Getting a Master`s in data revenue development requires a robust command of techniques of programming design techniques according to the analytical task. This unit equips learners with the potential to implement, optimise and design the programs which can transform raw datasets into useful knowledge. Learners have to dive into the advanced concept of coding that makes them prepare for complex calendars of real-world data throughout industries like engineering, healthcare, finance, and data science.
Learners have to identify the way through which multiple programming paradigms impact analytical outcomes, such as functional, object-oriented and procedural approaches. They work with manipulation libraries of data, methods for file handling and significant tools for preparing, structuring and cleaning large datasets. It focuses on writing efficient, scalable code and readable code suitable for statistical analysis with the workflow of machine learning.
A crucial aspect of this unit emphasises program development, which automates the process of analytics. Learners learn the way to customise algorithms, models of statistical implementation and visualisation techniques, optimisation for data patterns representation. They also increase hands-on experience with version control, debugging Strategies, and best practices, which enhance collaboration and program reliability.
This unit also pays attention to the significance of selecting accurate technology and tools for particular analytical tasks. Learners have to engage with contemporary language of programming commonly utilised in the data sciences, such as MATLAB, R, Python, etc. Moreover, they have to utilise relevant libraries for conducting exploratory analysis, managing unstructured and structured data and building prediction models. The potential to integrate external data sets, databases and API is also generated.
In the end, the students have to acquire robust skills of analytical programming, which align with the professional expectations in the industry of data. They have to be equipped with the developed application of a data-driven approach, interpretation of accurate outputs and knowledge presentation efficiently. This makes them prepare for a career in software development, data analytics, business intelligence and research while certifying that they are meeting the standards of Pearson for advanced problem solving in terms of computation.
Unit aims
The main aim of the unit is to make students learned it was advanced data analysis and programming. Apart from that, some other significant objects of the unit are demonstrated below.
- To increase learners` potential to apply advanced techniques of programming to identify complex problems of data analysis.
- To develop recognition of the with through which structures and paradigms of programming influence analytical problems
- To make learners excel with skills to perform confidently with youth datasets utilising specialised libraries and tools.
- To improve the potential of learners for creating programs and algorithms which automate the process of analytics.
- To generate competence in utilising visualisation and statistical models techniques to provide presentation and interpretation of knowledge.
- To prepare learners for professional duties by improving their potential to select suitable technology is following the standards of the industry, and debug efficiently.
Learning outcomes
The main learning outcomes of unit 24, advanced programming for data analysis, are associated with the core of this unit, according to Pearson, set are demonstrated below.
LO1: Explore the tools a programmer can use to manipulate large data sets for data analysis.
- Languages of data analysis:
- Identify languages of data analysis, for example, Julia, MATLAB, SQL, SAS, R
- Languages of general programming
- Identify languages of general programming: C, C++, C#, F#, Python, Java, Visual Basics
- Address interaction methods, runtime linking, and objects with direct manipulation
- Proposal:
- Utilise data sets, utilised language, what results are going to be attended and the integration method with data set analysis
- Techniques of good coding:
- Simple designing, for example, data configuration at a high level, methods, but consistency, constant names and meaningful variables
- Generate small procedures and functions by including minimal parameters, single action, comments to identify variables and functions clearly, and descriptive names
- Logical sources of structure code, declare local variables close to keep and usage line short. Keep global variables connected with the functions` comment and their place of use
- Data structures and develop objects for a single action
- Testing designs to certify their effectiveness, test the conditions of the boundary and reliability
- Recognising bed designing test, for example, misconduction, repetitive, over complex
- Large datasets:
- Investigate the large public domain available at suitable data sets for utilising with your software tools
LO2: Design a software tool to analyse a large data set for a given scenario.
- Software design:
- Designing to include analysis, cleaning and acquisition for digital data
- Operations of the dataset:
- Operations optimisation in the development of applications, for example, pointers and hash functions, sorts, for example, heap, merge, quick, insertion, searches such as recursive, binary, trees and linear. Analysis, cleaning and acquisition of digital data
- Methods of data analysis:
- Application of an accurate range of methods in data analysis
- Quantitative methods, for example, content analysis
- Quantitative analysis methods, for example, frequency, average and range, standard deviation, descriptive analysis and hypothesis testing
- Particular analysis techniques for descriptive methods, for example, factor analysis, time series analysis, discriminant analysis, and regression analysis
LO3: Develop a software tool to analyse a large data set for a given scenario.
- Implementation:
- Utilise the accurate development tools and language
- Prepare programme code with good quality which implements are design for software tool data analysis
LO4: Test a software tool used to analyse a large data set for quality of information produced.
- Categories of testing:
- Recognise the uses of integration and unit testing for significant applications
- Recognise the data-driven potential, login and debugging potentials, Independence platform, customisability and extensibility, version control, and email notification
- Assessing data analysis effectiveness:
- Identify the way through which effective tools of data analysis, for example, accuracy, detail level, outcome clarity, execution and validity.
- Results presentation:
- Summary, methods, e.g. histogram charts, frequency, polygons, narrative, imaginative optimisation of diagrams, interpretation and tables.
Assessment criteria
Unit 24 advanced programming for data analysis contains particular assessment criteria which is associated further to the learning outcomes of which unit has been designed according to the curriculum of computing.
LO1: Explore the tools a programmer can use to manipulate large data sets for data analysis.
- 1.1 Investigate the functions of a data analysis language.
- 1.2 Prepare a proposal for analysing a large dataset, including the method of analysis and the outcomes to be achieved.
- 1.3 Examine the ways that general programming languages can interact with a data analysis language.
- 1.4 Analyse the ways code written in different programming languages can be linked and called at run time to extend the functionality of computationally intensive tasks and manipulate data analysis objects directly.
LO2: Design a software tool to analyse a large data set for a given scenario.
- 2.1 Design a software tool to carry out a specific analysis on a chosen large dataset.
- 2.2 Create a detailed test plan for a software tool, identifying expected outcomes of the analysis.
- 2.3 Apply program code from both a general programming language and a data analysis-based language in designing the software tool.
LO3: Develop a software tool to analyse a large data set for a given scenario.
- 3.1 Build a software tool for analysing a large dataset according to a developed design.
- 3.2 Modify the program to include code from both a general programming language and a data analysis-based language in building the software tool.
- 3.3 Analyse the output of the data analysis process with focus on the quality of information produced from the dataset and identify possible improvements.
LO4: Test a software tool used to analyse a large data set for quality of information produced.
- 4.1 Implement a detailed test plan on a data analysis software tool.
- 4.2 Present the results of the analysis on the chosen data set.
- 4.3 Review the outcomes, utilising the software tool and the results of testing.
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