CU75111

Data Science Basics

Credits
5
Type
Data Science
Scheduled
Y1 – B4
Assessment
(Assignment)
Miller
2: Knows How
ZelCom
(I: 0 C: 0)
Course Owner
Cijsouw, Jolène
Designers
Description
In this course, you will be introduced to the workflow of a Data Scientist using the CRISP-DM framework. Building on your data preparation skills, you will perform exploratory data analysis (EDA)to find patterns and use Python to build and evaluate baseline and basic regression and classification models.
Learning Outcome
You define and apply fundamental data science principles and methodologies to understand, transform, and visualise data, to build and evaluate simple predictive models using approopriate techniques.
Indicators
  • Application of CRISP-DM phases to structure a data science project
  • Execution of exploratoty data analysis to identify patterns in a dataset
  • Transformation of data to prepare it for modelling
  • Construction of regression and classification models
  • Evaluation of model performance
Activities
  • You will map a business case on the CRISP-DM framework
  • You will perform exploratory data analysus to disover patterns in data
  • You will optimize your dataset to prepare it for modeling
  • You will build and optimize simple models to solve numeric and categorical problems
  • You will interpret model metrics to determine its quality
Notes
Activities
Analysis
Design
Realisation
Evaluation
Process
Competences
Analysis Advise Design Realise Manage and Control
User Interaction S
Organisational Processes S 1
Infrastructure
Software 2 2 2 2
Hardware Interfacing
Professional Skills
Future-Oriented Organisation
Organisation Context
Ethics✓
Process Management
Investigative Ability
Methodical Problem Approach✓
Research✓
Solution✓
Personal Leadership
Entrepreneurial Mindset
Personal Development
Personal Profiling✓
Targeted Interaction
Partners
Communication
Collaboration