3 Project Ideas for Beginner Data Analysts!
Data analysis is becoming one of the most in-demand skills of the 21st century. The exponential growth of data, the increase of computation power, and the reduced costs for cloud and high-performance computing allow both companies and individuals to analyze large amounts of data that were intractable 20 years ago. This type of analysis unlocks new business opportunities for companies that have decided to adopt data analytics in their business. Integrating data analytics into the core business of a company is not an easy task.
A well-established team of software engineers, data engineers, and data scientists is required, in which the team members not only have a broad experience of algorithms, software architecture, and machine learning, but also a good understanding of the business of the company. While the first three skills are easily transferable from one type of business to another, understanding the business itself takes time.
In this article, we will be taking a deeper dive into three project ideas that you can work on if you’re just starting out as a beginner data analyst. Let us take a deeper look into the examples of project ideas:
Project Idea #1 – Bike Sharing Analysis
Bike sharing is a fundamental service, commonly used in the urban mobility sector. It is easily accessible (as no driving license is required to ride a bike), is cheaper than normal car-sharing services (since bike maintenance and insurance are substantially cheaper than automobile ones), and, finally, is often a fast way to commute within the city. Therefore, understanding the driving factors of bike-sharing requests is essential for both companies and users.
From a company's perspective, identifying the expected bike demand in a specific area, within a specific time frame, can significantly increase revenue and customer satisfaction. Moreover, bike relocation can be optimized to further reduce operational costs. From a user's perspective, probably the most important factor is bike availability in the shortest wait time, which we can easily see aligning with the company's interests.
This dataset contains the hourly and daily count of rental bikes between the years 2011 and 2012 in the Capital BikeShare system with the corresponding weather and seasonal information.
With this project, you can analyze bike-sharing data from Capital Bikeshare in Washington, D.C., USA, for the period between January 1, 2011, and December 31, 2012. The data is aggregated on an hourly basis. This means that no initial and final locations of the individual rides are available, but only the total number of rides per hour. Nevertheless, additional meteorological information is available in the data, which could serve as a driving factor for identifying the total number of requests for a specific time frame (bad weather conditions could have a substantial impact on bike-sharing demand).
Project Idea #2 – Absenteeism at Work
Nowadays, with more companies adopting the WFH model, work relationships are becoming more and more trust-oriented, and conservative contracts (in which working time is strictly monitored) are being replaced with more agile ones in which the employee themselves are responsible for accounting for their working time. This liberty may lead to unregulated absenteeism and may reflect poorly on an employee's candidature, even if absent hours can be accounted for with genuine reasons.
The above database was created with records of absenteeism at work from July 2007 to July 2010 at a courier company in Brazil.
This can significantly undermine healthy working relationships. Furthermore, unregulated absenteeism can also have a negative impact on work productivity. It is possible to extend our knowledge by introducing mathematical models that are suitable for both data analysis and predictions. In this way, we will obtain the fundamental tools for deriving explanatory models and provide a generic framework for identifying causalities and effects when performing data analysis. You can create a project to gain a deeper understanding of predicting absenteeism at work based on certain metrics and factors.
Project Idea #3 – Analysis of Credit Card Defaulters
Another good example is for analyzing credit card payments of customers and using their transactional data to study the characteristics of the customers who are most likely to default, eventually building a profile of these customers. Credit card default has been a field of interest and extensive analysis for more than a decade.
There are two types of loans – secured and unsecured. A secured loan is one where some collateral is mandatory, so whenever a default happens, the banking institutions can take control of the underlying assets. This asset can vary from real estate to automobiles. In general, a secured loan is minimal risk.
The above research aimed at the case of customer default payments in Taiwan and compares the predictive accuracy of the probability of default among six data mining methods.
Unlike a secured loan, an unsecured loan does not require any underlying collateral. Lines of credit are unsecured by their very nature, so whenever a default on the payments happens, it is the credit card company or the bank that must take the loss. This concern has prompted banks and companies to invest heavily in the analysis and prediction of credit card defaults. In this chapter, we will be building a profile of the customers most likely to default using techniques such as univariate and bivariate analysis.
With this analysis, we will be able to understand the factors or characteristics of a customer who is likely to default. This profile will act as a criterion for the bank or lending facility to detect potential defaulters and take appropriate actions in a timely manner. There are many other project ideas such as analyzing bank marketing campaign data, tackling company bankruptcies, analyzing the purchase intentions of an online shopper, analyzing the online retail 2 datasets, and much more!
In conclusion, these are three basic project ideas that you can investigate as a beginner data analyst to build out your portfolio. All these project ideas are real-life examples and scenarios of how you can gain a deeper understanding of the core learning concepts.