
    Intro audio is a sound recording of the wind on Mars obtained by the Perseverance rover, provided by NASA.

    • 58 min
    [Bite] How to hire a great Data Scientist

    [Bite] How to hire a great Data Scientist

    Welcome to the first DataCafé Bite: a bite-size episode where Jason and Jeremy drop-in for a quick chat about a relevant or newsworthy topic from the world of Data Science. In this episode, we discuss how to hire a great Data Scientist, which is a challenge faced by many companies and is not easy to get right.

    From endless coding tests and weird logic puzzles, to personality quizzes and competency-based interviews; there are many examples of how companies try to assess how a candidate  handles and reacts to data problems. We share our thoughts and experiences on ways to set yourself up for success in hiring the best person for your team or company.

    Have you been asked to complete a week-long data science mini-project for a company, or taken part in a data hackathon? We'd love to hear your experiences of good and bad hiring practice around Data Science.  You can email us as jason at datacafe.uk or jeremy at datacafe.uk with your experiences. We'll be sure to revisit this topic as it's such a rich and changing landscape.

    Further Reading
    Article: Guide to hiring data Scientists (https://bit.ly/2OjnALi via kdnuggets.com)Article: Hiring a data scientist: the good the bad and the ugly! (https://bit.ly/3cMpLR5 via forbes.com)Article: How to Hire (https://bit.ly/3dCLTfO via Harvard Business Review)Podcast: How to start a startup (https://bit.ly/3sOWxGU via Y-Combinator/Stanford University)Video: Adam Grant: Hire for Culture Fit or Add? (https://bit.ly/3cNGWl3 via YouTube/Stanford eCorner)Some links above may require payment or login. We are not endorsing them or receiving any payment for mentioning them. They are provided as is. Often free versions of papers are available and we would encourage you to investigate.
    Recording date: 1 April 2021

    Intro music by Music 4 Video Library (Patreon supporter)

    • 14 min
    Bayesian Inference: The Foundation of Data Science

    Bayesian Inference: The Foundation of Data Science

    In this episode we talk about all things Bayesian. What is Bayesian inference and why is it the cornerstone of Data Science?

    Bayesian statistics embodies the Data Scientist and their role in the data modelling process. A Data Scientist starts with an idea of how to capture a particular phenomena in a mathematical model - maybe derived from talking to experts in the company. This represents the prior belief about the model. Then the model consumes data around the problem - historical data, real-time data, it doesn't matter. This data is used to update the model and the result is called the posterior.

    Why is this Data Science? Because models that react to data and refine their representation of the world in response to the data they see are what the Data Scientist is all about.

    We talk with Dr Joseph Walmswell, Principal Data Scientist at life sciences company Abcam, about his experience with Bayesian modelling.

    Further Reading
    Publication list for Dr. Joseph Walmswell (https://bit.ly/3s8xluH via researchgate.net)Blog on Bayesian Inference for parameter estimation (https://bit.ly/2OX46fV via towardsdatascience.com)Book Chapter on Bayesian Inference (https://bit.ly/2Pi9Ct9 via cmu.edu)Article on The Monty Hall problem (https://bit.ly/3f1pefr via Wikipedia)Podcast on  "The truth about obesity and Covid-19", More or Less: Behind the Stats podcast (https://bbc.in/3lBqCGS via bbc.co.uk)Gov.uk guidance:Article on "Understanding lateral flow antigen testing for people without symptoms" (https://bit.ly/313JDs9)Article on "Households and bubbles of pupils, students and staff of schools, nurseries and colleges: get rapid lateral flow tests" (https://bit.ly/3c5ZXih)Some links above may require payment or login. We are not endorsing them or receiving any payment for mentioning them. They are provided as is. Often free versions of papers are available and we would encourage you to investigate.

    Recording date: 16 March 2021
    Interview date: 26 February 2021

    • 42 min

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