In the daily life of those who work with IT, it’s common to have two very controversial situations. It is not difficult to find cases where unexpected solutions end up working better than planned solutions, not to mention when the planned ones do not work at all. While technical skills are extremely important in this field, the challenge is often to come up with innovative ideas for solving known problems in unusual ways.
When starting to work on data science, it's common to feel lost or overwhelmed. Even for senior professionals who have worked on many successful projects, data analysis may seem too bleak on occasion. If you are feeling stuck or don't know how to proceed with a specific problem, the topics covered in this post can help you with some relevant insights.
When we are having trouble analyzing data, we often turn to the scientific community through discussion forums like Stack Overflow and Quora for help. We usually find that we are facing a technical problem when it is adversity related to the structuring of our project. With this in mind, we believe it is pertinent to list some major factors to succeed in any kind of data analysis. See if your project conforms to the following checklist.
Understand the Problem
A simple practice that can help you is to set a provisional title for your work early in the project. The short title should work as a summary helping you to understand the theme. Also, always do exploratory research for related work in the field, even if you think you already know enough. Digital repositories like Google Scholar and DBPL can be important allies in this essential step of your project.
As its name suggests, data science is about science. Therefore, never forget the scientific method. Always define at least one hypothesis to be verified at the end of the project. The hypothesis is the project soul and must be clear, succinct, and unambiguous. All players should be aligned with the process, tasks, and timings. Define requirements and assign priorities.
Organize Your Project in Iterative and Cyclic Steps
Discipline is freedom, and organizing your project as soon as possible will help you focus on what matters. Be in balance with your schedule setting priorities and deadlines before you start developing any tasks. The goal is to avoid rework as much as possible. Set assessment metrics according to the machine learning tasks you are going to accomplish. Before starting the development step, you must have clearly defined the expected results and how to measure them.
Focus on functionality before thinking about developing something new (trying to provide evidence that the solution is efficient and viable). Making simple diagrams on a piece of paper or listing step by step what you want to develop are easy ways to review the coherence of the action plan. If necessary, create mockups or prototypes. An exploratory data analysis approach will help you identify problems and solutions after brainstorming.
Prioritize Initial Data Collection and Preprocessing Tasks
Conduct a field study to collect high-quality, reliable data, two factors that directly impact good results in data analysis. Aim to build consistent databases using relevant features. Ideally, the database has no missing or null values, but in real scenarios, this is not always possible. In these specific cases, you can use techniques that help to estimate missing data.
Invest time and effort into feature engineering, and try to represent your data most appropriately. There will always be a software engineering design pattern that most closely matches your needs, and there are several reasons why you should take it seriously. It doesn't stop you from exploring strategies to enrich your data, such as combinations and simplifications. Exploring different sampling techniques is also a great way to optimize your results.
Explore Data Systematically and as Deeply as Possible
Use existing tools, and don't lose focus on the impact of your results. Implement your solutions in simple scripts or using Jupyter notebooks, as there is not always a need for user interaction. Use the simplest algorithms first, and then the state-of-the-art algorithms that promise the best results, always comparing the cost-effectiveness of each method or technique tried. You have to think about the performance impact that the complex solution delivers.
Use the recommended metrics in the literature to measure the performance of implemented solutions and apply statistical tests to compare the relevant results. Statistical tests make it possible to verify whether there are statistically significant differences arising from the use of different techniques, algorithm configurations, or databases. Correlations between data attributes and model explicability can also reveal unexpected behaviors in the results.
Report the Results in a Detailed, Concise and Standardized Manner
There is no point in a successful project with poor or null documentation attached. Documentation is required to transmit knowledge and experience. Describe all the steps and tasks performed, as well as the results obtained. Document all generated material, including code, functionalities, experiments, and decisions made. Remember that you report to others and not only to yourself, so be clear, concise, and effective.
Review the hypotheses and goals previously defined, describing the conclusions of each subject covered and mentioning the intended work in the future. Report your vision of the project, what did well, what did poorly, and why. Highlight the good points and mention strategies to be used in the future to attenuate the faced problems. This habit helps others not to make the same mistakes as you.
Data analysis usually implies real-life abstractions, but we must never forget that our solutions must have actionable results. With an organized and well-defined project, it will be easier to explain your problem to other staff and less costly to ask for help. EAI developed a Data Strategy Program that can be applied to all data science projects. We'll be happy to talk to you and understand how we can help to optimize your processes using data and AI.
Enlightenment.AI is a boutique data science and artificial intelligence consulting company. If you’re interested in finding out more about our services and how they can transform your business, get in touch and we'd be happy to tell you more.