Our approach to Data Science is to act as the guide and facilitator to our customers; we use our knowledge and architecture to break down the barriers between business functions and to enable your IT, data science and business users to extract incremental and very tangible business value from your data.
Typical use cases data science teams support retailers with:
Merchandising and Visual Merchandising
Customer Journey Analytics
Customer Sentiment Analysis
Vincent's interest in algorithms and data led him to do a Master in Applied Physics at the Delft University of Technology followed by a PhD in computational physics. For his PhD, massive simulations on supercomputers were used to unravel the chaotic behaviour of colliding cloud droplets in their turbulent environment. Designing and optimising new parallel algorithms was at the core of the project to ensure exceptional scalability and fast data processing.
Data Science perspective
Analysing vast amounts of data is nothing new. Improved computing power and open source algorithms/APIs have made data science just much more accessible to a wider audience outside the academic world. Even with all advances which have been made in the recent years, it is essential to combine ML with creativity to truly unlock its potential.
Julian's interest in the diversity of techniques aimed at bringing sense and structure to crude data, led him to pursue a Technical Artificial Intelligence - Computer Science Master at the Vrije Universiteit Amsterdam, where he expanded his hands-on experience with the complementary theoretical framework.
Data Science perspective
The process of doing data science allows us to use tools like ML/AI, modern computing technologies, and statistics to strive to make informed decisions by generating objective and accurate observations about existing data.
Data Science is a discipline that uses scientific methodologies, workflows, models, algorithms and systems to extract knowledge and insights from data.
The Data Science definition can be referenced back to this .
In summary, Data Science is the art of extracting and applying actionable insights from your data.
The term Big Data refers to a massive volume of both structured and unstructured data that is so large it is difficult to process using traditional database and software techniques. In most enterprise scenarios the volume of data is too big or it moves too fast or it exceeds current processing capacity.
Big Data has the potential to help companies improve commercial performance and make faster, more insightful decisions. Data can be collected from a vast number of different sources. This data when captured, formatted, manipulated, stored and then analyzed, can provide actionable insights that improve commercial performance, enhance customer experience, improve operational efficiency and provide infrastructure gains.
At Attraqt we recognise that there is a data iceberg. We use this term to describe that there is a lot of data that is either available or can be captured, but what is important is understanding what data is worth transforming, curating and applying in a systematic way in order to deliver more valuable outcomes. This requires technology to be combined with human ingenuity in a way that focuses attention on the areas that really matter and can make a significant difference. This sits at the centre of the Attraqt Advantage.
Peter Thomas, Chief Technology Officer, Attraqt
Data Science has a structured lifecycle with well defined methodologies and processes.
It is reliant on human expertise at each step. It is critical that a Total Cost of Ownership (TCO) approach is taken so that each step is optimized to ensure the best possible outcomes.
It is also essential that it is agile, rapid, transparent and understood.
Feature engineering is the process of transforming raw data into features that better represent the underlying problem to the predictive models, resulting in improved model accuracy on unseen data.
Dr. Jason Brownlee
At the end of the day, some machine learning projects succeed and some fail. What makes the difference? Easily the most important factor is the features used.
Prof. Pedro Domingos
Coming up with features is difficult, time-consuming, requires expert knowledge. ‘Applied machine learning’ is basically feature engineering.
Prof. Andrew Ng
It is the combination of platform and tools + people and expertise that creates the best Data Science commercial outcomes.
By working with Attraqt, your Data Science initiatives will deliver the following.
Empowering management to make more informed and more valuable decisions.
Better understanding and predicting future trends.
Enabling the organisation to better focus on the areas that will help the organisation achieve its goals.
Identifying new opportunities to enhance commercial performance.
Improving decision-making at all levels of the organisation through quantitative data-driven evidence.
Testing and validating these decisions to support continuous improvement.
Enabling the organisation to engage with its customers in the most compelling and relevant way.
Assisting the organisation to attract and recruit the right talent.