Why join the Analytics Translators Programme?
What will you learn?
Successful implementation of data science products and insights takes more than just the hard skills to do data science. In this full programme you will learn both the foundations as well as the practical skills to fulfill the vital role of the Analytics Translator.
The analytics translator has some knowledge of machine learning and data science (but not to the technical depth at which the data scientists do their work). They are uniquely able to approach the project from the viewpoint of the business.
In this programme you will learn enough statistics and machine learning to spot opportunities for such techniques in solving a variety of business problems. You will also learn how to translate the analytics project results back to your business audience and how to facilitate a successful implementation.
Understanding data science requires an understanding of the underlying statistical concepts. You will be provided with the necessary statistical toolkit. Among others we will cover the probability theory and statistical terms, as well as inferential statistics how to evaluate sample data and how to conduct hypothesis testing.
Machine Learning is the basis of much of data science. We cover many of the popular algorithms like linear and logistic regression, decision trees, ensembles like random forests, k-nearest neighbors and neural networks. Special focus will be on how to use these in business settings. You will also apply the material to your own business by writing a data science project plan.
We will go into the topic of business questions that can launch fruitful data science projects and we will explore the dynamics of structured communications with the pyramid principle. We will also discuss the conditions for a multi-disciplinary data science team required to guide your business through a digital transformation. Subsequently, you will learn design principles and appbicablity of various visualization techniques. After that we will discuss principles and considerations of EU privacy law and we will conclude the programme with a case presentation and discussion of your very own data science project proposal.
You do not need programming skills to start with this programme.
During this 10-day programme you will acquire:
- A fundamental understanding of Machine Learning methods
- Identifying business opportunities for Machine Learning solutions
- Essential statistical concepts
- Identifying business opportunities for data science solutions
- Implementation of data science projects
- Data visualisation, dashboard design and storytelling with data
- Data science ethics and regulations, including GDPR
- Understand data science roles and what kind of teams are needed
- How to write a data science project plan
This course is for business professionals who want to become the crucial link between the business and the data science and analytics teams. Both middle management, as well as people in roles like business analysts or any other role that in some way touches upon the analysis of data will profit greatly from this course. If you are not sure if your background fits the course, please contact us via Executive-Education@uva.nl
Banks, Insurance Firms, Financial Institutions, Tax, Institutional Investors, Accountancy, Auditing, Consultancy, Services, Government, Energy, Telecom, IT, Internet, Marketing and Communication, Event Management, Logistics, Retail, Trade, Consumer Products, Freight, Healthcare and Pharmaceutical.
Location: Amsterdam Business School
Certification: Participants will receive a Certificate of Attendance from the University of Amsterdam
Fast Track Analytics Translator is designed by The Analytics Academy
, a collaboration of the Amsterdam Business School with ORTEC and Amsterdam Data Science
. The lecturers from The Analytics Academy specialise in offering data science and business analytics education to a wide range of professionals in open and in-company programmes. They help organisations to grow and sustain knowledge at every stage of their data-driven development.