NOTE: The date is provisional. You can start the course as soon as you buy a ticket

We live in the era of personalisation. Through data science it becomes possible to suggest, discover and create products that are tailor-suited to each individual’s preferences.

The first wave of personalisation through data science came in the form of recommender systems. Recommendations, however, are somewhat static. A recommender simply suggests products, and the user can choose to buy them or not. There are further optimisations we can do through data science in order to offer a more personalised service.

One such approach is dynamic pricing.

Using dynamic pricing you can find out the best price for a product or service by using historical data of past purchases. Successful implementation of dynamic pricing requires a combination of different data science techniques.

Who is this course for?

This course is ideal for:

• Software developers
• Junior data scientists
• Data analysts

A bit of knowledge of Python or machine learning will help you but it is not require

What am I going to get from the full course?

• What is dynamic pricing
• Introduction to machine learning for dynamic pricing
• How machine learning can be used for dynamic pricing
• Introduction to meta-heuristic optimisation
• Introduction to the PAO (predict-and-optimise) framework for dynamic pricing
• You get the chance to work on a dynamic pricing project, while receiving close 1:1 mentoring

Who teaches this course?

This course is taught by Dr Stylianos Kampakis who has more than 10 years of experience in the areas of data science and AI, and has implemented many real machine learning systems. Dr Kampakis is also the author of the 5-star rated The Decision Makers Handbook to Data Science.

He has taught multiple courses and bootcamps, ranging from courses for technical professionals, to executive level workshops. He is also working with many universities like London Business School, UCL, Cambridge University's Judge Business School, the University of Law, Nile University and the Cyprus International Institute of Management.

Course structure and requirements

How is the course structured?

This is an online-assisted learning course. This means that the students study at their own pace, while, at the same time, being offered unlimited close assistance by a mentor.

What are the requirements?

There are no course requirements in terms of prior knowledge, but some familiarity with coding is preferred.

How much time should I invest?

You can spend as few as 4 hours and as many as 20-30 hours on this. It really depends up to you and your personal/professional goals.

What is the full structure?

The structure is as follows

• Kick-off call with the mentor and all the students in order to get introduced to the bootcamp, and the learning platform. This event is the kick-off call, and is provided by free to anyone.
• Straight after the kick-off call, the students will define a project challenge to work on for the duration of the bootcamp. This can be something related to their work, or some personal project. If there is no clear idea of what the challenge will be about, then the mentor can provide them with one.
• For the next 6 weeks during the students get the chance to absorb the content. There is unlimited 24/7 mentoring provided, during which the students get the interact with the mentor over Zoom and Slack.
• Once the 6 weeks are over, the students reconvene with the mentor for a final call, where everyone can review what they did, and decide how to proceed with their project, after the bootcamp is over.

If you have any questions, make sure to contact stelios@thedatascientist.com