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ACS Emerging and Deep Tech Webinar: Data Science with Mars Geldard
Data Science beyond 'Python'
Tue 10 Mar 2020 12:30 PM AEDT
Duration: 1.0 hours
Register by Tue 10 Mar 2020 09:00 AM AEDT
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Webinar
CPD Hours: 1
Skills Level: Systems development management (DLMG) -> Level 5
About this event
'Python' is a polarising programming language. First released in 1991, it subverted many established paradigms of other languages at the time, and has since grown a large and dedicated community.
When Data Science was rising towards its current level of popularity--in what some are calling "The Third Summer of AI"- Python was widely regarded as a catch-all language. It was easy to learn and it was good enough at almost everything; even though it wasn't particularly good at almost anything.
The language was already used in the hard science labs and powering computationally expensive simulations and to anyone coming into the field it was like everyone had already made the decision for you that the simplicity of Python was worth the cost of performance.
Python 3 was released, and shortly after a new wave of Data Science professionals and enthusiasts arrived to meet it and an infeasible amount of processor operations later, Python had become the de-facto standard for Data Science.
Why would you want anything else? It's got easy IO (that doesn't bother verifying file formats or encoding half the time), it's got pandas for data manipulation (widely used in production, even though it only reached 1.0 in January, who knew?), and a whole host of frameworks for machine learning that practically come with a cherry on top. From Keras and Scikit-learn, to PyTorch and Theano, to the all-important TensorFlow and beyond.
But what about those who rebelled against this convention? Who are not content with shrinking imperfect things when new constraints make them infeasible? Who won't take MicroPython or Snek, tack on some TensorFlow Lite and carry on? Those who figured we could do better? They made new tools. New frameworks. They build them for sustainability, on high-performance compiled languages with robust infrastructure. New tools popped up in C, C++, Java, JavaScript, others and Google backed the creator of Swift--a language of Apple origins, now Open Source and community-managed--to take a powerful language that the broader developer community wrote off as "that thing you use when you want to write an iOS app" and make it the next big thing in Machine Learning.
These efforts took off slowly at first, struggling to propose sufficient value to those already established in the Python world, but they are growing. New compilers and intermediate languages have been born, and the communities and companies driving tools creation have set their eyes on longer-term sustainability and performance above convenience.
Mars Geldard will discuss this new wave of tools, compare them to what's available in Python, highlight where each camp fails to match up, and explore the implications of this industry schism on the future of Data Science.
Please note, this event beings at 12:30pm AEDT (Sydney time). Register via the link to receive the webinar details.
When Data Science was rising towards its current level of popularity--in what some are calling "The Third Summer of AI"- Python was widely regarded as a catch-all language. It was easy to learn and it was good enough at almost everything; even though it wasn't particularly good at almost anything.
The language was already used in the hard science labs and powering computationally expensive simulations and to anyone coming into the field it was like everyone had already made the decision for you that the simplicity of Python was worth the cost of performance.
Python 3 was released, and shortly after a new wave of Data Science professionals and enthusiasts arrived to meet it and an infeasible amount of processor operations later, Python had become the de-facto standard for Data Science.
Why would you want anything else? It's got easy IO (that doesn't bother verifying file formats or encoding half the time), it's got pandas for data manipulation (widely used in production, even though it only reached 1.0 in January, who knew?), and a whole host of frameworks for machine learning that practically come with a cherry on top. From Keras and Scikit-learn, to PyTorch and Theano, to the all-important TensorFlow and beyond.
But what about those who rebelled against this convention? Who are not content with shrinking imperfect things when new constraints make them infeasible? Who won't take MicroPython or Snek, tack on some TensorFlow Lite and carry on? Those who figured we could do better? They made new tools. New frameworks. They build them for sustainability, on high-performance compiled languages with robust infrastructure. New tools popped up in C, C++, Java, JavaScript, others and Google backed the creator of Swift--a language of Apple origins, now Open Source and community-managed--to take a powerful language that the broader developer community wrote off as "that thing you use when you want to write an iOS app" and make it the next big thing in Machine Learning.
These efforts took off slowly at first, struggling to propose sufficient value to those already established in the Python world, but they are growing. New compilers and intermediate languages have been born, and the communities and companies driving tools creation have set their eyes on longer-term sustainability and performance above convenience.
Mars Geldard will discuss this new wave of tools, compare them to what's available in Python, highlight where each camp fails to match up, and explore the implications of this industry schism on the future of Data Science.
Please note, this event beings at 12:30pm AEDT (Sydney time). Register via the link to receive the webinar details.
Speakers
Speaker
Marina Buttfield-Addison
Marina Rose Geldard, more commonly known as Mars, is a Software Engineer, Data Scientist and Researcher from Down Down Under in Tasmania. When she is not busy being the most annoyingly eager student and staff member ever at the University of Tasmania (UTAS)--or mouthing off about why everyone should love data science as much as she does--she compulsively volunteers at industry events, tutors, hikes around in the wilderness, dabbles in research, builds Game of Thrones in Minecraft (at the WesterosCraft project), and serves on the Executive Committee for her state's branch of the Australian Computer Society (ACS) as well as the national Executive Council of the AUC. In the time she has left she wrote ‘Practical Artificial Intelligence with Swift’ for O’Reilly Media.
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