How can researchers make use of Data Science?

Combining mathematics and statistics, computer science and domain expertise, Data Science is an interdisciplinary skill set that can be applied in every field, from business and finance to the natural sciences and engineering.

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.

Case Study: Loughborough University’s Data Science training and student outcomes


To facilitate Data Science skills development, Cambridge Spark worked with Loughborough University to help PhD Research Students complement their academic studies with Data Science using Python. The three-day Core Data Science programme exposed students to essential Machine Learning techniques and how they can be applied to their research.

We chose Cambridge Spark because we wanted the training to be onsite at a reasonable price, and 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.

Simon Blackwell, PhD Research Student at Loughborough University

Our personalised, project-based approach

Taking into account the diverse needs and research interests of PhD’s, Cambridge Spark delivered a comprehensive training programme covering Core Data Science in Python. Over three days, students got practical experience in a range of essential Machine Learning techniques using popular Python libraries such as Numpy, Pandas, Scikit-Learn and Matplotlib.

The in-person training is complemented by continuous learning projects using K.A.T.E.®, Cambridge Spark’s Knowledge Assessment Teaching Engine. When learning with 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.

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.

Simon Blackwell, PhD Research Student at Loughborough University

Our onsite training outcomes

Students developed an intuitive understanding of Data Science techniques and their real-world applications. 

Techniques covered in the three-day Core Data Science programme include:

  • Exploratory data analysis using Pandas
  • Principal Component Analysis
  • Unsupervised learning
  • k-means clustering
  • Supervised Learning
  • The K-nearest-neighbour algorithm
  • Decision Trees
  • Ensemble models and Random Forests
  • Logistic Regression

Interested in training for your teams?

Whether you're looking to train 5 people or 100 people, we have a variety of scalable training solutions to help you address a wide spectrum of training needs within the fields of Data Science, Artificial Intelligence, or Software Engineering.

Please contact us with your details and any known requirements. We'll then get in touch and guide you through every step of the way.