NCC Expert Short Course in Data Science
The highest level of TNEDU's data science short course series. This expert-level programme covers production machine learning, big data architecture, predictive analytics, data mining, and model deployment — designed for experienced data professionals seeking mastery.
Expert
Level
~200 Hours
Duration
NCC Education
Awarding Body
ML + Big Data
Key Focus
Course Overview
The Expert Short Course in Data Science is designed for professionals with solid data science foundations who are ready to master advanced methodologies. The programme covers the complete data science process, big data infrastructure (Hadoop, cloud computing), advanced machine learning, predictive analytics, web scraping, time series forecasting, data mining, and production model deployment using the CRISP-DM framework.
Entry Requirements
- Solid foundation in data science principles and basic programming skills
- Prior completion of intermediate-level data science courses (recommended)
- Working knowledge of Python and SQL
Course Modules
1
Data Science Process
Fundamentals, ethics, and scientific methodology for data projects
2
Optimisation and Problem Formulation
Linear, integer, and stochastic programming for complex problems
3
Big Data Concepts
Big data characteristics, Hadoop ecosystem, and cloud computing
4
Data Mashups and Infrastructure
Cloud architecture, MapReduce, and analytics at scale
5
Web Scraping Tools
DOM parsing, regular expressions, and automated data harvesting
6
Predictive Analysis and Regression
KNN, decision trees, clustering, and multiple regression
7
Logistic Regression
Binary and multi-class classification theory and applications
8
Time Series and Predictive Modelling
Decomposition, ARIMA, and advanced forecasting techniques
9
Data Mining and Visualisation
Exploration, data cleaning, and quality assessment
10
Data Transformation and Reduction
Normalisation, PCA, and dimensionality reduction
11
Cluster Analysis
Partitioning, hierarchical, and grid-based clustering methods
12
Decision Trees and Model Deployment
CRISP-DM phases, model evaluation, monitoring, and production deployment
This is the final level of TNEDU's Data Science short course pathway. Graduates are equipped to work at senior data scientist, ML engineer, and big data architect level — or to pursue postgraduate academic research in data science.
Career Outcomes
Senior Data Scientist
Machine Learning Engineer
Big Data Analyst
Data Mining Specialist
AI/ML Architect
Analytics Lead
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