This is Harnham’s weekly news digest, the place to come for a quick breakdown of the week’s top news stories from the world of Data & Analytics.Tech To Freedom: Five habits of insanely productive software engineersSoftware Engineering is a very special expertise, not to mention that it boasts some of the highest salaries around. Of course, as with all roles, years of experience can make a software engineer more efficient, but Tech for Freedom identify five tips for boosting your productivity, even if you’re just starting out. Here are just a couple: Learning by doing: Technology is evolving very quickly, so for a software engineer there is no time to rest on their laurels, they must be constantly learning. The speed of industry developments means that professionals don’t tend to have time to read hundreds of articles or take numerous courses in order to learn something new, instead they are likely to jump into the deep end and learn by doing. Asking for help: It would be impossible for any one person to know everything. So, one of the essential survival skills for software developers is knowing how to ask for help.You may have ten years’ worth of Python programming under your belt, but now you need to develop something using a special module/tool/framework that you have never used before. The most efficient way to solve your problem is to employ help from someone who does have the experience in that tool. A good engineer knows that titles like ‘junior’ and ‘senior’ do not hold much weight, every engineer, no matter what title they have, has a unique knowledge and experience.Read further insights here.Wealth Professional: Financial firms can’t agree on how to address climate riskWhile the risk to financial firms from climate change is considered a top priority, Bloomberg’s poll of 100 executives from financial services firm shows that there is still some way to go to address it.The survey revealed that while 85 per cent of firms have begun to assess the impact of climate risk, there is no consensus on how it should be embedded into risk management frameworks. Of these, 37 per cent are still in the early stages of planning how to incorporate climate risk into models and governance.When asked about the results, Zane Van Dusen, Head of Risk & Investment analytics products at Bloomberg, said: “…even those who say they have a robust model will be making significant changes over the next few years as our understanding and consensus around climate risk grows… More and better data will go a long way toward improving firms’ ability to manage climate risk.”Find out the key sticking points for the respondents here.The New Statesman: How data can help revive our high streets in the age of online shoppingHigh streets and town centres across the UK have undergone substantial transformations in recent years. Falling footfall, lost revenues and mounting fixed business costs have had a negative impact on traditional ‘bricks and mortar’ retailers, triggering a large wave of insolvencies across the UK.At the University of Liverpool, researchers have been utilising data and advanced geospatial algorithms to provide various retail-related research outputs and data products. The work is essential for the systematic monitoring of the performance of UK retail centres, giving the team a better understanding about retail centre exposure to current societal and market driving forces. Which will then allow them to track and predict the evolutionary trajectories of any given high street. As a result, in Liverpool they have estimated two types of retail catchments: drive times and walking distances, and then created profiles of those catchments based on numerous measures including deprivation, exposure to internet sales and geodemographics. It is hoped that these tools will aid policymakers, at both a local and national level, in making the decisions that will help revive flagging high streets and level up communities across the UK.Read more here.Technology Works: AI Reliably Predicts Structure of RNA MoleculesThe three-dimensional structure of biomolecules is crucial to their function. Therefore, researchers are interested in knowing more about their spatial structure, and with the help of artificial intelligence (AI), bioinformaticians can already reliably predict the three-dimensional structure of a protein from its amino acid sequence.But for RNA molecules (ribonucleic acid) this technology is still very underdeveloped. Researchers at Ruhr-Universität Bochum have found a way to use AI to reliably predict the structure of certain RNA molecules from their nucleotide sequence.“Identifying these self-similarities in an RNA sequence is like a mathematical puzzle”, explained researcher Vivian Brandenburg. The biophysical model for this puzzle cannot consider the cellular environment of the RNA – in other words it cannot process everything around the RNA.This is where AI comes into the mix. The AI can learn subtle patterns from the cellular environment based on known structures. It could then incorporate these findings into its structural predictions. But for this learning process, the AI needs sufficient training data – and this is lacking.To solve the problem of missing training data, the team used a trick. By working with known RNA structural motifs, researchers used a ‘reverse gear’ to allow them to generate almost any number of nucleotide sequences from the energy models of these structures, that would fold into these spatial structures. With the help of this ‘inverse folding’ the researchers generated sequences and structures with which they could train the AI.Find out if the process worked here.We’ve loved seeing all the news from Data & Analytics in the past week, it’s a market full of exciting and dynamic opportunities. To learn more about our work in this space, get in touch with us at email@example.com.