Data science is developing at a rapid pace. This development has a major impact on the way in which organizations, both in the public and private sector, structure and execute processes. This impact can also be felt in relations with business partners, in decision-making, and in terms of the competencies and skills that employees at every level of the organization must possess. The Foundation semester is comprised of eight modules:
1. Programming
2. Databases
3. Business Intelligence
4. Data Visualization I
5. Business Analytics I
6. Text Analytics I
7. Big Data Analytics I
8. Managing Data Science Projects
The CPP Data Science (Python) is a unique and challenging course, offering a mix of advanced data-science topics that will help you to prepare yourself for this development and be part of it.
It is an advanced training for anyone looking to seriously improve their technical data science skills and capabilities. A group of highly specialised experts will go in-depth with you, delving into the world of data. Combining theory and practice in case studies and actual challenges of your own.
The program is divided into two semesters: Foundation (semester 1) and Future (semester 2).
Each semester lasts four to five months and is comprised of short and long modules or blocks.
You start the CPP Data Science (Python) in the Foundation semester, as this will give you the knowledge required to participate in the more advanced Future semester. During the Future semester we will delve even deeper into the subject matter.
The learning aims of this SLP are:
• After completing the examination in this course the student will be able to understand the key elements of machine learning, such as dataset, classification algorithm, regression and clustering, label, target variable, prediction, recall and confusion matrix
• Being able to program in Python to perform analysis
• Understand the main concepts of Data Analytics and its role in organizational decision making and innovation.
• Understand the difference between: predictive models, and descriptive models.
• Describe the steps and stakeholders in the Knowledge Discovery and Data Mining process.
• Derive a researchable data mining question from business needs and therefore evaluate costs and benefits of a machine learning model.
• Apply the following analytical techniques multiple regression analysis, clustering, classification trees, Naybe Bayes, Bayesian Networks, Support vector machines, principal component analysis and linear discriminant analysis
• Using a tool like Jupyter.
• Be able to interpret results from the Knowledge Discovery and Data Mining process and to present these in laymen terms.
• Apply Association Rule Mining or Text Analytics models to the data.
The Foundation semester is comprised of eight modules:
Programming
Databases
Business Intelligence
Data Visualization I
Business Analytics I
Text Analytics I
Big Data Analytics I
Managing Data Science Projects
The program has been designed to help you solve generic business problems and master a specific skill or competence, ensuring a constant relation between theory and practice. We also apply insights to your cases and business challenges and show you how you can use data-science techniques to improve innovation at your company.
The program is offered online and will consist of virtual meetings and independent learning in order to master all the topics that are discussed.
Technical understanding of programming, database structures, etc.
Course level: Quantitative Bachelor Degree, preferably MSc
After completing the course you will receive a certificate.