maths & statistics awareness month
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April marks Mathematics and Statistics awareness month #MathStatMonth in the USA, with the aim of increasing the level of interest in these subjects. Working in an increasingly data-driven world, the ability to harness meaningful insights from data is an essential business and requires specialised data science expertise.

Data science is the proactive use of data and advanced analytics to drive better decision making. This ‘proactive’ use of data is what distinguishes data science from traditional ‘statistical analysis’ and needs to be an active part of an organisation in search of insight, better decision making or improvement.

Data science as a career choice for maths, statistics, and science graduates

Many graduates from maths, statistics and science backgrounds are increasingly attracted by a career in data science. Our current graduate placement Student, Elizabeth tells us more about her early interest in data science and why it presents a natural career path for those interested in mathematics and statistics. “Data science combines the skills and applications of mathematics and statistics with the use of big data and innovating technology to solve a variety of problems. I’m particularly interested in providing solutions to real-world problems and communicating these results at a high level within a business”, says Elizabeth.

“Throughout my placement I have seen the application of using mathematics and statistics within data science projects in performing exploratory data analysis to creating statistical models. My personal interest is in different types of statistical models, and I am due to study Time Series and Bayesian statistics in the final year of my degree”.

Elizabeth has benefited from seeing how mathematics and statistics have been used to model complex situations and improve business decisions from the optimum timing of routine maintenance, saving unnecessary reactivity and costs to creating descriptive, diagnostic and predictive insights which delivered great value and significant return on investment during her time at Mango.

Growing demand for data science

With the demand for Data Scientists still on the rise into 2021, the pandemic has created an even more urgent need for rapid decision making, informed and supported by constantly changing data sets, backed by effective visualization (highlighted by the World Economic Forum (WEF) in July).

Rich Pugh, Mango’s Chief Data Scientist summarises, “Leaders increasingly understand the potential of using data to create smarter, leaner, more engaging organisations. As such, we are still seeing growing demand for “data scientists” who are able to turn that data into acumen in a repeatable and scalable way. As a multi-disciplinary practice, “data science” relies on the combination of “advanced analytics” and “computer science” skill – this, combined with an ability to creatively explore challenges that can be solved, is at the core of realising the value promised by data science”.

“At it’s core, data science relies on mathematics and statistical rigour to provide robust algorithms that can be relied upon to solve often-complex challenges. As interest in data science continues to grow, the work at the Royal Statistical Society becomes increasingly important – to drive the discussion around statistical governance, and the correct and ethical application of statistical routines”, Rich concludes.

demand forecasting this easter
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Using data to accurately forecast demand for chocolate this Easter

Easter is a significant holiday for many businesses especially within the UK. The holiday period brings an increase to food and drink sales, with Easter being the second most popular period of the year for chocolate consumption. Many Britons book holiday trips in this period as well, making it an important time for the leisure industry.

Data science enables businesses to effectively plan for this busy period. Analysis of historical sales data can be used to predict future demand, allowing businesses to accurately plan stock levels; real-time analysis provides visibility of the state of a product, enabling businesses to quickly resolve issues regarding the manufacturing of products like chocolate bunnies.

Accurate forecasting to match demand

Demand forecasting is a commonly used approach which allows businesses to effectively predict future sales, plan and schedule production, improve budget planning, and develop efficient pricing strategies. Predictive analysis is used to understand and forecast demand over time, helping businesses make well-informed decisions.

Adapting in line with the coronavirus pandemic

The COVID-19 pandemic has brought great challenges within businesses. Easter brings challenges itself with the date of the holiday moving each year, however in 2020, businesses were simply not prepared for the impact of the pandemic. Easter egg sales fell by £36 million with many retailers having to sell lots of eggs at discounted prices. Some retailers were also unprepared for the boom in online sales and were not able to meet demand. On the upside, many businesses are better prepared for this year’s Easter period, with many focussing on online operations. Through demand forecasting techniques and last year’s data, businesses have been able to better prepare for this year’s demand. Many, for example Hotel Chocolat, are offering a limited range of Easter eggs this year.

As well as benefitting the retail and leisure industries, demand forecasting is used by other organisations over Easter. The NHS use forecasting techniques to predict demand and capacity for their services. This has been particularly important during the pandemic. In the January peak, NHS hospitals were caring for over 34,000 COVID-19 patients in England, approximately 80% higher than the first peak in 2020. Demand forecasting and mathematical models are being used to predict hospital bed demands frequently, tailored to specific hospitals, to help the NHS and government plan for future holiday periods such as Easter.

Demand forecasting is an effective approach that is being used by many businesses to plan for this year’s Easter holiday. Data-driven businesses can make well-informed decisions for the future, and as a result many will be better prepared this Easter.

Can we help with any aspect of your demand forecasting? Read our case study to find out how we have helped other companies with this.

world water day
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As we celebrate World Water Day, we consider the lack of access to safe and affordable water – a dangerous reality for billions across the world. It has a damaging effect on not only the health of billions of people but also on many aspects of their lives. With climate change comes droughts, floods and scarcity of water, bringing with it social and economic devastation. For example, clean water and sanitation is an important factor in reaching equity amongst gender.

By 2030, the UN aims to achieve universal and equitable access to safe and affordable drinking water and adequate sanitation and hygiene for all. To reach these targets, it is vital that businesses create and achieve their own targets for water consumption to help make a positive change. Unfortunately, many of these targets are not being met.

Using the power of data to make a positive change

In data-driven world, data science allows us to harness the power of data to make positive change. It can be used in several ways to improve water quality and water consumption around the world:

  • The innovation of machine learning can be used to improve the way in which water is collected and transported to reduce CO2 emissions. Moreover, it can be used to improve the treatment and utilization of water.
  • Real-time monitoring gives communities the power to ensure water is safe to drink while saving on resources.
  • Data analysis allows predictions to be made about the quality of water given a number of factors like weather and pollution. This allows for better planning when it comes to supplying clean drinking water to those in need.
  • Identification of water supply issues can be significant in preventing the spread of diseases through water supplies.

Mango realises the importance of using data to help other businesses bring positive change to the world. Data science can be used to inform businesses on their water targets and make steps towards reaching them. As well as harnessing data, it is equally important to involve stakeholders in decision making to identify, understand and overcome water challenges.

Reducing water waste

Water waste is a problem that many businesses face, with more than 25% of water wasted being due to leaks. This can be significantly reduced through the use of data science and in turn help businesses reduce their water consumption.

One company, i20, provides smart network solutions designed to help water companies reduce their leaks and bursts, energy use and CO2 emissions. The company recognised that it was collecting a large amount of data but were not harnessing it. Mango were able to not only help i20 realise their data capabilities but develop a solution that greatly improved the performance of their smart network solutions, leading them into the world of AI and data analytics. This has enabled water companies to shift to conditional based maintenance and reduce the number of water leaks. One client reduced leakage by 15% in the north of their city within 2 weeks of using the solution.

Data science is a powerful tool which can be used to inform businesses and improve their water consumption as well as having world-wide applications in reaching the UN targets of providing clean, accessible water to all.

Find out more about how Mango has helped i2o harness their data.

 

Global Recycling Day
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It’s Global Recycling Day today and a day to raise awareness of the importance of recycling and how crucial and lasting change can help preserve the future of our planet. Recognised in the UN’s Sustainable Development Goals 2030, we are already seeing many individuals, governments and organisations taking direct action to support the global green agenda.

Founded by the Global Recycling Foundation, this year’s theme is #recyclingheros and recognises people, places and organisations that inspire us – demonstrating positive action.

From plastic pledges and recycling,  to waste targets, businesses are positively impacting the environment, and data science is being used positively to reduce environmental impact and financial costs – aligned to their corporate responsibility. There are many examples of data analytics applications that can play just a small part in decelerating the process of climate change – the more focus that organisations place on this, the brighter the outlook for our planet. We decided to take a look at some of the positive use cases.

Food waste

Think about the waste problem in supermarket fresh food sales. Many businesses are using data science to help the UK meet its target of eliminating food waste to landfill by 2030. Analytics of weather patterns can help supermarkets ensure they have the right amount of seasonal produce to meet demand for a particular weather period without wastage; and enhanced analytics of customer weekly shopping habits would mean the store could ensure it has met demand without having surplus fresh food.

Gousto, a British meal kit retailer, implemented forecasting algorithms in an effort to reduce their food waste. They were able to predict demand and analyse seasonal trends to better manage their fresh food stock. Forecast modelling allowed the business to not only predict with a high degree of confidence the number of orders they would receive in future weeks, but also predict the performance of existing and new recipes.

Plastic waste

Plastic waste posing a considerable threat to our planet, with 8 million metric tons of plastic being added annually to the world’s oceans. That is why many businesses in the UK have come together to work towards having all plastic packaging recyclable, reusable or compostable by 2025. One such company is Tesco who are rolling out collection points for soft plastic packaging in their stores. Tesco’s efforts should help the public in their efforts to recycle as well their own.

Outside of the UK, many companies are also reducing their plastic waste and increasing their recycling, with many also helping the public do so. The Gringgo Indonesia Foundation, with the help of Google, have used AI and machine learning to create an app to help better classify waste items. It can be used by businesses and the public to help improve their recycling. With the use of data science, within a year of launching the app, recycling rates were increased by 35% in their first pilot village.

Space junk

Space junk poses a danger to astronauts in orbit, the world’s network of communication and weather satellites. Luckily, data science is here to help. NASA have been developing technology to remove space junk. Using machine learning algorithms, NASA are working towards improving the detection of space junk for removal.

Clinical waste

Since the start of the COVID-19 pandemic, there has been an increase in single use plastics and clinical waste. With only 15% of clinical waste being hazardous, there is a massive opportunity to reduce and properly manage clinical waste using data science. From reducing the number of unnecessary hospital appointments to the size of some healthcare equipment, a positive change can be made.

Reducing waste and recycling is vital for the future of our world. Data science provides many tools for creating and implementing solutions, and with data-driven businesses striving to reduce their waste, the future looks bright.

To discuss any use cases to align your recycling goals, contact us.

Author: Elizabeth Brown, Professional Placement Student at Mango

British Science Week
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As a data science consultancy, we’d like to celebrate British Science Week #BSW21 and the innovation in science, technology, maths and engineering, as well as the diversity of these roles. Many of our graduate consultants join Mango from a Maths and Statistics background, but also equally from a science background where many of the data and analytic approaches, including R are introduced.

Can data science be classified as a Science? We asked our Consultants, and the answer was a resounding ‘yes’, as our Graduate Data Scientist, Elizabeth Brown explains. “In my opinion, Data Science is a Science. The goal of Science is to gain a better understanding of the world around us, to explain why things happen or to describe the relationship between concepts. A big part of science is taking this understanding and applying it to real world situations, whether that be making advancements in medicine as we have witnessed with the vaccine development, or a modern way of introducing scientific methods to automate processes and make more intelligent decisions – ‘innovating for the future’, just as the theme for British Science Week this year. Like a science, we make observations, come up with hypotheses and through experimentation, test our hypotheses”.

Rich Pugh, Mango’s Chief Data Scientist agrees, “When done right, data science should be based on, and resemble, the scientific method. We formulate a “hypothesis” (although the structure of this can vary across application), then we use data to test that hypothesis”.

“Data is everywhere and being able to use it effectively to improve our understanding of the world is very exciting – expanding data-driven decision making, scientific discovery and  automation”, explains Elizabeth.

The growing capabilities of AI and machine learning are paving the way for real world solutions such as self-driving cars, much needed fraud prevention and addressing climate change. Fundamentally, data science isn’t just solving business problems, as a career it can support initiatives to create a healthier, greener and kinder world.

If you are interested in a data science career with Mango, click here to find out more.

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It’s mostly preaching to the converted to say that ‘open-source is changing enterprises’. The 2020 Open Source Security and Risk Analysis (OSSRA) Report found that 99 per cent of enterprise codebases audited in 2019 contained open-source components, and that 70 per cent of all audited codebases were entirely open-source.

Hearts and minds have most certainly been won, then, but there are still a surprising number of enterprise outliers when it comes to adopting open-source tools and methods. It’s no surprise that regulated industries are one such open-source averse group.

It’s still difficult to shake off the reputation open-source resources can have for being badly-built, experimental, or put together by communities with less recognisable credentials than big players in software. When your industry exists on trust in your methods – be it protecting client finances in banking, or the health of your patients in pharma – it’s often easier just to make do, and plan something more adventurous ‘tomorrow’.

This approach made a certain amount of sense in years past, when embracing open-source was more a question of saving capex with ‘free’ software, and taking the risk.

Then, along comes something like Covid-19, and the CEO of Pfizer – who are now among those leading the way in a usable vaccine – singing the praises of open-source approaches back in March 2020. Months down the line, AstraZeneca and Oxford University’s 70 percent-efficacy Covid-19 vaccine emerged. AstraZeneca is having a public conversation around how it’s “embracing data science and AI across [the] organisation” while it continues to “push the boundaries of science to deliver life-changing medicines”.

Maybe tomorrow has finally arrived.

At Mango, our primary interest is in data science and analytics, but we also have a great interest in the open-source programming language R when we’re thinking about statistical programming. We’re not attached to R for any other reason than we find it hugely effective in overcoming the obstacles the pharmaceutical industry recognises implicitly – accessing better capabilities, and faster.

With a growing number of pharmaceutical companies starting to move towards R for clinical submissions, we thought it would be useful to find out why. Asking experts from Janssen, Roche, Bayer and more, we collected first-hand use cases, experiences and stories of challenges overcome, as well as finding out how these companies are breaking the deadlock of open-source’s reputation versus its huge potential for good in a world where everything needs to move faster, while performing exceptionally. Watch the full round table recording here.

If you’d like to find out more, please get in touch and we’d be happy to continue the conversation.

Author: Rich Pugh, Chief Data Scientist at Mango

International Women's Day
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On International Women’s Day, we’d like to celebrate the achievement of our own talent, raise awareness against bias and take positive action for equality. At Mango we believe in creating inclusive and diverse workplaces. A workplace that offers challenges and an active learning environment.

Kasia Kulma, one of Mango’s Senior Data Scientist’s, reinforces the many benefits that diversity brings to the workplace, her opinions are shared and published in Diversity Q, FE News and Education Technology.

According to Kasia, “parity in tech companies can be hugely beneficial to the industry as a whole be it in respect to gender, ethnicity, age, sexual orientation or other. Some of them are rather pragmatic, for example, by embracing a more diverse talent pool we can address talent shortages and progress to closing the talent supply/demand gap. More fundamentally, though, diversity brings a variety of perspectives which has a knock-on effect in increased creativity and thus faster problem solving and improved products. The company culture can, benefit significantly too. By helping employees feel included, no matter their background or gender, it can break down barriers and reduce the fear of being rejected”.

“This is a great way to empower your employees”, says Kasia and “harness their ideas and thoughts and attract talent”.

Closing the diversity gap

When you increase the investment in training for women to fill the corporate need, we believe it goes a long way to the overall goal of closing the gap once and for all.

According to Kasia, “the tech industry is very dynamic and it could offer the most creative and stimulating of environments. It gives you the flexibility to work cross-industry or to specialise in one area if you like. You could work on the bleeding edge of innovation and devote your time and career to something you really care about.”

“The most important advice I could give to an aspiring women technologist would be to look for employers that offer a supportive environment and embrace diversity – this way you’ll be more likely to spread your wings and learn. There are more and more tech companies that understand the importance and value that the workplace diversity brings, so join them… and enjoy the ride!”

Happy International Women’s Day!

If you’re interested in a career in data science,  you can find out more about Mango and check out our current vacancies.

 

managed service
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As a language, R can come with barriers when it comes to the implementation and necessary technical know-how of installing, configuring, and supporting a centralised data science platform. As a full service Certified RStudio partner in Europe, we have introduced a new service to help overcome such technical hurdles associated with scaling your R environment.

Designed to be up and running effectively almost immediately, this expert Managed Service, removes the need for specialist in-house IT expertise and guarantees a service level agreement to meet your requirements in terms of configuration, maintenance and system updates.

Is Managed Service for you?

A Managed Service RStudio environment gives you the benefit of a quick and effective cloud environment, run and maintained by Mango Solutions to minimise client responsibility. It presents a solution for reducing concerns of supporting an appropriate infrastructure for data science teams, allowing them to focus their valuable time on vital project collaboration and their core area of responsibility , rather than needing to have any concerns over their system configuration and maintenance.

The benefits an RStudio Managed Service provides:

  • Quick and effective installation – the environment is setup in the Cloud, negating the need for Linux experts within your business
  • Outsourced management – guaranteeing an excellent service level agreement (SLA) with automatic updates, managed maintenance, and reporting
  • Option for pre-installed validated packages – using ValidR® the solution maximises assurance of a compliant environment and provides the reassurance of knowing the code is robust, effective, and reproducible
  • Predictable low cost – outsourcing complex solutions ensure simplified budgets and costs
  • Proven expertise – provides levels of support to run and maintain to meet your business, reducing the time and support from your over-burdened IT teams
  • Option to engage with additional Data Science services to grow your knowledge and productivity

Keen to know more? We can demonstrate how this is already providing an effective solution within Government departments – talk to our Managed Service team.

adding data thinking to software solutions
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Whilst the world of Data and AI offers significant opportunity to drive value, knowledge of their potential and mechanics are mostly confined to data practitioners.

As a result, when business users look for solutions to their challenges, they are typically unaware of this potential.  Instead, they may ask technical teams to deliver a software solution to their problem, outlining a capability via a set of features.

However, when making this leap we risk missing out on the opportunity to build more effective systems using data and analytics, creating “part solutions” to our challenge.

Let’s use a real-life example to illustrate this …

Case Study: Customer Engagement

One of our customers is a major financial services firm, which has a number of touch points with their B2B customers.  This can include a variety of interactions including customer support calls, service contract renewals and even customer complaints.  These interactions are driven by their large, globally dispersed customer team.

The goal of the customer team is to increase retention of their high-value customers and, where possible, to upsell them to more expensive service offerings.  As such, they see that every interaction is an opportunity to build better relationships with customers, and to suggest compelling offers for new products.

Let’s imagine the head of this customer team looks for support to better achieve their aims … 

Software-first Projects

A classic approach would be for the customer team to turn to the world of software for support.

Knowing the possibilities that a modern software system can bring which puts all the information about a customer in front of the customer team member during interactions (akin to a “Single Customer View”).  This information could include:

  • General customer details (e.g. sector, size)
  • Purchasing history (e.g. services they current subscribe to, volumes)
  • Usage (e.g. how often they use a particular product or service)
  • Recent interactions (e.g. what happened during the last interaction)
  • Offers (e.g. what did we last offer them and how did they react)

This could create an invaluable asset for the customer team – by having all of the relevant information at hand they can have more informed discussions.

However, the customer team still needs to fill the “gap” between being presented information and achieving their goal of customer retention and product upsell.  They do this using standard scripts, or by interpreting the information presented to consider appropriate discussion points.

So while the software system supports their aims, the human brain is left to do most of the work.  

Data-first Projects

In the above example, the head of the customer team didn’t request a software system – instead, she turned to an internal data professional for advice.  After some conversations, the data professional identified the potential for analytics to support the customer team.

They engaged us with the concept of building a “next best action” engine that could support more intelligent customer conversations.  Working with the customer team and the internal data professional, we developed a system that presented the relevant information (as above), but crucially added:

  • Enriched data outputs (e.g. expected customer lifetime value)
  • Predicted outcomes (e.g. likelihood that the customer will churn in next 3 months)
  • Suggested “next best actions” (e.g. best offer to present to the customer which maximised the chance of conversion, best action to reduce churn risk)

These capabilities spoke more directly to the customer team aims, and demonstrated a significant uplift in retention and upsell.  The system has since been rolled out to the global teams, and is considered to be one of a few “core applications” for the organisation – a real success story.

Software vs Data Projects

It is important to note here the similarities in the delivery of the system between these 2 approaches: fundamentally, the majority of the work involved in both approaches would be considered software development.  After all, developing clever algorithms only gets you so far – to realise value we need to implement software systems to deliver wisdom to end users, and to support resulting actions by integration with internal systems.

However, the key difference in mindset that leads to the approaches described are driven by 2 characteristics:

  • Knowledge of the Data Opportunity – a key factor in the above example was the presence of a data professional who could empathise with the head of the customer team, and identify the potential for analytics. Having this viewpoint available ensured that the broader capabilities of software AND data were available when considering a possible solution to the challenge presented.  Without access to this knowledge, this would likely have turned into a “single customer view” software project.
  • An Openness to Design Thinking – in the world of software design best practices, there are 2 (often conflated) concepts: “design thinking” (empathise and ideate to develop effective solutions) and “user-centred design” (put the user first when designing user experiences). In software-first projects, the focus is often on the delivery of a solution that has been pre-determined, leading to a user-centred design process.  When we consider the world of data, the lack of understanding of the potential solutions in this space can lead more naturally to a “design thinking” process, where we focus more on “how can we solve this challenge” as opposed to “how do I build this software system really well”. 

Adding Data Thinking to “Software-First” Projects

So how do we ensure we consider the broader opportunity, and potential that data and analytics provides, when presented with a software development project?   We can accomplish this with 3 steps:

  1. Enable a Design Thinking Approach

Design thinking allows us to empathise with a challenge and ideate to find solutions, as opposed to focusing on the delivery of a pre-determined solution.  Within this context, we can focus on the broader aspirations, constraints and consequences so that a solution can be considered which connects more closely to the business outcomes.

  1. Include Data Knowledge

During this design thinking activity, it is essential that we have representatives who understand the potential that data and analytics represents.  In this way, the team is able to consider the broader set of capabilities when designing possible solutions.

  1. Design the Data flow

Data is always a consideration in software design.   However, the potential of analytics requires us to think differently around the flow of data through a system with a view to delivering value-add capabilities.  This takes us beyond thinking about how we store and manage data, and towards a situation where we consider new data sources, data access, and the lifecycle of model-driven data outputs (such as predictions or actions).  This is particularly important where the “data” opportunity may be added to a system at a later date, once core “nuts and bolts” functionality has been delivered.

Data + Software + Design Thinking

The approach described here enables us to leverage the opportunity that resides on the bounds of data and software, and fundamentally deliver more value to users by delivering richer capabilities more aligned to business outcomes.

Moreover, we’ve seen that effective application of design thinking, combined with deep knowledge of data, analytic and software, has enabled us to deliver significant value for customers that goes way beyond solutions that may have been originally imagined.

Author: Rich Pugh, Chief Data Scientist

 

rstudioconf
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We’re delighted to be sponsoring the rstudio::global conference this year. It’s a credit to the community that such events (including our own flagship EARL conference) have been so readily able to respond to the pandemic lockdown and transform to a fully virtual presence, providing inspirational talks on all things R. 

We are excited to see John Burn-Murdoch, senior data visualisation journalist at the Financial Times amongst the keynotes.  John led the FT’s data-driven coverage of the pandemic, amassing an enormous following on social media for his incisive reporting.  It will be fascinating to hear the lessons he has learned from his reporting and visualisations and how he addressed the challenges of communicating often complex findings to the population at large. It was our pleasure to have had John speak at the EARL conference in both 2014 and 2016 – so we know that the rstudio::global audience can expect a riveting presentation. 

With a packed itinerary and 24-hour streaming for accessibility all over the world, there will be some extremely useful presentations and stimulating conversations to be had for the 10,000 expected data professionals.  

As a sponsor of this event and as RStudio’s longest serving Full Service Certified Partner, we would like this opportunity to invite attendees to meet us in our virtual booth. Whether you are scaling the use of R in your organisation and require technical advice on setup or configuration, lacking internal IT to support the required maintenance of RStudio products or have reservations around the validation of open-source packages from a security or malware perspective, we can help.   

Some of the services that we offer include: 

  • A Managed Service providing a scalable RStudio environment which can be effectively built up, run in the cloud and fully maintained by Mango, to minimise the responsibility and burden on your inhouse IT teams. 
  • An On-Premise solution designed to address current in-house service gaps; following an Installation, Accelerate and Healthcheck review, this service offers the full installation, configuration and maintenance of RStudio products. 
  • A new validation service through ValidR® presents a validated collection of the 150 most popular industry leading R packages, such as those within the tidyverse and can be deployed with RStudio Package Manager (RSPM) to mitigate any uncertainty of using opensource software, with guaranteed reproducibility for any data science team. 

We’re very much looking forward to seeing you at the event on 21st January – don’t forget to sign up for this event now if you haven’t already. 

#Rstudioconf2021 #rstats #RStudiofullservicepartner