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.

How might we turn 120kg of plastics into art?

“With a team of 80 volunteers, Mundane Matters collected 2,319kg of plastic from the oceans around the Whitsunday Islands over the course of just one week, repurposing 120kg of the damaging debris into these ‘manmade oranges’ for the Wasteland installation.” READ MORE…

Wasteland is 24m tall installation at Customs House, presented by Art & About Sydney. Over the past year we've been working on the design and production, as well as collection of marine debris from the Great Barrier Reef to build this work. Video by Jordi Marin.

How AI can save our humanity

A wonderful talk by renowned computer scientist Kai-Fu Lee (@kaifulee).

AI is massively transforming our world, but there's one thing it cannot do: love. In a visionary talk, computer scientist Kai-Fu Lee details how the US and China are driving a deep learning revolution -- and shares a blueprint for how humans can thrive in the age of AI by harnessing compassion and creativity.

Dalberg Data Insights identifies areas at-risk for food insecurity using mobile phone data

From the Dalberg Data Insights blog…

“Widespread poverty in Uganda is closely related to food insecurity and malnutrition, with 36% of children in the country chronically undernourished or stunted.  For development actors to effectively respond to crisis, they must be able to understand which communities are most vulnerable in real-time and at high geographic granularity. Food security intervention planning however often relies on outdated survey data, which harms the efficacy of these interventions.

Recent research indicates that mobile phone data can be linked to food insecurity because mobile phone usage patterns reveal shocks to household expenditure in real-time. Considering this research, Dalberg Data Insights has built a Food Security Manager that displays changes in mobile phone top-up patterns, where certain changes in spending serve as further indicators for changes in food spending. The Food Security Manager allows development actors to pinpoint where food shortages and malnutrition may occur so that food security actors can better distribute resources to the most vulnerable communities….”. READ MORE >>

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.