The module will cover topics such as:

Defining Data, Data Science and Data Science lifecycle

The mathematical techniques of probability and statistics to understand data.

Exploring and analysing relational and non-relational data

Working with python for data exploration with libraries such as Pandas

Programmatic data transformations

Programmatic data visualization

Data techniques for cleansing and transforming the data.

Visualizing proportion, relationships



This module will support the development and assessment of the following Computing Data Analyst Professional Skill from the DTSP Apprenticeship Standard:¿



Knowledge

K54 How to critically analyse, interpret and evaluate complex information from diverse datasets.

K55 Data formats, structures, architectures, and data delivery methods including “unstructured” data.

Skills

S48 Define Data Requirements and perform Data Collection, Data Processing and Data Cleansing.

S49 Apply different types of Data Analysis, as appropriate, to drive improvements for specific business problems.

S50 Find, present, communicate and disseminate data analysis outputs effectively and with high impact through creative storytelling, tailoring the message for the audience. Visualise data to tell compelling and actionable narratives by using the best medium for each audience, such as charts, graphs and dashboards.

S52 Apply a range of techniques for analysing quantitative data such as data mining, time series forecasting, algorithms, statistics, and modelling techniques to identify and predict trends and patterns in data.

S53 Apply exploratory or confirmatory approaches to analysing data. Validate and test stability of the results.

S54 Extract data from a range of sources. For example, databases, web services, open data.

S55 Analyse in detail large data sets, using a range of industry standard tools and data analysis methods.

Two pieces of coursework covering all learning outcomes using the learners work context where possible. A back up scenario will also be provided, and the learners will have the choice between the two. However, encouragement to use the workplace as the scenario for the assignment will be given.





Coursework 1 - A portfolio of Data Analysis Tasks Weighted at 30% Learning outcomes 2, 3 and 4.

Assessing the following Data Analyst KSBs

Knowledge

K54 How to critically analyse, interpret and evaluate complex information from diverse datasets.

K55 Data formats, structures, architectures, and data delivery methods including “unstructured” data.

Skills

S48 Define Data Requirements and perform Data Collection, Data Processing and Data Cleansing.

S49 Apply different types of Data Analysis, as appropriate, to drive improvements for specific business problems.

S52 Apply a range of techniques for analysing quantitative data such as data mining, time series forecasting, algorithms, statistics, and modelling techniques to identify and predict trends and patterns in data.

S54 Extract data from a range of sources. For example, databases, web services, open data.

S55 Analyse in detail large data sets, using a range of industry standard tools and data analysis methods.





Coursework 2 - An individual assignment to develop a data analytical solution which will be assessed by demonstration typically 30 minutes. Weighted at 70% Learning outcomes 1, 4 and 5.

Assessing the following Data Analyst KSBs

Knowledge

K55 Data formats, structures, architectures, and data delivery methods including “unstructured” data.

Skills

S48 Define Data Requirements and perform Data Collection, Data Processing and Data Cleansing.

S49 Apply different types of Data Analysis, as appropriate, to drive improvements for specific business problems.

S50 Find, present, communicate and disseminate data analysis outputs effectively and with high impact through creative storytelling, tailoring the message for the audience. Visualise data to tell compelling and actionable narratives by using the best medium for each audience, such as charts, graphs and dashboards.

S52 Apply a range of techniques for analysing quantitative data such as data mining, time series forecasting, algorithms, statistics, and modelling techniques to identify and predict trends and patterns in data.

S53 Apply exploratory or confirmatory approaches to analysing data. Validate and test stability of the results.

The module will be delivered in a Blended Learning Mode consisting of face to face, online and guided learning sessions.¿

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Teaching sessions will blend theory and practical learning and most importantly where possible contextualised in your workplace as part of your apprenticeship. Learners will be introduced to curriculum concepts and ideas and will then be able to apply theory to practical examples. In addition, students will be provided with a range of resources for independent study such as case studies, academic papers and industry case studies.¿ There will be a mixture of practical and theoretical formative (mock or practice) exercises which will help students build knowledge and confidence in preparation for summative (formal) assessment.¿

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The delivery will be delivered as follows:¿

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Module Launch week: 12 hours.

There will be a module launch session consisting of up to 12 hours face to face contact time devoted to developing your understanding of the core purpose and assessment of the module. Learners will be presented with details of how the learning will be structure and how to access to the learning materials for the remainder of the module.



Mid module on campus week: 12 hours

There will be a mid-module consisting of up to 12 hours face to face contact time devoted to developing your understanding of the core purpose and assessment of the module. Learners will be presented with details of how the learning will be structure and how to access to the learning materials for the remainder of the module.

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Structured Learning Sessions: 30 hours¿

Following the module launch week you will have a further 15 hours of contact time as a class with the module team.¿ This will typically be as 20 x 1.5-hour online classes which will be a combination of activities including lectures, demonstrations, discussions, tutorials and seminars.¿ Some sessions are likely to be in flipped classroom style, where you will be expected to watch online recordings, read materials or respond to practical activities in preparation for active engagement with problem solving in the online session.¿

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1:1 Progress Checks: 2 hours¿

As a Blended Learner understanding your progress can be a challenge so you are allocated an hour of 1:1 time with your tutor (typically 6 x 20 minute).¿ Some of these may be in small groups if appropriate.¿ These sessions may be used to discuss key topics, troubleshoot solutions, review working drafts etc.¿

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Guided Independent Learning: 344 hours.¿

The module leader will provide resources through the virtual learning environment which will include videos and presentations as well as links to useful websites and other resources.¿ Additional academic learning will be achieved through reading around the subject area, module tutors will suggest useful texts, though many others will be suitable and can be found in our e-library. You should also draw on the expertise in your workplace via your workplace mentor and other colleagues.¿ If you require help understanding any of the concepts, you should contact your module tutor for assistance.¿

As an apprentice you are constantly developing your Digital Skills as part of your substantial role, and this applies to the development of the knowledge for your modules too.¿ In some cases, there will be a significant cross over between the module content and in others less so depending on the nature of your workplace duties, this will have a direct impact on to the number of Independent Learning required.¿

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Within the Independent learning time you will be expected to complete your assignments, as a guide a typical module assignment should take around 120 hours to complete.¿

1. Demonstrate a systematic understanding of programming language and specialised packages for data analysis

2. Research, analyse and critically evaluate best practices for problem solving within context of Data Analytics

3. Demonstrate understanding of different phases and techniques of Data Mining, and to have critical ability in selecting appropriate methods to analyse specific Datasets

4. Demonstrate ability to extract, cleanse and prepare data for interpret appropriate information from a data analysis output

5. Test and develop systematic analytical approaches to problem solving and ability to present visual and coherent arguments for choice of approach.

These are indicative only. Texts are updated on an annual basis and when you start to study this module, you will be referred to an online reading list, currently provided through Keylinks. You are advised not to buy any textbooks for this module without checking the online reading list.



Sharda, R. (2017), Business Intelligence, Analytics, and Data Science: A Managerial Perspective (4th Edition), Pearson, ISBN-10: 0134633288



Marr, B. (2020), The Intelligence Revolution: Transforming Your Business with AI, Kogan Page Publishing, ISBN-10: 1789664349



Park, A. (2020), Data Science for Beginners: This Book Includes: Python Programming, Data Analysis, Machine Learning. A Complete Overview to Master the Art of Data Science from Scratch Using Python, Independent Publishing, ISBN-13: 979-8645845551



Salcedo, J. (2019), Machine Learning for Data Mining: Improve your data mining capabilities with advanced predictive modelling, Packt Publishing, ISBN-10:1838828974


Paul et al, (2020) Business Analysis. BCS Learning, ISBN 78-1-780-78017-277-4



Paul, D. 2014, Business analysis, 3rd edn, BCS, Swindon. ISBN: 178017277 x; 9781780172774.

Blackboard (VLE)

University library

LinkedIn Learning

Python or similar

JetBrains PyCharm, Visual Studio Code or similar IDE

Libraries such as Pandas, Matplotlib, NumPy

Making sense of new data is vital to allow organisations to carry out business, respond to changes and take advantage of new opportunities. In this module, you will practice, using formal methods of investigation, data analysis and learn how to extract information and insights using algorithmic approaches. The module covers common programming data structures, flow controls, data input and output, and error handling. In particular, the module places emphasis on data manipulation and presentation for data analysis. A substantial practical element is integrated into the module to enable students to use a programming language (e.g., Python) to prepare data for analysis and develop data analytical solutions.