Hackathons are widely acknowledged to be very good value for companies that support and sponsor them. They help to encourage innovation in several different ways, including through bringing in new talent and new ideas. Especially in older, more established firms, the benefits of shaking up the culture slightly cannot be
Tag: data science
What is the ideal training and development programme for data scientists? Discuss. This ‘exam question’ crops up time and time again in discussions with students, academics, data scientists, and across the business community. This is in large part because in a fast-moving field like data science, the quality of training
Data science continues to become more and more critical, especially in the education sector. There is an ever-growing need to encourage and persuade younger generations to choose a career pathway in STEM areas from an early stage in their education, not least because organisations are struggling to find the analytical
We already know that work and workplaces are changing. But what do these changes mean for the skills that students should be developing to improve their employability? Previous generations had a simple recipe for success in their jobs: They chose a profession, acquired foundational knowledge and slowly became an expert
Employability in general, and in data science in particular, means your skills must adapt to business needs. Thomas Davenport and D.J. Patil described data scientist as “The Sexiest Job of the 21st Century” in 2012. Since then, understanding has grown that businesses across industries need employees who can work with
Employability and data science There is general agreement that employment is changing. There is, however, much less agreement about precisely how it will change, although plenty of crystal-ball gazing has gone on. A study published by the World Economic Forum in 2016 suggests a rather nuanced situation with employment fluctuations
Recently I have been thinking about data preparation, but not just any kind of data preparation. I have been thinking about the preparation required for advanced analytics and predictive models. Recently, I had to explain the process of how to create this data and this proved to be somewhat challenging given a
Hackathons - short-term programming events using data, APIs and analytics to solve real-world problems are great for team-building, recruitment, networking and experimenting. But there is a growing sense that they are also contributing to banking innovation. Here are my observations on the subject. Hackathons are a relatively cheap way to
Data science and machine learning are riding the popularity wave. There is plenty of buzz in social media, crowds at meetups and conferences, and rising interest in postgraduate studies in this area. There is clearly a growing awareness of the power of advanced data analysis methods and the benefits that
In Part 1 and Part 2 of this blog posting series, we discussed: Our current viewpoints on marketing attribution and conversion journey analysis in 2017. The selection criteria of the best measurement approach. Introduced our vision on handling marketing attribution and conversion journey analysis. We would like to conclude this
In Part 1 of this blog posting series, we discussed our current viewpoints on marketing attribution and conversion journey analysis in 2017. We concluded on a cliffhanger, and would like to return to our question of which attribution measurement method should we ultimately focus on. As with all difficult questions
I’m a bit of a data nerd, and especially a data visualisation nerd. I love graphs and charts, but not for their own sake. What I love about them is the way that a single picture can show so many different things, and often provide new business insights into data.
Everyone has a marketing attribution problem, and all attribution measurement methods are wrong. We hear that all the time. Like many urban myths, it is founded in truth. Most organizations believe they can do better on attribution. They all understand that there are gaps, for example, missing touchpoint data, multiple
W ostatnim czasie zalała nas bezprecedensowa fala popularności tematyki Data Science/Machine Learning. Mnóstwo szumu w mediach społecznościowych, tłumy na meetup-ach i konferencjach, popularność profilowanych studiów podyplomowych – to zaledwie kilka jej przejawów.
From June 6 to 9 the first ever ‘Dutch Data Science Week’ took place in the Netherlands. The week consisted of 4 days in which 28 events took place in 6 different locations. During the week people could attend classes ranging from beginner to advanced levels, visit meetups, participate in
There is a saying in business that you can have any two out of good, fast and cheap. All three cannot be done, or at least only in an ideal world. There is therefore a strategic trade-off between the three, with a recognition that every business has a different balance
How can you tell if your marketing is working? How can you determine the cost and return of your campaigns? How can you decide what to do next? An effective way to answer these questions is to monitor a set of key performance indicators, or KPIs. KPIs are the basic
Diversity is a big topic in the press at the moment. In July, the UK’s BBC published data about pay, exposing a huge gender pay gap. But the question of diversity goes far beyond a simple pay gap. I caught up with Josefin Rosén to discuss how organizations can harness
A growing area of focus is analytics developer experience, and for good reason. As with applications, analytics teams need to respond to business needs in an agile manner, and developers play a crucial role. But how exactly does developer experience fit into the broader scheme of things? I asked Mark
Wo können Sie Ihr Können mit dem von anderen Experten vergleichen? Und in der Data Science Community über den Tellerrand blicken? Oder einfach ungezwungen programmieren - wie zu Studienzeiten - weil es Spaß macht? Zum Beispiel bei einem Hackathon. Mit Fabian Buchert, selbst Data Scientist, sprach ich über seine Erfahrungen beim
I started my training in machine learning at the University of Tennessee in the late 1980s. Of course, we didn’t call it machine learning then, and we didn’t call ourselves data scientists yet either. We used terms like statistics, analytics, data mining and data modeling. Regardless of what you call
Ensemble methods are commonly used to boost predictive accuracy by combining the predictions of multiple machine learning models. The traditional wisdom has been to combine so-called “weak” learners. However, a more modern approach is to create an ensemble of a well-chosen collection of strong yet diverse models. Building powerful ensemble models
A big part of our existence is about making choices. Preferably the best ones. Evidence-based decision-making matters more than the type and quantity of the data, and by establishing evidence-based decision-making as a deep corporate culture, you could fulfill data’s vast potential. I find the resemblance between this way of
In 1977, George Lucas released the first film in what would become the epic Stars Wars series. Back then, who could have predicted that this film would spawn the huge cultural phenomenon that is Star Wars? Films, congresses, spin-offs, toys: the list goes on, and over 40 years this year.
The development and use of self-service analytics has brought with it a new role in many organisations: the citizen data scientist. But is this genuinely a new role, or is it just a new name for a business analyst? Is this a definition thing? Business analysis is broadly defined as
In 1901, Gottlieb Daimler predicted: “The global demand for motor vehicles will not exceed one million—simply because of the lack of available chauffeurs”. Today, most people drive themselves and self-propelled cars are rapidly becoming a reality. Are we likely to see the same situation for data scientists, with more and
This resource is designed primarily for beginner to intermediate data scientists or analysts who are interested in identifying and applying machine learning algorithms to address the problems of their interest. A typical question asked by a beginner, when facing a wide variety of machine learning algorithms, is “which algorithm should
To make faster and more accurate decisions for better results is an everyday battle to management in organizations. Those who use data wisely will be the winners. Becoming a data driven organization is the key, but it often requires a change of mindset and stepping out of the old habits.
Digitalization is supposed to change everything, or perhaps even revolutionize everything, in both our private and professional lives. The way we live and work will change to match a digital society, whether we like it or not. Structures and organizational forms in companies will also change. And these changes will
The social media buzz around the Data Science Enthusiast Meetup in Istanbul on 17th Feb was hard to miss. Tuba Islam, Senior Business Solution Manager at SAS, was one of the speakers and she hosted a session around Analytics in Action. I caught up with her after the event and