The Analytics Academy (TAA) is a unique learning and development partnership of UvA, Amsterdam Business School, Amsterdam Data Science and ORTEC. TAA merges ORTEC’s global business consulting and optimization software development practices with one of the Netherlands’ top-ranking universities (57th QS World University ranking 2018-2019). Our experience and trainers have served leading globally operating firms in developing new skills and insights that are required in their data and digital development and transformations.
Through offering a broad range of Data Science training, The Analytics Academy helps companies and analytics teams to grow and sustain knowledge at each stage of the data driven transformation. Our trainings go through all layers of the organization and range from creating basic understanding at board level in order to develop a data strategy to acquiring hands-on skills enabling your staff to deliver value in data-oriented projects. Unique benefit of our partnership for our clients, is the ability to combine tailored classroom training with on-the-job coaching leading to better adoption and implementation of the capabilities within the organization.
The Analytics Academy prides itself on its flexibility in program design, striving towards a perfect fit between your educational needs, planning and availability concerns, and the financial aspects of capability building. We prefer to co-design a curriculum and program, because nobody knows your organization better than you. Want to find out what would fit for you organization? Contact us via the link below.
The Analytics Academy can provide the Analytics Capability Scan to help determine the educational need of participants. The ACS is an assessment based on the EDISON-framework, which allows the participant to be assessed against EU standards for the different analytics roles. The ACS consists of a knowledge test, a numeric assessment, a coding assignment, an expert interview and the EDISON scoring workshop. After the assessment, the participant receives an extensive report. An example report can be downloaded below.
The lectures provided by The Analytics Academy aim to provide the participant with both informative, practical and inspiring information, and a platform for discussion and interaction. Because of this, we rarely suggest a group size larger than 20 people per lecture. Our lectures create an intimate session with experienced topic experts, and allow participants to engage with the expert about their day-to-day activities.
To translate the theory from the classroom modules into the (daily) business of our clients, we offer hands-on coaching by experienced professionals. During these days, the participants work on their own ‘real life’ cases (emerging from the company’s business) By using this approach, the newly acquired skills from classroom trainings are translated to actual cases which deliver business value to the clients. The participants own the cases, apply the freshly gained knowledge and are supported by the on-the-job trainers.
Module Name |
Short Description |
| Data Visualization | This module will teach the participants to effectively communicate the insights from their data through visualizations. |
| Data Preprocessing | In this module, you will learn to recognize and fix errors in your data, improve the overall quality, and apply best practices. |
| Deep Learning and Computer Vision | This course provides a deep dive into the theory behind deep learning techniques. Conventional and specific neural networks will be covered for different use-cases, with a strong emphasis on computer vision. |
| Implementation and Change | Learn all about how data driven decision making can be implemented successfully in an organization. |
| Machine Learning with R or Python | This course will teach you the basics regarding Machine Learning (ML). ML is widely applied in the industry for application like face-recognition, fraud detection and predictive maintenance. Here, we will focus on the various algorithms and train and assess them ourselves. |
| Optimization and Modelling | Optimization is widely used within business process to improve logistics, operation and planning. This course will provide the skills to recognize and solve (optimization) problems. |
| Quantitative Marketing and Marketing Analytics | An in-depth module on various use-cases related to data-driven and quantitative marketing. |
| Unstructured Data and Text Mining | A module on text mining techniques for unstructured big-data. |
| Machine Learning for Business Translators | An introduction to machine learning and its applications for business translators. |
| Storytelling with Data | Understand the applicability of various visualization techniques for (big-)data to support valuable story telling. |
| Statistics, Predictive and Prescriptive Analytics for Business Translators | This module will give an introduction on statistics, machine learning and optimization. Both skills and use-cases are covered. |
| Big Data Processing | An advanced course in the field of (big-) data engineering, its techniques, architectures and applications. |
| Programming (in R/Python) | Learning the essentials of various tools and languages within the data science domain. |
| Best Practices in Data Science Programming | Insights into topics that should make your code more readable, understandable, reusable and maintainable |
| Cloud Computing on Azure | This module explains the theories behind cloud computing and allows you to start applying technologies within the cloud domain. |
| Data Processing Tools and Techniques, an Introduction | This module introduces the landscape of data processing tools and techniques. |
| Robust Data Architectures in Production | This module aims to help you understand the basics in designing such an architecture and give you the insights to identify the necessary tools and techniques needed in bringing your data science project to production. |
| Data Governance, Security and Privacy | This module describes different laws and regulations, and how to conform to them. It also lets you critically think about and discuss ethical and non-ethical processing of (private) data. |
| Data management: doing (data) things right | Practically every situation in which people or systems work together using a shared data source, things like data quality, accessibility, interoperability, compliance to standards and publication rights arise. This module guides the student through these processes and creates insights in when to use what policies. |
| Data management: strategy and policy | This module introduces the concepts of data strategy and data policy, and lets the student develop insights in how to design and maintain complex data systems. |
| Law and Ethics | This module covers the topic of law and ethics in the field of Big Data and Analytics. |
| Exploratory Data Analysis | Get a first feeling with the data you are using in Exploratory Data Analysis. |
| Introduction to Data Science and the Business Translator Role | In this introductory module we will get you up to speed with the basics of Data Science. |
| Hackathon | Apply your newly learnt skills in practice in the competitive hackathon. At the end a winner is selected by a jury on multiple criteria. |
| Statistics | This course discusses the core concepts of statistics, how to design effective analysis and research designs, and how to evaluate the quality of models. |
| Managing Data Science Projects | A guide through data science project management, selecting the right people for your project and required skills. |
| Creativity | Data Science requires a totally new way of thinking, with this creativity workshop you’ll learn to think outside the box. |
| Determining Business Questions | A guide to determine relevant business questions suitable for data science and machine learning. |
| Strategy, the Value Chain and AI | This course describes how to connect you company strategy to incorporate a more data-driven mindset. |
| Model Performance, Selection and Tuning | This module discusses various methods to assess the performance of machine learning models and ways to improve upon your results. |
| Data management & strategies: ensuring quality and stability | How to organize your data management and structure: best practices and how to apply the strategy. |
| Explainable and Fair AI (XAIFAI) | Make complex algorithms transparent, explainable and fair in order to practice Machine Learning responsibly. |
| Coaching | Dedicated on-the-job coaching sessions to help getting the course knowledge into practice |
| Statistics for Business Translators | A course about the best practices of statistics, tailored to the role of business translators |
