BTEC HND Level 5 Unit 22 Applied Analytical Models Assignment Sample

Course: Pearson BTEC Levels 4 and 5 Higher Nationals in Computing Specification

BTEC HND Level 5 Unit 22 Applied Analytical Models is a fascinating and detailed unit that explores the use of mathematical models in the business. You will study how businesses can use analytical models to make better decisions, understand consumer behaviour and even predict future trends. The unit also covers the different types of analytical models that are available and how they can be applied in different situations.

This is an ideal unit for those who are interested in the use of analytics and data science in business. Whether you are working in a large corporation or a small startup, understanding how analytical models can help you make better decisions and gain competitive advantages is essential to success.

Buy perfect assignment solutions for BTEC HND Level 5 Unit 22 Applied Analytical Models

At Students Assignment Help UK, We have a team of professional writers who are well versed with all the concepts of BTEC HND Level 5 Unit 22 Applied Analytical Models. We provide a wide range of assignments to suit your needs. Some examples include CIPD levels 3 and 5, BTECs ATHE, HND, or an undergraduate degree in humanities with honors.

We are discussing some assignment activities in this unit. These are:

Assignment Activity 1: Examine applied analytical modelling methods.

There are a variety of analytical methods which can be used in business in order to make better decisions. These methods can be divided into three broad categories:

1. Descriptive analytics: Descriptive analytics is concerned with providing a summary of data that has already been collected. This summary can take the form of simple reports or more sophisticated visualisations such as graphs and charts. The goal of descriptive analytics is to help businesses understand what has happened in the past so that they can make better decisions in the future.
2. Prescriptive analytics: Prescriptive analytics uses algorithms and mathematical models to make recommendations about how businesses can improve their operations or solve specific business problems. For example, prescriptive analytics can be used to identify the key drivers of customer loyalty or recommend ways to reduce supply chain costs.
3. Predictive analytics: Predictive analytics takes a more forward-looking approach, using data modelling techniques such as machine learning and statistical analysis to predict future trends or behaviours. For example, predictive analytics can be used to forecast customer demand for products or predict the likelihood of default on loans.

Overall, applied analytical modelling is an important tool that can help businesses make more informed decisions and improve their performance in a wide range of areas. Whether you are working in marketing, finance, operations or any other area of business, understanding how to use analytical methods can give you a significant competitive advantage.

Assignment Activity 2: Prepare a large data set for use in an applied analytical model.

One of the key challenges in using analytical models is preparing a large data set for use in the analysis. This requires collecting and cleaning the data, as well as ensuring that it is appropriately structured to enable accurate modelling and analysis. There are a number of techniques that can be used to prepare large data sets for use in applied analytical models, including data aggregation, dimension reduction, and sampling.

• One useful strategy for preparing a large data set is to use data aggregation. This involves combining multiple sources of data into a single dataset that can be used more effectively in the analysis. For example, you might combine sales data from different regions or product categories to get a more complete picture of overall customer behaviour.
• Another common approach is to use dimension reduction techniques such as principle component analysis or cluster analysis. These methods can help you identify key trends and patterns within the data that may not be immediately apparent when examining individual variables. For example, using cluster analysis, you might discover different customer segments based on their purchasing behaviour or geographic location.
• Once you have prepared the data set, it is important to ensure that it is appropriately sampling. This means selecting a representative sample of the data that accurately reflects the population of interest. For example, if you are interested in predicting customer behaviour, you will need to make sure that your sample includes a wide range of customers from different demographics and geographical locations. Sampling techniques such as stratified sampling and random sampling can be useful for selecting an appropriate sample for your data set.

Overall, preparing a large data set for use in an applied analytical model requires careful planning and attention to detail. By using techniques such as data aggregation, dimension reduction, and sampling, you can ensure that the data set is well-structured and prepared for accurate modelling and analysis. With these tools at your disposal, you can gain valuable insights into key trends and patterns within your data and make more informed decisions that can lead to improved business performance.

Assignment Activity 3: Demonstrate the use of an analytical model with a large data set.

Once you have prepared a large data set for use in an analytical model, it is important to demonstrate the use of the model with the data. This can be done by running a series of tests or simulations with the data to show how the model works and what it is capable of predicting. For example, if you are using a predictive analytics model to forecast customer demand, you might run a series of simulations with different data sets to show how the model works and what kinds of results it is able to produce.

This demonstration will help you to understand the capabilities of the analytical model and how it can be used to improve decision-making in your business. It will also give you a better sense of the data and input parameters that are most important for achieving accurate results from the model. With this information, you can fine-tune your data set or adjust your modelling approach to get better performance from the analytical model.

Overall, demonstrating the use of an analytical model with a large data set is an important step in understanding how the model works and what it is capable of predicting. By running a series of tests or simulations, you can gain valuable insights into the model’s performance and make adjustments to improve its accuracy.

Assignment Activity 4: Investigate improvements to an applied analytical model.

After you have demonstrated the use of an analytical model with a large data set, it is important to investigate ways to improve the model’s performance. This can be done by tweaking the model’s input parameters or by using different data sets for training and testing. For example, if you are using a predictive analytics model to forecast customer demand, you might explore different methods for training the model such as using historical sales data or demographic information.

In addition to exploring different input parameters, you can also investigate improvements to the applied analytical model itself. This might include using alternative machine learning algorithms or refining your data processing techniques. For example, if your current predictive analytics model is struggling with outliers in the data, you might try using a different algorithm that is more robust to outliers.

Investigating improvements to an applied analytical model is an important step in ensuring that the model is as accurate and reliable as possible. By exploring different input parameters, data sets, and algorithms, you can find ways to improve the performance of the model and make more informed decisions about how to use it in your business.

Avail of the best assignment writing solutions from our experienced writers and impress your professors!

The assignment sample discussed above is based on BTEC HND Level 5 Unit 22 Applied Analytical Models. If you are looking for help with writing your own analytical model assignment, we can provide the expert guidance and assignment support you need to get excellent results. Just say “write my BTEC assignment for me” and our team of qualified writers will get to work on your project right away.

You can hire us to do your assignment or get online exam help in UK, and we will always deliver high-quality work that meets your expectations. You can also hire best HND assignment helpers from our team to get the assignment writing solutions you need, delivered exactly when you need them. So why wait? Get in touch with us now and take advantage of the best assignment writing services!