Programming for Data Analysts

LO 1: Critically evaluate the principles of programming and apply them in a business context.

LO 2: Critically evaluate the use of code libraries in programming for a business context.

LO 3: Construct a programming solution to solve a defined business problem.

Assessment tasks

This assessment is made up of TWO Parts
Part 1 - a coding exercise in data analysis using a Python notebook.
Part 2 - writing a business report.
You will have worked on both these Parts for your Formative Assessment. Now update both Parts for your Summative Assessment as set out below. You should act on any feedback you received on your Formative submission, together with your own further learning and development across the module.

Case Scenario
Apex Financial Services (AFS) is a local company that provides small business loans. Last year, loan applications increased by over 200%, largely because of a concerted online campaign to establish a strong digital presence. Almost all loan applications and business leads are generated from search engines and digital advertisements, reflecting the decision to increase advertising spend on SEO (Search Engine Optimisation) channels such as Google, Facebook, LinkedIn and similar platforms. Get Assignment Help Now!

Despite a strong digital marketing approach, the current loan application process remains manual.

It requires the online completion of information, including gender, marital status, number of dependents, education, income etc. Loan decisions are manually categorised as either "approved" or "rejected." To date, several of these factors have been considered in the approval decision. All applications are reviewed and approved by the loan team which, given the recent increase in volumes, has resulted in skills shortages, longer loan approval times and increased potential operational and control risk. The current operating model constrains further growth.

You are employed by AFS as a Consultant Data Analyst, with programming and data analytics knowledge, as well as a deep appreciation for the need to balance business growth with a robust operational control environment. You will be leading this project with a team of programmers, with various levels of experience, and have been tasked with providing a scalable solution - that addresses key resourcing and control risks.

Specifically, the Board has instructed you to develop and maintain several partial automation processes that will help the existing loans team, freeing up their time for greater one-on-one customer contact. You need to provide a data-driven solution while working with a variety of key stakeholders each with varying objectives such as marketing, internal audit and compliance. You need to assure the board that you have consulted widely in drawing up requirements and have considered how the any code produced will be further developed and maintained. You also need to consider that any code developed will need to be accessible to (used by) other staff such as sales, customer service and support personnel.

An in-house database administrator (DBA) was able to compile a PDF of past applications which the loans team are hoping to map to previous loan approval outcomes.
The two files provided by the DBA are:

A file in PDF format called ‘Loans_Database_Table.pdf'
An Excel file, called ‘Apex Loans Data.xlsx'
(Note: These same two files are used in the Formative and the Summative).

The first file has been extracted from business loan records from the previous year, and it includes a loan approval status field (Yes/No) for each application, allowing the business to map inputs to outcomes for a possible supervised machine learning exercise.

The Excel file is maintained by the Sales team and it is currently being saved in a shared folder. This increases the chance of duplication and missing values.

You will need to reflect the learnings throughout this module and consider the learning outcomes particularly LO 3: Construct a programming solution to solve a defined business problem as you create your answer.

Part 1: Construct a Programming Solution (LO3)
In Part 1, you will deliver an interactive Python notebook (a . ipynb file) using Google Colab, with the code used, with comments, to explain the scripts, the libraries used, and the logic. All such commentary should be written using the built-in markup language (Markdown text) or embedded using the standard Python syntax.

The notebook which you create should highlight some of the key findings which you have in the data and the insights which you can provide to the business. The tasks which need to be completed in the Python Notebook include the following:

Task 1: Loan Data Automation

Create a new .ipynb notebook within Google Colab and load the TWO data files provided by the DBA. Programmatically extract the two datasets from these two files which contains information about past loan records. The integers shown in each column of the loan datasets are to be interpreted as follows and you may read them as strings or numbers in your code as you deem appropriate.:
Gender: 1-Male, 2-Female

Married: 0-Single, 1-Married
Dependents: 0, 1, 2, 3+
Graduate: 0-No, 1-Yes
Self_Employed: 0-No, 1-Yes
Credit_History: 0-No, 1-Yes
Property_Area: 1-Urban, 2-Semiurban, 3-Rural
You should use Python to load the information of these datasets in memory. You should also add comments to your notebook, explaining the steps taken to load the data, how you treated the PDF data, the libraries called and the overall procedure. Recall this will be used for training colleagues in future.

Task 2: Descriptive analysis

First, check the datasets and make sure the data that comes from these two files is valid. Ensure your loan data is correctly indexed on the LoanID column.
Then, clean the loan data. Provide an explanation of the steps taken to ensure data preparation for analysis such as the correction of duplicates, missing values, outliers etc.

Next, carry out Descriptive analysis on the current loan data. Your notebook file should contain the code and output of some basic Exploratory Data Analysis (EDA) which must include the following:

The total amount loaned by AFS.
The average amount loaned.
The average loan term.
The total number of applicants broken down into Approved and Rejected, the number of males and female in each case, and shown in an appropriate chart.
The maximum and minimum of amounts loaned and shown in an appropriate chart.
The number of self-employed who had their loan approved, expressed as a percentage of all who had their loan approved, and shown in an appropriate chart.

Optional (additional marks will be given for the following)
The income distribution of all main applicants, showing average and standard deviation.
The top ten applicants by loan amount.
The distribution of properties (rural, urban etc) of all loan applicants, shown in an appropriate chart.

The code must be commented using the built-in markup facility in Colab or embedded commentary so that it can be maintained in future. The output of calculations and visualisations should be showed in the notebook and in the report in Part 2. This code and outputs should then be copied and pasted as Appendix 1 in your Part 2 report. Also, this pre-run code file (Python Jupyter notebook) MUST then be embedded into your report. Details of how you can do that is provided in the next section.

Part 2: Report - Business Case (LO1, LO2)
Using the scenario given, develop a report that proposes and justifies the continued development and maintenance of the code that has been written by the programming team.

The format of the report should include:

Introduction: This should first set out the purpose of the report and its structure. It must then cover the current business environment of companies like AFS, the problems that the programming solution has addressed, and what impact and benefits your proposed development and maintenance of the code might have on the business. You should also mention the implications of not proceeding and the kind of human resources needed. There is no need to discuss project finances, but your introduction must comment on the following:
the source and nature of the supplied dataset and its suitability for data analytics;
what user requirements have been considered (how will the tool be used);
the challenges the team faces in further developing and maintaining the code, the benefits of using reusable code and any regulatory and ethical implications.

Approach: Describe the approach you took to implement your solution. This should be based on a suitable data analysis lifecycle or framework, such as one covered in the module, Describe, especially, the key steps taken in loading and preparing the data for analysis. Use the outputs (calculated values and any charts and visuals) from your EDA in Part 1 to explain the key findings. Key findings can relate to any business insight that might be gained from the analysis of the historical loan records, as well as any general lessons about the approach being taken for this project. The AFS board want to know if your approach works, is sustainable and worth building on.

You should provide discussion of
The code libraries used (what these are and why they are needed);
The reasons for choice of language and platform;
The design (e.g., using pseudo code) to aid understanding and allow the design to be implemented in other languages if needed (code agnostic);
Testing the code (e.g., by using Excel) to confirm that the programme works; and
Maintenance of any code to allow controlled and efficient development (e.g., by version control using Git and good commentary)

Recommendations for future work: Briefly explain how your solution could be further developed to build a predictive model using the historical data provided. (i.e. A model that can be trained to predict the outcome of any future loan application.)

This should include a short explanation of the techniques, libraries, tools, and objective functions used to evaluate the precision of your recommended predictive model.
Note: There is no need to implement your ideas in code or build a predictive model

Your recommendation must also discuss:
how existing data collection and data quality may be improved;
if other libraries might be used to increase model accuracy;
additional training or support the staff will need in order to use these new tools.
safeguards needed to ensure compliance to regulatory and ethical standards.

Conclusions: A brief conclusion summarising the main points in the report.

In writing your report, use the insight and knowledge provided in this module but also leverage sound academic research to support your report. As you develop your work, you should self- evaluate your developing draft against the criteria set out in the Marking Guide below (See Section 5).

Report Structure and Referencing
In addition, ten marks are awarded for the overall professionalism of your report and the adoption of academic standards.
Guidelines:
Your report should follow the section naming structure and order set out in the Brief. You should also add your own sub-headings as you see fit to demonstrate your ability to develop structure and content.
Your report should include an auto-generated contents page including section headings and sub-headings. The contents page should also include a page-referenced list all tables, charts and figures provided in your report. Remember to number all pages in your report, for
example ‘Page 8 of 12'.
Ensure you develop your discussion in a logical progression: Findings, inferences, conclusions, recommendations.
Do not make general assertions without supporting evidence.