Analytic Tools for Data Science

• Credit value: 15 credits at Level 7
• Convenor: Dr Taolue Chen
• Assessment: a data analysis project (20%) and two-hour examination (80%)

In this module we cover the fundamental concepts and techniques of data analytics with Python, demonstrating how to apply these in order to process and visualise datasets. We will look at tools such as database systems (SQL), data analytics techniques using machine learning models (clustering, and neural networks) and Python libraries.

During the lab sessions, you will work on case studies to apply data analytics to real-world problems.

Indicative syllabus

• Introduction to machine learning and data analytics
• Python libraries: Numpy and Pandas
• Python libraries: Scipy and Matplotlib
• Data cleaning methods and processing methods
• Introduction to machine learning with Python
• Database management systems and SQL
• Classification and regression with Python
• Introduction to neural networks and deep learning
• Introduction to cloud computing
• Introduction to big data analytics

By the end of this module, you will be able to:

• demonstrate satisfactory knowledge of data analytics using the Python programming language
• understand techniques for data cleaning and processing
• retrieve data from database systems for analysis and processing
• understand the latest technologies used for data analytics such as big data systems and cloud computing
• use Python to apply machine learning techniques (clustering, regression, and neural networks) to real-world problems.