DATA SCIENCE ADVANCED
The Learner will:
- Be able to extract, transform and load (ETL) data.
- Be able to explain the ETL testing process and types of ETL testing.
- Understand the basic properties of matrices and vectors, multiplication, linear transpose, conjugate, determinant.
- Understand inner and outer products, matrix inverse, matrix multiplication rules and algorithms.
- Undertake data analysis with R.
- Become familiar structures.
- Discover R’s graphic packages.
- Be able to create different visualizations using R.
- Understand supervised and unsupervised learning and automated knowledge acquisition.
- Be able able to analyse scenarios for different industries using machine learning.
- Understand design principle for data visualisation.
- Be able to explain EDA and perform a data story presentation and dashboard design for communication.
- Introduction to Extract, Transform and Load Process
- Linear Algebra I
- Linear Algebra II
- Data Analysis with R
- Understanding R Data Structures
- R’s Graphic packages & Visualisations
- Automated Knowledge Acquisition
- Supervised and Unsupervised Learning
- Analytics Scenarios for Different Industries
- Machine Learning Models
- Design Principles for Data Visualisation
- Exploratory, Descriptive and Diagnostic Analysis
- Data Story Presentation & Dashboard Design for Communication