Data Science Training Programme
Course Description
The Data Science Training Programme is designed to equip learners with the knowledge and practical skills required to collect, manage, analyse, and interpret large and complex datasets using modern data science tools and technologies. The programme provides a comprehensive understanding of data analysis, statistical modelling, machine learning, data visualisation, and big data technologies to support evidence-based decision-making across business, scientific, and industrial environments.
Learners will gain hands-on experience with industry-standard programming languages and tools, including Python, R, SQL, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, and cloud-based big data platforms. The course emphasises practical applications through real-world datasets and projects, preparing learners to solve complex analytical problems and develop data-driven solutions.
Upon successful completion, learners will possess the analytical, technical, and problem-solving skills required to pursue careers in data science, business analytics, artificial intelligence, and data engineering.
Course Objective
This programme aims to develop professionals capable of transforming raw data into meaningful insights that support strategic decision-making. As organisations increasingly rely on data to drive innovation and business growth, the demand for skilled data scientists continues to expand across every industry.
The objectives of this course are to enable learners to:
- Develop a strong foundation in data science principles, statistics, and analytical thinking.
- Acquire proficiency in programming languages such as Python, R, and SQL for data analysis.
- Collect, clean, transform, and manage structured and unstructured datasets.
- Apply statistical techniques to analyse data and generate actionable insights.
- Develop predictive models using machine learning algorithms.
- Explore deep learning concepts for advanced data modelling and artificial intelligence applications.
- Create meaningful data visualisations and interactive dashboards for effective communication.
- Work with big data technologies and cloud computing platforms for scalable data processing.
- Solve real-world business, scientific, and industrial problems using data-driven approaches.
- Build a professional portfolio through practical projects and case studies.
Learning Outcomes
Upon successful completion of this course, learners will be able to:
- Collect, clean, and preprocess datasets to ensure data quality and integrity.
- Apply data manipulation techniques using Python libraries such as Pandas and NumPy.
- Explore and analyse data to identify trends, patterns, and relationships.
- Create professional data visualisations using Matplotlib, Seaborn, Plotly, and similar tools.
- Perform statistical analyses, including hypothesis testing, regression analysis, and correlation analysis.
- Build, evaluate, and optimise machine learning models using Scikit-learn and other industry-standard frameworks.
- Apply deep learning concepts using neural networks for advanced predictive modelling.
- Process and analyse large-scale datasets using big data technologies such as Hadoop, Apache Spark, and Apache Flink.
- Utilise cloud computing platforms, including AWS, Microsoft Azure, and Google Cloud Platform (GCP), for scalable data storage and processing.
- Interpret analytical results and communicate findings effectively through reports, dashboards, and presentations.
- Apply data science methodologies to solve business, financial, healthcare, scientific, and industrial challenges while following ethical and responsible data practices.
Areas of Expertise
Throughout the programme, learners will develop expertise in the following areas:
Data Manipulation and Cleaning
- Handling missing values, duplicates, and outliers
- Data transformation and preprocessing
- Data wrangling using Python (Pandas and NumPy) and R
- Data quality assessment and validation
Data Exploration and Visualisation
- Exploratory Data Analysis (EDA)
- Data distribution and trend analysis
- Interactive dashboards and visual storytelling
- Visualisation using Matplotlib, Seaborn, Plotly, and Tableau
Statistical Analysis
- Descriptive and inferential statistics
- Hypothesis testing
- Correlation and regression analysis
- Statistical modelling using Python, R, and SPSS
Machine Learning
- Supervised and unsupervised learning
- Linear and logistic regression
- Decision trees and random forests
- Support Vector Machines (SVM)
- Model evaluation and performance optimisation
- Machine learning implementation using Scikit-learn
Deep Learning
- Fundamentals of neural networks
- Deep learning architectures
- Image and text processing
- Time-series forecasting and predictive analytics
- AI applications using TensorFlow and Keras
Big Data Technologies
- Big data architecture and ecosystem
- Hadoop, Apache Spark, and Apache Flink
- Distributed data processing
- Cloud-based analytics using AWS, Microsoft Azure, and Google Cloud Platform (GCP)
- Scalable data storage and processing techniques
