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Satellite Data Science MSc
Satellite Data Science MSc

Satellite Data Science MSc

  • ID:UOL400251
  • Level:Master's Degree
  • Duration:
  • Intake:

Fees (GBP)

Estimated Total/program:
Apply
60
Accept letter
100
Visa
20
Fly
1

Admission Requirements

Entry Requirements

  • 2:2 degree (or equivalent) in a relevant subject. Non-standard qualification or professional experience in a relevant area may be considered. 

English Requirements

  • IELTS 6.5 or equivalent.

Course Information

Satellite observations of the Earth provide us with detailed information about the natural environment and allow us to monitor how humans are altering the planet. Uptake, use, and commercialisation of Earth observation data demands expertise in data analysis. There is a need for graduates who understand the principles of remote sensing whilst being able to write computer programmes, especially in Python, and use high-performance computing infrastructure. This course covers these areas, placing you in a strong position to seek employment in industry.

On this degree you will:

  • Use data from space-based sensors to understand and tackle pressing global environmental challenges related to the biosphere and atmosphere.

  • Develop analytical skills to use and process big datasets from satellites and other sensors.

  • Learn to effectively use and modify open-access big data processing environments such as Python and R.

  • Get started with cutting-edge image processing techniques, artificial intelligence and machine learning, on different platforms (e.g. cloud computing).

  • Gain an appreciation for the extensive application of Earth observations to multiple sectors throughout the world. 

Satellite Data Systems expertise at the University of Leicester

The University of Leicester is the UK hub of Earth observations research and expertise. We host the NERC National Centre for Earth Observation (NCEO), established the Centre for Landscape and Climate Research (CLCR) in 2012, and created the world-class Leicester Institute for Space and Earth Observation (LISEO) in 2016 following 55 years of international space research. 

Space Park Leicester is an ambitious project for creating a new £100 million centre for the analysis and commercialisation of space-enabled data and space mission development. Phase 1 is already approved and will consolidate Earth observation expertise and resources from across the university and promote innovative collaborations with collocated space sector industries.

More Info: click here

Core modules

  • Remote sensing

  • Earth observations of the atmosphere

  • R for Data Science

  • Fundamentals of GIS

  • Satellite Data Analysis in Python

  • Big Data and Predictive Analytics

Option modules

Choose two option modules from:

  • Field Data Capture

  • Geospatial Data Analytics

  • Geospatial Databases and Information Retrieval

Industrial placement (optional)

Studying a 'with industry' masters degree is the perfect way for you to enhance your career prospects and deepen your understanding of your specific area of expertise. By experiencing real-world scenarios and applying your skills and knowledge to business challenges, you will gain a unique insight into how your studies can be utilised in industry. This will not only showcase your abilities to future employers, but will also enhance your studies when you return to University to complete your programme.

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Pre Courses

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Pathway Courses

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Career Opportunity

Career Opportunity

This course will provide you with a broad-based education in data analytics techniques suitable for a career in data science in multiple sectors, not necessarily limited to a career focused on satellite observations: research, academia, industry, SMEs, start-ups, government, policy, NGOs, not-for-profits. You will gain practical experience using tools and software (R, Python) that increase your employability and contribute to addressing the shortage of qualified data scientists in the UK and abroad.

Ability to settle

Overseas Student Health Cover

OSHC - $12276 GBP / per year

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