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BSc (Hons) in Data Science (Top Up) - London Met University UK

This Data Science BSc course offers a comprehensive introduction to the most important areas of the discipline, including data programming, statistical modelling, business intelligence, machine learning and data visualisation.

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Overview

Developed with input from industry experts, this course covers all the necessary skills and competencies required to delve deeper into this fascinating field. By the end of the BSc degree, you’ll be ready to apply for rewarding roles in the data science and big data industries, as well as the many sectors and organisations that increasingly require data scientists.

Designed by academics from both Mathematics and Applied Computing backgrounds, this course is made up of fine-tuned modules which are prepared with your future in mind. The course will foster your learning development using a range of tools and big data platforms, allowing you to continue to specialise in data engineering, analytics, big data visualisation, statistical modelling and machine learning.

Course Outline

Semester 1

This module introduces methods of analysing data using appropriate statistical software. Descriptive statistics and statistical techniques that are useful to present, analyse and make inferences about data are also introduced. A selection of suitable software (e.g. Excel, SPSS, R) will enable students to analyse data in order to make informed decisions.

Semester 2

This module introduces methods of analysing data using appropriate statistical software. Descriptive statistics and statistical techniques that are useful to present, analyse and make inferences about data are also introduced. A selection of suitable software (e.g. Excel, SPSS, R) will enable students to analyse data in order to make informed decisions.

Course Outline

  • apply maths, statistics and science practice
  • recognise and exploit business opportunities using data science innovation
  • find a solution to domain-specific problems using data science capability
  • utilise a range of coding practices
  • build scalable data products for strategic or operational business and contribute through the product life cycle
  • use tools such as Spark, Kafka, Hadoop, Oracle, SQL Server, Linux, Apache Airflow, RStudio, Python – Jupyter, Tableau, and D3 technology
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