The Aegon Analytical Academy, launched in 2013, is a 3-year program that has trained more than 80 data scientists and data engineers to become AI professionals at Aegon worldwide. Laurie, Alan and Welmoed – three recent graduates with diverse profiles – talk about their firsthand experiences in how the academy helped shape their careers within data analytics and AI.
Our 8-year long collaboration with Bol.com continues in 2021 with focus on upskilling business analysts. The program will prepare business analysts to identify, validate and communicate opportunities for analytics & AI solutions. By the end of the year 533 people from Bol.com will have been trained by GAIn. Read about how the company took on the challenge to start building data-driven business teams. This case study is from 2015 and was first published on MIcompany.nl.
Did you have a childhood dream of becoming a super hero or a pop star? Perhaps you wanted to be Indiana Jones or Lara Croft; something that seemed impossible. But nowadays it can be real, with the help of AI.
How do you choose which machine learning algorithm to use? It is a fascinating question, as an enormous amount of algorithms has been developed in recent years. To be able to address this question in some detail in this (short) blog post, we will limit ourselves to classification algorithms, which are the algorithms most commonly used in our branch.
Aegon started building its data analytics capabilities back in 2013, when they set up the Aegon Analytical Academy together with the training school of MIcompany, now called GAIn. Today, 8 years after its launch, the academy is still running and has upskilled more than 80 employees in AI & data across the company in 11 countries. Have a look back on how and why Aegon started this important initiative to educate its people. This case study is from 2015 and was first published on MIcompany.nl.
As part of the 3-year program of the Aegon Analytical Academy, participants complete two exchange projects of 3 months at another Aegon Unit, in many cases abroad. In Paula Genovés’ case, she went to Budapest to work on a churn prediction project, putting all her new skills and learnings into practice. Read on to learn about her experience building a model that could save the company more than €1,000,000.00 a year!
Berkeley, a small town twenty minutes east of San Francisco, is home to UC Berkeley, the oldest campus of the University of California. This prestigious university is famous for its excellent institution and has been the Alma Mater for more than seventy Nobel prize winners. In 1973, the university discovered something shocking about its admissions: 44 percent of all men that applied for a PhD position at UC Berkeley were accepted, whilst only 35 percent of women were accepted. This seemed to indicate gender discrimination in the selection process!
Oleksii Klymchuk, Senior Business Analyst at eBay, followed the GAIn Train-The-Trainer program in 2020 through the eCG Analytics University. We interviewed him recently to look back on this experience and learn how he applies his trainer skills in his daily job.
In 2020, the World Economic Forum published “The Future of Jobs Report”, indicating the top declining and emerging roles in the world. According to the publication, while 75 Million (mostly) clerical jobs will decline, 133 million new roles will emerge. Can you guess which roles are dominating the top 10 emerging roles? That’s right – data analysts, data scientists and other data specialists.
Nowadays, they are everywhere. Netflix uses them to determine what your favorite series is. Facebook uses them to recognize and tag its users in images. Google uses them for their translators, to interpret spoken language and even to develop a chess-playing bot which can defeat any of its human or artificial predecessors.
This week we hosted a training in one of our most promising new topics: Bayesian Networks – a method which shows how different variables together cause an event to occur. The strengths of the method lie in the fact that it can estimate and show the dependencies between all variables in the model and that prior field knowledge by the research team can be formally included in the model.