Data Science
- You are able to understand the benefits of data analysis and learn about the wide range of applications and relevant types of data sources.
- You are able to carry out descriptive and exploratory data analysis and visualise the results using standard graphical representations.
- You are familiar with the various classifications of algorithmic approaches to machine learning, such as the distinction between supervised and unsupervised learning.
- You are able to identify which type of algorithms can be used to solve a specific use case.
- You also have a firm grasp of the fundamental methods, techniques and algorithms used in statistics and machine learning, such as linear and logistic regression, decision trees, distance-based methods such as the k-Nearest Neighbor algorithm, support vector machines and neural networks.
- You are familiar with the various stages involved in carrying out a data science project and have explored process models for implementing such projects.
- You are able to design and develop analytical solutions to describe, predict and improve business outcomes. They have knowledge of selected advanced concepts and are able to apply a number of them.
- You are able to carry out data analysis and modelling independently, for example using the Python programming language, and are capable of prototyping data pre-processing steps, feature engineering, and training and testing algorithms.
- You are able to identify the specific challenges involved in operating data-driven software in a production environment and are familiar with the concepts and measures required to successfully transfer prototype approaches into production.
- You have gained an overview of the risks associated with the use of machine learning applications, such as training based on biased data, and have explored various approaches to understanding and ensuring the transparency of algorithmic decisions.