Data Science

Designing for Resilience


A few months ago IDEO published an article about designing for resilience and using technology can be leveraged to make services more intelligent and empathetic. Which got me thinking (yet again…) How do you actually do this?

 

How do you back up anecdotes or interviews with hard data? and what are the most commonly used algorithms, platforms and tools that data scientists, designers, and social entrepreneurs are using?


 

Kuja Kuja is one product that has made significant impact in Africa – the world’s first real-time customer feedback platform for refugee environments, I’d read about the development of this before and it’s a great example of how a successful platform co-designed and built for one particular organisation can become a tool help communities throughout the continent.

So one of my goals for this week is to…

Find as many examples of data science projects that have a significant level of co-design involved, whether that be for the social sector, for government, for research, the tech industry, or business; and try and identify the common tools, technologies, algorithms that underpin each one.




From Astrophysics to Data Science

 

Visualising the career paths of 200 astronomers turned data scientists.

The first in a series of blog posts that explore how and when astronomers transition into data science careers.

 

The Science to Data Science (S2DS) and Insight Data Science fellowships are 5–7 week intensive post-doctoral training fellowships that bridge the gap between academia and data science in industry. This interactive visualisation was created to get a better sense of what stage in their career astronomers move into data science.

  • How many PhD students forego postdoctoral research in favour of moving straight into data science?

  • How many professional astronomers are moving into data science?

  • At what stage of their career do they do this?

  • How may postdocs do they have on their CV before deciding to leave?

  • Are tenured astronomers moving into data science?

  • How many go through a data science fellowship programs?

  • How many do industry internships?

  • How many go back to complete a Masters in Data Science or other similarly formal data science education?

  • How many make the transition without a data science fellowship? Have any moved back and forth between academia and data science? </p>

  • Why do they move in data science?

The data: comes from the LinkedIn profiles of 116 astronomers who moved from astronomy to data science at some point in their career.

The visualisation: was created using the d3.js sunburst template, HTML, and CSS.

Data Science at Made.com

 

MADE.com is a UK-based online retailer with an award-winning business model. Foregoing traditional storefronts, Made has only three UK-based showrooms and works directly with designers and manufacturers to produce high-quality, thoughtfully designed furniture at remarkable prices.

As a made-to-order online company with furniture moving from ship to lounge room, leveraging production and customer data is critical to managing their supply chain. If too many products are made the company loses money is dock or storage fees; too little and customers wait longer to receive the orders. Made also prides itself on keeping ahead of trends, so predicting customer behaviour is vital to their business.

What I love most about Made.com is their TalentLAB crowdfunding platform. It’s a place to discover new talent and unique products you won’t find anywhere else. TalentLAB was born out of the MADE Emerging Talent Award – an annual competition for up-and-coming designers to break into the industry, and get their product made and sold.

We believe that great design is for everyone. It surprises, tells a story and makes the everyday a little less ordinary. At MADE we design for how people live today. No champagne, no private view, just affordable high-end design at your fingertips.
— Made.com

An evening of networking & talks

We had the privilege of spending the evening with Made’s CEO, Talent Acquisition team, and Data Science team. Being a design focussed retailer, it should come as no surprise that visiting Made’s London Headquarters was a really lovely experience. We were welcomed with drinks and nibbles, and more food following talks from Made’s leaders. Talks focussed on Made’s current and future data science aspirations, and why growing their team is critical to their future success.

Where does Data Science fit in?


 

Understanding the Customer Lifecycle

  • Considered purchases means long sales cycles.

  • Cross device browsing is hard to track – but here to stay

  • User journeys do not start at their site, they start in search engines.

  • There is untapped potential to drive repeat purchases.


Supply Chain

& Forecasting

  • Accurate sales forecasting is key to good lead times

  • Predicting forecast hits at the design stage could be game changing


Customer Service
Automation

  • The customer service team currently scales with sales

  • Advances in AI means many simple customer service queries should be able to be automated.


Visual Analysis
& Recommendations

  • Visual search – finding products of a similar colour, shape, or style.

  • Identifying products that work well together.

  • Learning to understand a customers style.

 
 







An evening with Mike Butcher from Tech Crunch

 

Mike Butcher MBE, is Editor-at-large of TechCrunch, the biggest breaking news site about the world’s hottest tech companies. Mike has been named one of the most influential people in technology, and is a regular commentator on the tech business. He founded the Europas Conference & Awards, the charity Techfugees, and has been an advisor on startups to the British Prime Minister and the Mayor of London. He was awarded an MBE in the Queen’s Birthday Honours list 2016 for services to the UK technology industry and journalism.

We had the privilege of spending an evening Mike, and hearing him speak about Artificial Intelligence and the future of technology.
Needless to say it was quite enlightening…

Deep Learning with Microsoft Azure

 

Discussing Ethics & Empathy in AI

S2DS London 2018

Today is the first day of S2DS London Data Science Fellowship program and there is a palpable buzz in the air, perhaps made a little more intense due to my complete lack of jetlag. Arna 1 – BST 0). It may also have something to do with the fact that Yorkshire tea bags have more tea than in Australia, or the fact that it's 7:23am I am already on my second cup. Regardless, the wait is over and I'm keen to get started.

 

Mapping the Journey:

My journey to get here started back in April and I'll talk about that in a future blog post. Suffice to say, I've been mapping the customer experience/service design as a Data Fellow – so far so good. Once I'm back in Australia I'll attempt to present my findings in a cohesive, informative and productive way. I've been using UXPressia, an elegant solution for quickly putting the main elements in place. Smaply and Realtime Board are also good options. The final journey map will look something like this;

   My Service Design Journey   by Georgi Lewis

This particular Journey Map was created by Georgi Lewis, a Melbourne-based human centered designer and recent judge for the 2018 Random Hacks of Kindness (RHoK) Winter Hackathon. 

 

 

 

 

 

 

 

 

n accepted into this year's Science to Data Science (S2DS) Fellowship program. 

 

 

 

 

 

Going deep vs. Going wide

I spent most of today learning more about the various schools of thought around design thinking, reading Tim Brown's Design Thinking blog, writing up a short case study about designing for a circular economy, and thinking more about the intersection between data science and human-centred design. In a perfect world I would be paid to do this all day, every day.

One blog post; The Career Choice Nobody Tells You About,  really resonated with me. It's short and contains a simple message, but it was a nice reminder for why I wanted to "leave" astronomy research and pursue new opportunities.

Going deep requires incredible focus, lifelong commitment to a single cause, a willingness to be patient towards achieving success, and the confidence to follow a path others may not understand or value...

Going wide, on the other hand, is about making connections between what you already know and what you’re curious about discovering. It requires systems thinking in order for the whole to be greater than the sum of the parts. It means developing the skills to collaborate for the purpose of learning. It’s about seeing the creative possibilities in breaking down boundaries and describing the world, your organization, the problem in new ways.
— Tim Brown, CEO of IDEO

Data science offers researchers in academia an opportunity to go wide, to explore problems across all sciences, the arts, across business and technology, and even the not-for-profit social sector. While many researchers leave academia because of negative experiences or job insecurity, I suspect that most (like myself) leave because there are just so many more equally exciting things in the world to discover (or make, or teach) and a whole new community of amazing people to learn from.

Personally, committing a lifetime to academic research wasn't enough for me. Although my research was exciting and I had the opportunity to work at world-leading academic institutions, with incredibly clever and talented and researchers, there was always something missing. Perhaps it was a fear of missing out on all the other wonderful things people were doing?

Fortunately I've managed to have found a way to find aspects of astrophysics research where i can make significant contributions, and in the meantime work with data and technology within a completely different industry. There is a stigma around leaving academia so choosing how and why you leave matters.