There are many data analytics project for students which get derailed at different stages of the project life cycle due to inherent challenges associated with data collection, processing, integration, and visualization. Students often lack the skills and resources required to handle challenges in working with Big Data projects, getting overwhelmed with the volume, variety, and readability of information. According to a leading report on the success rate of data science projects, less than 20% actually make it to the eventual finishing line. It can be said that Paretoâ€™s 80:20 rule applies to data analytics projects for students too! 20 percent of successful data analytics projects shall decide the future.
In this article, I have pointed to some of the important aspects of data analytics, and how student managers can address the complex and perennial challenges linked with the projects that can decide the course of their careers.
Data Maturity Roadmap
To succeed with any data analytics project for students, you should be ready to take some risky steps linked with the evaluation of data maturity and transformation processes. Maturity is a very important aspect of these processes. It invariably prepares project developers to see the purpose of the whole project. Letâ€™s understand how you can evaluate data maturity at any stage of project development.
- Data analytics is the journey, not the destination.
The first step to succeed with data analytics as a student starts with your understanding of the important steps involved in data preparation. Thatâ€™s the foundational baseline for any project.
Most student analysts pursuing projects in data analytics misread their judgment about project outlines, hoping their efforts would certainly find an answer to the problem under investigation and analysis. Leveraging your data goes beyond implementing tools. It is a journey your organization will take to understand what is important to measure and how you use that information to drive improved decision-making.
- Pursue any project goal like a business operation
Most projects fail because they miss out on an important dimension of data maturity. Any projectâ€™s success can be outlined as the amalgamation of skills, processes, decisions, and techniques taken to transform data that shows â€œwhat happenedâ€ to â€œwhat is happeningâ€ to â€œwhat will happen.â€ The three stages of data maturity create ample opportunities for your project when it comes to transforming a basic idea into descriptive, diagnostic, predictive, and finally, prescriptive analytics. Your goal should always be targeting Prescriptive Analytics. If you aim for the highest form of machine-level intelligence, your data analytics project can easily be pursued as automated Artificial Intelligence and machine learning algorithms for all kinds of business challenges.
Thatâ€™s why best data analytics projects for students often revolve around the idea of data maturity concepts delivering either predictive or prescriptive intelligence for key business functions in an organization â€“ Marketing, Sales, Finance, IT and Operations, and HR. Only recently, data science has opened up for product engineering and software development services, which takes the descriptive and diagnostic approaches to succeed.
Data Visualization / Data Storytelling
Every project speaks its own story about the journey. From- answering typical questions such as:
- How did I get the data?
- Why did I use the â€œXâ€ tool to churn and analyze data?
- Does the data meet my project goals?
- Could I trade off accuracy for real-time analytics speed and reliability?
to – creating a value-based evaluation matrix for data maturity scoring, visualization based on data analytics is an enormously pivotal process in business intelligence operations.
Business leaders rely on various types of data analytics and visualization tools to make important decisions related to profit and loss, the financial health of the company, and employee management. In recent years, data analytics applications have exploded beyond imagination largely due to the arrival of open-source data analytics projects for students. Though branded as part of academic research and innovation, these types of projects often find backing from leading industry experts and AI trainers who are more than willing to contribute to the overall development of the idea and rectify issues that could derail the project.
If you are committed to your project and understand how to integrate data maturity and data visualization into the operations, you will succeed in achieving your goals.