KL6000 - Data Science

You will learn through a series of lectorials which combine formal lectures and hands on experience using computer software. Classes will be scheduled in our modern computer laboratories enabling you to apply the techniques presented in the lecture part of the session and, in this way, deepen your understanding of the material and develop your practical skills. This, in turn, develops your confidence to explore the subject area further as an independent learner outside of the classroom.

Formative feedback is available weekly in the classes as you get to grips with new techniques and perform analyses. In addition, we operate an open door policy where you can meet with your module tutor to seek further advice or help if required. Your ability to select appropriate techniques and use the appropriate approach to analyse different types of data is assessed in lab based examinations at the end of both semesters.

General feedback on assessments will be given in class followed by individual feedback . An opportunity to discuss work further will be available on an individual basis when work is returned and also through the open door policy.

Direct contact with the teaching team during the lectorials will involve participation in both general class discussions as well as one to one discussions during the hands-on part of the lectorial. This gives you a chance to get immediate feedback pertinent to your particular needs in this session. Further feedback and discussion with the teaching team are also available at any time through our open door policy. In addition, all teaching materials, selected R scripts, and supplementary material (such as interesting articles) are available through the e-learning portal.

All modules at Northumbria include a range of reading materials that students are expected to engage with. The reading list for this module can be found at: http://readinglists.northumbria.ac.uk
(Reading List service online guide for academic staff this containing contact details for the Reading List team – http://library.northumbria.ac.uk/readinglists)

Knowledge & Understanding:

1. Consider which method of analysis is most appropriate for a given set of data and the information required
2. Appraise a data analysis for its accuracy, robustness and suitability


Intellectual / Professional skills & abilities:

3. Evaluate the associations between elements of a set of data and summarise these relationships

4. Consider different classification techniques and select the most appropriate for analysis

Personal Values Attributes (Global / Cultural awareness, Ethics, Curiosity) (PVA):

5. Critically appraise the use of data analysis techniques in various scenarios and determine which approach is relevant for selected data and analyse and summarise results

SUMMATIVE

1. Lab based examination (50%) – 1,3,5

2. Lab based examination (50%) – 2,4,5

Summative assessment will be tak place as formal examination at the end of each semester.

FORMATIVE
Formative assessment will be available on a weekly basis in the lectorials through normal lecturer-student interactions, allowing them to extend, consolidate and evaluate their knowledge.

Formative feedback will be provided on student work and errors in understanding will be addressed reactively using individual discussion. Solutions for laboratory tasks will be provided after the students have attempted the questions, allowing students to receive feedback on the correctness of their solutions and to seek help if matters are still not clear.

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‘Data Science’ concerns extracting information from data using statistical and computational techniques. You will learn about the four main areas of data science and learn how the approach to analysing data depends on both the type of data and the aim of the analysis.
You will learn to answer questions such as ‘Is this new customer likely to buy?’ or ‘Which type of customer is this new customer most like?’. The module is ‘hands-on’ and is taught through a combination of lectures and tutorials. Formative feedback is available weekly in the classes as you get to grips with new cryptologic techniques and solve problems. Assessment is via lab-based exams (where you will analyse brand new data sets) each worth 50% of the module mark.

The module has been designed to give you an education in how data science techniques have developed recently as well as an ability to assess which technique is most appropriate for the data available and question posed.