For professionals with a solid data science foundation, this advanced course dives into ETL pipelines, linear algebra for machine learning, data analysis with R, advanced visualisation, and automated knowledge acquisition — bridging intermediate skills to expert-level data science practice.
Course Overview
The NCC Advance Short Course in Data Science (Level 3 of 4 in the pathway) is designed for individuals with a foundational and intermediate understanding of data science who want to delve deeper into advanced methodologies. The programme covers ETL processes, linear algebra for machine learning, in-depth data analysis using R, advanced visualisation with ggplot2, automated knowledge acquisition, and machine learning pipelines using clustering and matrix factorisation techniques.
Entry Requirements
- Solid grasp of foundational and intermediate data science principles
- Proficiency in Python; basic familiarity with R is beneficial
- Strong analytical abilities and data handling experience
- Prior completion of an intermediate data science course (recommended)
Course Modules
1
Extract, Transform, and Load (ETL)
ETL fundamentals, tools, testing methodologies, and real-world data pipeline scenarios
2
Linear Algebra I
Matrix operations — transpose, addition, subtraction, scalar multiplication, and ranks
3
Linear Algebra II
Echelon form, vector products, matrix multiplication, and inverse calculations
4
Data Analysis with R
R programming fundamentals, conditional statements, loops, and data manipulation
5
R Data Structures
Vectors, lists, matrices, arrays, data frames, and factors in R
6
R Visualisation & Graphics
Base R graphics, scatterplots, bar charts, box plots, histograms, and ggplot2
7
Automated Knowledge Acquisition
Knowledge engineering, expert systems, AI rules, semantic networks, and reasoning
8
Machine Learning I
Supervised and unsupervised learning, clustering (K-means, DBSCAN), and applications
9
Machine Learning II
Matrix factorisation, dimensionality reduction, and industry-specific ML applications
10
Data Visualisation Principles
Design principles, exploratory analysis, and effective communication of data insights
11
Dashboard Design
Interactive dashboard creation and data storytelling techniques for business audiences
12
Advanced Data Communication
Presenting analytical findings to technical and non-technical stakeholders
This is Level 3 of TNEDU's four-level Data Science short course pathway. Completing this course equips students with the R programming and advanced ML skills needed to tackle the Expert-level course — and to work at a senior data analyst or ML engineer level.
Career Outcomes
Senior Data Analyst
Machine Learning Engineer
Data Scientist
Data Visualisation Specialist
Business Intelligence Developer
Progression Routes
NCC Expert Short Course in Data Science → Big data architecture, predictive analytics, and production model deployment
