Data Analysis and Statistics (Distance Learning) - CMI7508

Assessment 1: Other

Students will undertake a series of formative exercises through the University’s Virtual Learning Environment. Feedback, advice and guidance will be provided for these exercises.

Other

Assessment 2: Portfolio

Individually produce a portfolio on the data analysis tasks proposed in the first task, including a description of the issue, a full account of the work carried out, results and accomplishments.

Portfolio

Assessment 1: Portfolio

Individually produce a portfolio to consist of a selected dataset and proposed analysis methods to be applied, together with accounting of motivation and justification.

<p>Statistical methodology and statistical practice are very central for data analysis. Statistical methods and statistical implementation are also complementary to machine learning and data mining, covering supervised and unsupervised methods. In this module you will be exposed to current core research topics in data mining, machine learning, and interdisciplinary research in which data analysis plays an essential role. You will explore real world applications in business (e.g. customer analytics, credit scoring, financial forecasting), in health and medical research (e.g. automatic diagnosing, genetic data mining and bioinformatics), and in structured and unstructured data analysis.</p>

School of Computing and Engineering

<p>https://library.hud.ac.uk/readinglists/CMI7508</p>

Computing and Information Systems

CMI7508 - Data Analysis and Statistics (Distance Learning)

Data Analysis and Statistics (Distance Learning)

Describe and analyse statistical strategies and techniques.

Design and conduct analyses on structured and unstructured data coming from various sources as datasets using industry standard tools and languages.

Conduct research to devise new analytical techniques or employ conventional/modern statistical and analytical techniques in interdisciplinary research pertaining to fields such as medicine, pharmaceutics, bioinformatics, social sciences and engineering.

Examine applications of analysing data encountered in business sectors as customer relationship management (customer retention), banking and credit risk, retail, intelligent web, and in specific data products.