KL6000 - Data Science

What will I learn on this module?

Data Science concerns extracting information from data – in other words giving a voice to the data. Different analysts may have different purposes when analysing data – the intention may be to describe the information in the data, explain the relationships between parts of the data or use a subset of the data to predict the outcome of a variable of interest. For example, that variable could be whether a customer with a particular profile may buy an item of interest. Most companies collect data on their customers and are interested in how this data can be used to improve customer experience as well as profits. Depending on the intention, the approach taken by the analyst will differ and this module will cover the main tools for classification, clustering, association mining and outlier detection allowing you to analyse data with confidence.

By the end of the module, you should have developed an awareness of different approaches to analysing various forms of data and should have an ability to appraise which analytical techniques are appropriate. You will be able to perform the analysis and interpret the results correctly.

Outline Syllabus

Classification techniques that may include decision trees, support vector machines, linear discriminant techniques and logistic regression.

Clustering techniques including k-means clustering, apriori association mining, naïve Bayes and dimensionality reduction.

How will I learn on this module?

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.

How will I be supported academically on this module?

Direct contact with the teaching team during the lectorials will involve participation in both general class discussions as well as one to one discussion 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.

What will I be expected to read on this module?

All modules at Northumbria include a range of reading materials that students are expected to engage with. Online reading lists (provided after enrolment) give you access to your reading material for your modules. The Library works in partnership with your module tutors to ensure you have access to the material that you need.

What will I be expected to achieve?

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

How will I be assessed?

SUMMATIVE

1. Coursework (50%) – 1,3,5

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

Summative assessment will be take 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.

Pre-requisite(s)

None

Co-requisite(s)

None

Module abstract

‘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 data science techniques and solve problems. Assessment is via a series of bi-weekly homeworks (worth 50% of the module mark) and a lab-based exam based on analysing data sets (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.

Course info

UCAS Code Z069

Credits 20

Level of Study Undergraduate

Mode of Study 4 years Full Time or 5 years with a placement (sandwich)/study abroad

School Engineering, Physics and Mathematics

Location City Campus, Northumbria University

City Newcastle

Start September 2027

Fee Information

Module Information

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