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To celebrate EARL taking place this September we launched a competition to win a free online training course and the competition closes today at midnight – Submit your competition entry here.

You have the chance to win a 2-day training course for you and up to 9 other attendees from your company. The winner can select either ‘Intro the R for analytics’ or ‘Intro to Python for analytics’. The workshop will be delivered online on UK time – so you can enter from any location!

To enter you just need to complete this form and answer a few questions about you and your team. The closing date is the 30th of September 2021, the winner will be contacted in early October. Please read the full terms and conditions here.

 

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Sara Hamilton Shortlisted for Women in Tech Excellence Awards 

We’re delighted that Sara’s efforts have been recognised in her recent shortlist nomination for this award, in the Transformation Leader:Tech category. 

Despite a record number of entries this year, Deputy Director, Product & Managed Service’s Sara’s technically accomplished team mentor, coach and passionate approach for user experience, alongside her desire to streamline processes, improve efficiency and develop products from conception – made her worthy of reaching the shortlist. 

As an active member of Ascent’s Women in Tech user group, Sara actively aims to contribute to change towards workplace diversity, with her belief that it can bring huge benefits to tech companies in terms of creativity, improved problem solving and improved products. As an active social campaigner, Sara believes how employers should emphasise how there is no heroism in martyrdom or being seen to be working and the real money is in working effectively and sustainably – something she actively encourages as part of her team.  

Experienced in agile transformation, scrum implementation, product and programme ownership, internal audits and quality management, Sara has been responsible for introducing agile processes into product development at Mango. With colleagues, describing Sara as a ‘fantastic team lead, who has instigated change at a team and process level – testing technology delivery and bringing a genuine culture of ownership to the team’, it’s not surprising Sara’s talent’s have been recognised.  

We look forward to the attending the awards dinner on 24th November and congratulating Sara on her achievements at Mango, alongside colleague Layla Marshall, Ascent’s Director of Product & Marketing who has also been shortlisted for the ‘Outstanding Returner Award’.     

 

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Thank you to everyone who joined us for EARL 2021 – especially to all of the fantastic presenters! We were pleased to receive lots of really positive feedback from the online event and there are plenty of highlights to share.

Branka Subotic, NATS

It was great to kick off EARL 2021 with our first keynote of the day from Branka. She has worked for NATS since 2018 and is currently their Director of Analytics. Branka shared with us interesting ways to help teams to work together and also some unusual ways to upskill! Her talk was peppered with some videos showing us flight data and the impacts of Covid.

Chris Beeley, NHS – Stronger together, making healthcare open- building the NHS-R Community

We are always delighted to hear from the NHS at the EARL Conference and this year was no exception. We were treated to a passionate talk from Chris on how the NHS-R community has been built up over the years and how their conference has gone from strength to strength. We all know how supportive the R community can be, so it is great to see this in action.

Amit Kohli – Introduction to network analysis

Amit gave us an introduction to the principles of network analysis and shared several use-cases demonstrating their unique powers. Amit also included a fun way to interact with his talk with the use of a QR code  – we can always rely on Amit to entertain us! Our team thought it was a really interesting topic and it felt accessible to those who perhaps don’t know much on the subject.

Emily Riederer, Capital One – How to make R packages part of your team

We loved Emily’s fun concept of making R packages a real part of your team and her use of code, and the choices she made along the way. Her talk examined how internal R packages can drive the most value for their organisation when they embrace an organisation’s context, as opposed to open source packages which thrive with increasing abstraction. Read our interview with Emily here.

Dr. Jacqueline Nolis, Saturn Cloud

We closed the day with our final keynote talk from Jacqueline Nolis. She is a data science leader with over 15 years of experience in managing data science teams and projects, at companies ranging from DSW to Airbnb. She currently is the Head of Data Science at Saturn Cloud where she helps design products for data scientists. Jacqueline spoke to us about taking risks in your career and shared with us the various risks she has taken over her career and how they went! It was inspiring to hear from an experienced data scientist that it’s ok to take a risk every now and then  – and refreshing to hear her honesty about what could have gone better – and how she has ultimately learned and grown from this.

These are just a few of the brilliant talks from a fantastic conference day. It was a delight to have speakers and attendees joining us from across the world – so thank you again to all that came along.

We are hoping to be back in London next year to host EARL in-person again. We are tentatively holding the 6th-8th of September 2022 as our conference dates. If you’d like to keep up-to-date on all things EARL please join our mailing list. We will open the call for abstracts in January 2022.

 

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Mango’s ‘Meet-Up’ at Big Data London on 22nd September features guest speaker Adam Hughes, Data Scientist for The Bank of England, whose remit involves working with incredibly rich datasets, feeding into strategic decision-making on monetary policy. You can read about Adam’s incredibly interesting data remit and his team’s journey through Covid-19, in this short Q&A.

Can you tell us about your interest in data and your role at Bank of England?

Working at the Bank it’s hard not to be interested in data! So much of what we do as an organisation is data driven, with access to some incredibly rich datasets enabling interesting analysis. In Advanced Analytics, we leverage a variety of data science skills to support policy-making and facilitate the effective use of big, complex and granular data sets. As a data scientist, I get involved in all of this, working across the data science workflow.

What’s the inspiration for your talk  – effectively data science at speed?

As with so much recently – Covid. With how fast things have been moving and changing, traditional data sources that policymakers were relying on weren’t being updated fast enough to reflect the situation.

Can you tell us about your data team’s journey through covid-19 and the impact it has had?

In a recent survey, the Bank of England sought to understand how Covid has affected the adoption and use of ML and DS across UK Banks. Half of the banks surveyed reported an increase in the importance of ML and DS as a result of the pandemic. Covid created a lot of demand for DS skills and expertise within the Bank of England too. Initially this led to some long hours, but it was motivating and generally rewarding to work on something so clearly important. Working remotely 100% of the time was a challenge at first, but generally the transition away from the office has been remarkably smooth in terms of day-to-day working (though there are still disadvantages due to the lack of face-to-face contact). As outputs have subsequently been developed and shared widely in the organisation, they have been an excellent advert for data science, showing the value it can add. In particular, it’s been great to see the business areas we worked with building up their local data science skills as a consequence.

What’s the talk about and what are the key takeaways?

The talk will cover some of the techniques we used to get, process and use new data sources under time pressure, including what we’ve learnt from the process. The key takeaways are:

  • Non-traditional datasets contain some really useful information – and can form part of the toolkit even in normal times;
  • Building partnerships is key;
  • A suite of useful building blocks, such as helper packages or code adapted from cleverer people helps speed things up;
  • Working fast doesn’t mean worse outcomes.

We look forward to seeing you at Mango’s Big Data London, Meet Up, 22nd September 6-8pm, Olympia ML Ops Theatre. You can sign up here.

Guest speaker, Adam Hughes is one of The Bank of England’s Data Scientists, https://www.linkedin.com/in/adam-james-hughes/

flexing analytical muscle
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Mango’s ‘Meet-Up’ at Big Data London on 22nd September features guest speaker Premal Desai, Head of the Data & AI function for The Gym Group.

For the Gym Group, advancing customer retention and pricing strategies have been critical drivers to success. Their focus over the last few years has been to deliver value from their analytic investments and to develop a stronger analytical culture. You can read about their data journey in this short interview with Premal – the key successes and challenges they have overcome.

 Introducing Premal Desai, Head of the Data & AI

Premal currently leads the Data & AI function for The Gym Group where he oversees a high performance team covering Engineering, Analytics and Data Science. Prior to this, Premal spent almost a decade with Thomson Reuters, leading Strategic Marketing & Analytics functions globally. He has previous experience across Europe working for Orange, and spent several years with PA Consulting in their Strategy & Marketing Practice. Premal is a 2021 Board Advisor for the Business of Data, a Seed Investor for a number of start-ups and outside of work, Premal is a passionate Liverpool FC fan + renowned for his desire of fast German automobiles!

Can you tell us a bit about your role at The Gym Group?

I’ve been fortunate to have a varied and interesting career over the past 20 years – spanning both client side and consultancy roles. I’ve worked across different sized organisations, covering Telecoms, Financial Services and most recently Health & Leisure. The two common passions for me are getting to grips with understanding numbers and customers! I currently look after the Data & AI team at the Gym Group which allows me to combine these passions – I have a small, but very capable team of Engineers, Analysts and Data Scientists who support the business to uncover insights and drive growth.

Can you tell us a bit about your journey with data at The Gym Group?

The Gym Group are generally a data-oriented business, which is fortunate given my role! Over the past 3 years, we’ve been on a mission to grow our capability across a number of areas – the depth and breadth of data used across the organisation, to improve the level of granularity and sophistication of data & analytics and to turn data into more of a strategic asset. As part of this, we’ve also expanded the range and skills of our people, how we consume data and grown the range of insight we’re able to provide.

How important has being data driven been as part of your recovery post covid?

During COVID, data was absolutely central to give the business confidence across a number of areas during a unprecedented period of time. For example, we wanted our customers to feel safe using our facilities once they opened – and so the company developed a capability showing live Gym Busyness via mobile – and we used various models to help us determine appropriate levels of Gym occupancy. As a business, it’s actually helped us to see data use cases more strategically and we’re delighted to have seen really positive business recovery since Gyms re-opened in April 2021.

Can you tell us about your talk ?  what should we expect and what are the key takeaways?

The talk will be focused on a couple of really important commercial drivers for our business – Customer Retention and Pricing – and the data / data science journey we’ve been on in these areas over the past couple of years. For balance, we’ll talk about both successes and some practical challenges. In addition, we’ll also speak to the importance of culture and ROI, and some factors that I think have been important on our journey. In terms of key takeaways – I encourage you to attend the talk!

Keen to join us? Premal’s talk at the Data Science Meet-Up is featured as part of the Big Data LDN line up at 6-8pm, 22nd September , AI & MLOPs Theatre. Register here.

We hope to see you there.

 

Dockerisation
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Best practice in data science can lead to long-lived business results. A structure that encourages repeatable processes for generating value from data, leads to a fully productive team working, allowing reproducible results, time and time again. When this process is ingrained across a company’s culture and the business and data teams are working together in harmony with the business goals, then the value of data can be realised into an overall centre of excellence and a shared language for best practice.

A shared language of best practice

Layers of operational best practice allow a standard practice to be adopted – ensuring the best possible outcome of your data science investment. For a data science team, best practices could relate to developing models or structuring analysis, quality standards or how a project is delivered. Alternatively, they could even align to the selection of your data and analysis tools, as these can easily impact the success of your project.

With data science teams coming from a diverse range of backgrounds and experiences, what may be obvious to one can be a novelty to another. A shared language of best practice allows collaborators to focus on the all-important value generated. A workflow that adheres to a best practice ensures quality, whether that be business value of insights to the accuracy of models. Best practices take the guess work out, minimise mistakes and create a platform for future success.

4 best practices every data delivery teams should focus on:

  • Reproducibility – Whatever the task is. If your results can’t be repeated, then is it really done?
  • Robustness – Results and quality of analysis can have a huge impact, ensuring your best practices that has checks and balances will lead to better quality
  • Collaboration – What use are your results if they are difficult to share. Having standards for collaboration means business value can be attained
  • Automation – It is very easy to do work with no automation, frameworks for automation can help accelerate teams

Best practice in Dockerisation

My talk at the Big Data London Meet Up ‘ How Docker can help you become more reproducible’, takes one element of best practice in data science, focusing on Dockerisation which is proving to be a powerful tool – one that is already turning established best practices in teams on its head. The tools allow teams to collaborate much easier, to be much more reproducible and automate workflows, in an impressive way. Yet, it has not had as much adoption within data science as it has within software engineering. My talk will explore just how Docker can super charge workflow and your valuable use cases.

This talk will be of interest to any data scientist who has had trouble with, deploying or working with engineering teams, reproducing colleagues’ analysis. It will also be of interest to anyone wanting to know how docker can scale a team, making it less intimidating and perfectly arming practitioners with the tools to give it a go.

I look forward to seeing you at Mango’s Big Data London, Meet Up, 22nd September 6-8pm, Olympia AI & MLOPS Theatre. You can sign up here

Kapil Patel is one of Mango’s Data Science Consultants.

 

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