Case Studies

Automating and optimising processes to enhance productivity

In the Marketing sector, Data Science and Machine Learning techniques can help predict customer acquisition, retention and profitability to improve business outcomes. There are a plethora of Data Science tools and techniques available to analysts. Therefore, ensuring analysts understand when and how to apply these techniques to generate business value is essential.

“We worked with the team at Cambridge Spark to define a package that would suit our needs specifically – It was absolutely relevant to us,” says Matt Wilson, Senior Manager, EDF Energy. “We’ve actually re-used a lot of the code put together in the training exercises to deploy production models – the workbooks completed as part of the training are referred to by our data science team frequently.”

The Business Outcomes

“Utilising the skills picked up in this training we have been able to market to our customers and prospects more effectively, using less resources to acquire and retain customers.
We’re now moving forward and looking at using some of the methods we’ve picked up as a result of this training to fix some larger business problems,” says Matt Wilson. “We’re 5 months on from the training now, the training was very useful for us to build capability quickly, we used it to quickly deploy a series of classification models for marketing optimisation (predicting campaign outcome).
We’re still using them now, however, they have been vastly improved on, tuned and fully automated.”

Designing new product offerings that add value to your clients

New research and technologies are continuously emerging in the fields of Big Data and Data Science. To keep up to date with the latest advancements, Deloitte chose Cambridge Spark to provide on-site data science training for their Consulting Analysts.

The training equipped analysts with Deep Learning, Natural Language Processing and advanced analytical skills to bring to new projects and client offering across healthcare, life science, retail and industrial products.

The Business Outcomes

“There are always new algorithms and tools coming out that we need to learn about, and these courses help us understand the fundamentals then dig deeper,” says Fei Liao, Technology Consulting Analyst at Deloitte. “At Cambridge Spark, you have really exceptional trainers who are specialists in the field. We can definitely trust your team and your expertise.”

“I focus on delivering data-driven solutions to clients, most recently for the cognitive and robotics automation projects. Deep Learning and neural work are such hot topics in this field, so myself and my colleagues need to keep up with these new advanced technologies. I’ve been really looking forward to this training to learn more,” says Fei Liao. “We have a good relationship with Cambridge Spark, last year we ran the Machine Learning Bootcamp in Python. The course received really good feedback from attendees and we have continued to do great stuff together.”

Enhancing the education and employability of students

Combining mathematics and statistics, computer science and domain expertise, Data Science is an interdisciplinary skill set that can be applied in every field and an essential secondary skill set students can learn to complement their academic studies. There are an abundant number of tools and techniques that can help students analyse vast amounts of data for their research, and in doing so, help them build skills that are highly sought after by industry.

“The course content was varied enough and advanced enough to meet the diverse requirements of our group. As the attendees are from different backgrounds, researching completely different areas, we will all use the topics to different degrees and in different ways,” says Simon Blackwell, PhD Research Student at Loughborough University.

The Training Outcomes

The in-person Core Data Science training was complemented by continuous learning projects using K.A.T.E., Cambridge Spark’s Knowledge Assessment Teaching Engine. Using K.A.T.E., students can keep experimenting with Python exercises as well as Machine Learning tasks within a simulated work environment and receive instant feedback to help improve their code.

“The objective of the programme was to give researchers an understanding of the concepts and benefits of Data Science and Machine Learning, and a solid foundation on how to apply those techniques, says Simon Blackwell. “The most enjoyable part was the informal delivery with great dialogue. We were encouraged to ask questions and free to deviate from the material whenever useful. The instructors were knowledgeable without being unapproachable.”