I am creating a written portfolio of evidence for my Data Analyst level 4 apprenticeship and my apprenticeship training provider has provided me with the answers I wrote to lots of separate questions, which, when combined, will provide the necessary content for my portfolio of evidence. however, the questions will not automatically flow together as one piece of text, because I wrote the answers to each one of them in isolation. Therefore I need you to reproduce the text I am about to copy as one written document which flows well. Do not change the order and do not remove any of the details, simply join the paragraphs in a more flowing way and add context about myself or about my organisation (Aon) where you feel it's needed. highlight any areas where you feel you need more information from me in order to do a good job of this. Please write a 200 word summary at the end of what I learnt during this module and the implications for my work as an analyst 4. Descriptive analytics (K14, K13, S10, B1, K15)Predictive analytics uses statistical models to analyse historical data in order to make predictions about future outcomes. Predictive models attempt to simulate real-world systems, but in a simplified way. This allows users of the models to amend variables in the model to predict what impact this would have on the outcome.

The key principles of predictive analytics are data quality, use of statistical and machine learning techniques, validation of models on real data to test the accuracy of the prediction and production of actionable insights. Types of predictive models include linear regressions and decision trees. Predictive analytics can support decision-making by helping decision-makers to understand the relationships between variables and what future outcome they can potentially expect, based on different levels of these variables. One example of how it can bring value to businesses in my sector, HR, is where predictive analytics is used to predict which factors result in an increased likelihood that an employee will leave the company. For example, an analysis I worked on looked at the impact of factors such as engagement scores, salary increases over the past 3 years, tenure and performance rating on an employee's likelihood to leave a company. Understanding which factors are most influential in driving an employee to leave can help inform HR leaders about which factors they should focus on in order to increase retention rates. Prescriptive analytics uses the output of analysis to provide recommendations on actions to take in order to achieve desired outcomes. In the context of HR, prescriptive analytics can be used to guide workforce planning, talent management and other strategic talent decisions by analysing workforce data to predict the outcomes of various interventions. For example, an application could involve looking at the impact of implementing different development programmes on employee engagement scores, with a recommendation on which combination of programmes will raise the scores the most. The key risks associated with predictive analytics include data quality issues, model bias, privacy concerns and a lack of transparency around what the model is doing.

The use of historical data can cause or perpetuate biases, and lead to discrimination. Using personal data can raise privacy risks. Black-box models make it difficult to determine whether the methodology is correct or whether the decisions made by the model are in line with the intended approach.

These risks can be mitigated through robust data governance, bias testing, high standards for data privacy and adhering to ethical AI principles. Correlation Matrix_Pay Analysis 2020.JPG I have provided an example of a correlation matrix, created using Python, which is a descriptive statistics approach to identify the strength and direction of the linear relationships between variables. Correlations range from -1 to +1, with negative values indicating a negative relationship and positive values indicating positive relationships. In this correlation matrix, I apply this methodology to analyse the relationships between the variables.

From the correlation matrix, it can be seen that the correlation between age and salary is 0.464429 or 46%. This led me to formulate the hypothesis that that there is a moderately strong positive relationship between age and salary (i.e. older employees are paid more). Model summary_Pay Analysis 2020.JPG The r-squared figure describes how well the model fits the data, i.e. how effective it is at predicting the independent variable. It is a measure which represents the proportion of the variance of the dependent variable that can be accounted for by the effect of the independent variables. The r-squared in this case was 0.399, which indicates that 39.9% of the variance in salaries can be explained by the variance in age and the number of direct reports an employee has. The maximum possible value for r-squared is 1, which would mean that 100% of the variance can be explained by the independent variables. The coefficients for each of the independent variables define the model equation. Positive coefficients indicate a positive impact on the relationship.

The points below provide some interpretations of the model summary: • The dependent variable in my model is salary, while the independent variables are age and number of direct reports. • The r-squared of 0.399 indicates that my model predicts a moderately positive relationship between age and number of direct reports and salary. • The dependent variables have p-values of less than 0.05, which indicates that a statistically significant relationship exists between the independent variable and each of the dependent variables, and implying that the model predictions will be reliable. • The coefficients describe the effect sizes. The coefficient for number of direct reports is very large (18,830), showing that variance in this variable will have a very large impact on the independent variable. This is likely due to the fact that the total range for this variable is relatively small. The coefficient for age is 2732, which shows that for every 1 year increase in age (as the units are in years), salary will increase by £2,732 (all else equal).

Excel model predicting value_Pay Analysis 2020.JPG The model predicted that the salary for an employee who is aged 30 and who has 1 direct report will be £96,179.10. This was calculated using the following formula: predicted value for independent variable = constant coefficient + (Age coefficient * Age value) + (Number of direct reports coefficient * Number of direct reports value). This formula is derived from y = mx +C, which is the standard equation for a line.

In order to get the data in a suitable format to conduct the linear regression analysis, I needed to calculate age using employees' date of birth and today's date. I performed this transformation in Excel, using the YEARFRAC() function. I communicated the findings from my linear regression analysis to the HR Director for the client company involved, using a table on a slide which showed some example predicted data points and a summary strapline to confirm that age and number of direct reports were significant factors in determining an employee's salary. Using the example predicted data points helped to provide him with some context around what employees of different ages may be likely to be paid, and what the impact of adding or reducing direct reports was likely to be. When agreeing the scope and limitations for the project, I took the initiative to set up a meeting with the key stakeholder (the HR Director) firstly to understand his objectives for the analysis and to understand why he wanted to analyse the different factors driving pay variance in his organisation, to make sure that I was including variables that would assist in solving the business problem he was aiming to address with this exercise. This model was one of a range of different models which we analysed as part of a wider project. Conducting the linear regression project involved comprehensive planning, risk mitigation strategies, and robust data security protocols to maintain a productive, professional, and secure working environment. I used a rigorous statistical methodology, which adheres to industry best practices. Clear communication and collaborative sessions with stakeholders facilitated productive knowledge sharing. The culmination was the delivery of actionable, data-driven insights and recommendations tailored to my stakeholders' objectives, which they were able to use to support effective decision-making.

Based on your reading and analysis of section 5 above, please compare, contrast, analyse, and evaluate the key points in this section and provide a concise summary of your learning. You must include the terms 'compare', 'contrast', 'analysis', 'justify' and 'evaluate' within your answer. (e.g. Evaluate the model you used compared to some other machine learning models; Analyse the usefulness of the regression analysis for your organisation). (Max. 150 words)