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Data science explained in 10 applications


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Slide notes of an interactive workshop at the Brussels Data Innovation Summit 2018. In this workshop for management and aspiring data scientists, we have briefly covered 10 key Data Science applications. For each application, we described what the application is about, and illustrated this with vivid example. As a bonus, we explained how we use such slides and workshops to inspire clients and prospects on generating their ideas to harvest innovative data science projects internally.

Published in: Business

Data science explained in 10 applications

  1. 1. Geert Verstraeten Managing Partner - Python Predictions Data Innovation Summit 27th June 2018 #DISUMMIT Seeing the forest through the trees Data science explained in 10 concrete applications
  2. 2. Geert Verstraeten – Seeing the forest through the trees – @pythongeert – #DISUMMIT Why this workshop? My cocktail problem in Data Science In the beginning of my career (2001), when I mentioned to people I was predicting human behavior using data, I received a lot of blank stares
  3. 3. Geert Verstraeten – Seeing the forest through the trees – @pythongeert – #DISUMMIT Nowadays (2018) my friend tells me that the camera on his smartphone has AI – and I’m doing the blank staring - “What does a camera with AI do? And why would I need that?” AI, machine learning and data science are hyped today – I decided to focus on some cool yet concrete applications of data science here
  4. 4. AIRPORT USE CASE IDEAS Geert Verstraeten – Seeing the forest through the trees – @pythongeert – #DISUMMIT SEEING THE FOREST THROUGH THE TREES I’ve aimed to provide an idea of what data science is, based on 10 concrete examples of 10 different and important data science applications
  5. 5. Geert Verstraeten – Seeing the forest through the trees – @pythongeert – #DISUMMIT AUDIO & TEXT ANALYTICS Some towns in Belgium are experimenting with a system to detect loneliness among older people – most will reply they are not, but the audio information (such as intonation) reveals much more than the text (link - in Dutch) Extracting useful information out of text and audio inputs Other examples: call centers may use audio signals to detect whether customers are likely to leave – or whether clients call them out of loneliness versus when they have issues with their product or service, but also chatbots are cool examples of improved text analytics
  6. 6. Geert Verstraeten – Seeing the forest through the trees – @pythongeert – #DISUMMIT RECOMMENDER SYSTEMS In his PhD for VDAB, Michael Reusens used recommender systems to match jobs to candidates – in that case it’s not only important that the candidate finds the job interesting, the idea is that jobs are recommended where the recruiter would find the candidate interesting - so it should be a match from both sides (link) Recommending the right offers or content to the right audience Other examples: movie recommendations by Netflix are probably the most known application in Data Science, but news recommendations work in similar ways, and dating apps might face similar challenges as VDAB. Recommending the right politician to the right voter could work in the same way but is obviously more sensitive ☺
  7. 7. Geert Verstraeten – Seeing the forest through the trees – @pythongeert – #DISUMMIT COMPUTER VISION SAS is contributing the a project named WildTrack, an organization counting the number of animals in the wild for certain species. While such studies depended on expert track finders before, everyone with a smartphone is now encouraged to take photographs of footprints, and using image recognition, they are classified into the right species, which leads to better information (link) Extracting useful information out of images and video Many other examples: two high school students predict forest fires by examining the soil – Octinion produced a prototype robot for picking strawberries at the right time – diagnosis of medical images surpassed human expert level (for example for detecting melanoma) – robotic weed control – detection of plastic waste in the ocean
  8. 8. PREDICTIVE ANALYTICS Geert Verstraeten – Seeing the forest through the trees – @pythongeert – #DISUMMIT Predicting behavior in a business context, with interpretation One of the most interesting projects we’ve worked on, is a project that started out as employee burnout prediction with sdworx, as presented on the Data Innovation Summit 2017. In this case, we learned a lot about absenteeism, constructed a model with good quality on aggregated level, and we taught sdworx how they can do similar projects for their clients (link). Some other examples: Companies are typically predicting who will buy a product, who will become a valuable client in the future, who will leave the relationship, who will repay their debts, who will show fraudulent behavior, who will leave their employer, … In most of these cases, the interpretation of the model is equally important as the predictive quality. A great summary can be found in this book
  9. 9. Geert Verstraeten – Seeing the forest through the trees – @pythongeert – #DISUMMIT EXPERIMENTAL DESIGN Testing different options and approaches to maximize impact Prediction by itself does not create value, but the resulting actions do. Yet to be sure value is created, these actions should be tested through carefully designed experiments. Experimentation is common in online environments, but also useful offline. It may range from simple A / B tests to complex tests – for example some debt collectors have optimized the way they collect through offline experimentation of collection strategies Few companies understand this better than, who created a platform allowing hundreds of employees to run hundreds of small experiments at any time - resulting in conversion levels 2-3x the industry average (link)
  10. 10. ANOMALY DETECTION Geert Verstraeten – Seeing the forest through the trees – @pythongeert – #DISUMMIT Data Science is in many cases concerned by understanding the patterns. Yet sometimes, it may be interesting to look at what deviates. For example, volcanic eruptions can be detected based on anomalies in thermal features such as geysers, hot springs and lava flows (link) Some other examples: anomaly detection is most commonly used to detect fraud (see for example how HSBC screens card fraud), crime in general (including cybersecurity) – such type of behavior is very volatile, but consistent in the fact that it differs from average, normal behavior. Another remarkable example: Chinese researchers are also using anomalies in the genepool to identify potential athletes. It may be interesting to detect what deviates from the pattern
  11. 11. Geert Verstraeten – Seeing the forest through the trees – @pythongeert – #DISUMMIT SEGMENTATION Grouping objects based on their similarity Perhaps one of the oldest applications in the book, but certainly incredibly valuable for exploratory work. One of the coolest projects we’ve done is predicting needs of Private Banking clients – not all wealthy clients of a bank expect the same benefits – some like VIP events, but others want immediate financial information, etc (no link or details due to confidentiality). Some other examples: Due to the strategic nature of segmentation projects, it proved difficult to find concrete cases online. But in this context, it may be fun to look at the different segments of participants in the Belgian Data Science community (article and dashboard).
  12. 12. Geert Verstraeten – Seeing the forest through the trees – @pythongeert – #DISUMMIT IOT ANALYTICS Extracting information of connected devices It is easy to find analytics of sensor data, but more difficult to find cases where the connectivity between devices is key. Google Nest is a great example – in the sense that it learns from past behavior (e.g. when someone is absent) and can communicate to other devices (e.g. to turn off the oven). Some other examples: The Belgian Red Devils are training using sensors that measure fatigue to prevent injuries, Volvo Trucks claims to be able to reduce standstill of it’s fleet by 80% by predicting the need for maintenance, and sensors are crucial for many applications in healthcare
  13. 13. NETWORK ANALYSIS Geert Verstraeten – Seeing the forest through the trees – @pythongeert – #DISUMMIT Sometimes the most important information is the network itself In specific cases, the understanding of a network of people and things can help solve a case. For example, in an analysis of the 9-11 attacks, It has been estimated that the 9-11 network could have been dismantled if just three central nodes had been eliminated (link) Some other examples: Network analysis is a common technique for fraud and crime detection. It can also serve to detect churn (wich customer will leave a company), to understand which companies are connected through shareholders and which authors are connected through publications, etc
  14. 14. Geert Verstraeten – Seeing the forest through the trees – @pythongeert – #DISUMMIT PROCESS MINING Understand and reduce inefficient processes one of the largest academic hospitals in the Netherlands uses process mining to reduce waiting times, increase efficiency and optimize treatments (link) Processes are everywhere: from job applications to complaint handling, road works, and new client onboarding. Increasing their efficiency often leads to reduced costs and improved (customer) experience. Another concrete example of using process mining to improve the Customer Journey: ING Belgium
  15. 15. About half the time was spend explaining the examples I had found in preparing the session, with help from many friends on LinkedIn In the other half of the time, the audience interacted with their often colorful examples, which resulted in a fun and interactive session that seemed to be appreciated Looking back at this, an exciting journey into exploring what Data Science is today, and how it leads to concrete value. Thanks to all who participated and provided input!