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## NSC1002 - Mathematics and Computing: Integrative Tools for Natural Sciences (2016)

MODULE TITLE | Mathematics and Computing: Integrative Tools for Natural Sciences | CREDIT VALUE | 30 |
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MODULE CODE | NSC1002 | MODULE CONVENER | Unknown |

DURATION: TERM | 1 | 2 | 3 |
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DURATION: WEEKS | 11 | 11 |

Number of Students Taking Module (anticipated) | 40 |
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Mathematical and computational methods play an increasingly important role in understanding the complex observational data that are used to understand problems across the natural sciences, for example developmental biology, biochemistry, physics and medicine. These systems are often best explored using the power of mathematical and computational tools and the purpose of this module is to introduce some of the fundamental techniques and tools that are used to study these problems.

This is a compulsory module for students on the BSc/MSci Natural Sciences and should be taken in parallel with NSC1001 Frontiers in Science 1 and NSC1003 Foundations in Natural Science.

The intentions of the module are to teach fundamental mathematical and computational techniques and to demonstrate their relevance to the natural sciences through specific examples from biology, biochemistry, physics and medicine throughout the module

On successful completion of this module ** you should be able to**:

**Module Specific Skills and Knowledge**

2. Write, compile, test, and debug a computer program

**Discipline Specific Skills and Knowledge**

7. Systematically break down a problem into its components

**Personal and Key Transferable / Employment Skills and Knowledge**

10. Work co-operatively and develop time-management strategies to meet deadlines for work

In the first part of the module, lectures will be used to introduce the fundamental aspects of mathematics and computing (one for each discipline per week). In the second part of the module you will have sufficient knowledge to cover integrated material. Throughout the module, workshops will introduce the basic tools and environment for programming and tutorials will be used to support the mathematical concepts that are being taught.

Mathematics:

Part I. Introductory material, summary overview and background

Part II. Linear algebra and complex numbers

Part III. Sequences, Series, Limits and Convergence

Part IV. Calculus and Differential Equations

Part V. Multivariable calculus and vector calculus

Part VI. Probability and Statistics

Computing:

Part I. Programming overview and introduction to Python as a language (statements, comments and simple arithmetic operations).

Part II. Variables, scope and data types

Part III. Control flows, conditionals, loops and iterations

Part IV. Functions, debugging and testing

Part V. Input and Output

Part VI. Scientific computing in Python

Part VII. Visualisation and algorithms in Matlab

Scheduled Learning & Teaching Activities | 141.00 | Guided Independent Study | 159.00 | Placement / Study Abroad | 0.00 |
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Category | Hours of study time | Description |

Scheduled Learning and Teaching | 77 | Lectures (four hours a week in Term 1, three hours a week in Term 2) |

Scheduled Learning and Teaching | 44 | Programming workshops (two hours a week in Term 1 and in Term 2) |

Scheduled Learning and Teaching | 12 | Mathematics tutorials (one hour every other week) |

Scheduled Learning and Teaching | 8 | Surgeries (one for each assessment) |

Guided independent study | 159 | Additional research, reading and preparation for module assessments |

Form of Assessment | Size of Assessment (e.g. duration/length) | ILOs Assessed | Feedback Method |
---|---|---|---|

8 x problem sets – mix of mathematics and computer science questions | 1 hour per set | 1,2,6-9,11 | Individual Form |

Coursework | 60 | Written Exams | 40 | Practical Exams | 0 |
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Form of Assessment | % of Credit | Size of Assessment (e.g. duration/length) | ILOs Assessed | Feedback Method |
---|---|---|---|---|

Problem set 1 | 5 | 5 hours | 1, 2, 6-11 | e/written assessment, then discussed in tutorials/workshops |

Problem set 2 | 5 | 5 hours | 1, 2, 6-11 | e/written assessment, then discussed in tutorials/workshops |

Problem set 3 | 5 | 5 hours | 1, 2, 6-11 | e/written assessment, then discussed in tutorials/workshops |

Problem set 4 | 5 | 5 hours | 1, 2, 6-11 | e/written assessment, then discussed in tutorials/workshops |

Problem set 5 | 5 | 5 hours | 1, 2, 6-11 | e/written assessment, then discussed in tutorials/workshops |

Problem set 6 | 5 | 5 hours | 1, 2, 6-11 | e/written assessment, then discussed in tutorials/workshops |

Programming exercise 1 | 10 | 5 hours | 2-5, 7-10, 12 | Individual form |

Programming exercise 2 | 10 | 5 hours | 2-5, 7-10, 12 | Individual form |

Mid-term test | 10 | 1 hour | 1, 3, 5, 7-9, 11 | Marked, then discussed in tutorials |

Final examination | 40 | 2 hours | 1, 3, 5, 7-9, 11 | Mark via MyExeter, collective feedback via ELE and solutions |

Original Form of Assessment | Form of Re-assessment | ILOs Re-assessed | Time Scale for Re-assessment |
---|---|---|---|

Problem sets, programming exercises | Continuous assessment (problem sheet with a programming exercise) | All | August/September assessment period |

Final examination and mid-term test | Written examination | 1, 3, 4, 7-9, 11 | August/September assessment period |

If deferred, both forms of re-assessment must take place with the continuous assessment counting for 60% of the overall mark, and the written examination counting for 40% of the marks. If referred in the final exam only, only the written exam must be taken. If referred in all, both re-assessment components must be taken and a mark of 40% achieved in both.

The mark given for a re-assessment taken as a result of referral will be capped at 40%. The mark given for a re-assessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.

information that you are expected to consult. Further guidance will be provided by the Module Convener

**Basic reading:**

Engineering Mathematics. K. A. Stroud. Palgrave Macmillan

Advanced Engineering Mathematics. K. A. Stroud. Palgrave Macmillan

Core Maths for the Biosciences. M. B. Reed. Oxford University Press

Elementary Statistics. Mario F Triola. Pearson.

A Primer on Scientific Programming with Python (Texts in Computational Science and Engineering). Hans Petter Langtangen. Springer.

**ELE: **http://vle.exeter.ac.uk/course/view.php?id=3803

**Web based and Electronic Resources:**

**Other Resources:**

Reading list for this module:

There are currently no reading list entries found for this module.

CREDIT VALUE | 30 | ECTS VALUE | 15 |
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PRE-REQUISITE MODULES | None |
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CO-REQUISITE MODULES | None |

NQF LEVEL (FHEQ) | 4 | AVAILABLE AS DISTANCE LEARNING | No |
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ORIGIN DATE | Wednesday 11 November 2015 | LAST REVISION DATE | Wednesday 11 November 2015 |

KEY WORDS SEARCH | Mathematics, computing, natural sciences, differential equations, Matlab, Python |
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