What are the best techniques for analyzing complex data sets?

Data cleaning is not just "an essential step"; it's a mandatory step before commencing your analysis. I typically prefer to begin with some Exploratory Data Analysis (EDA) before diving into data cleaning. EDA helps you visualize your data, making the subsequent cleaning process more straightforward. Before embarking on the cleaning process, it's crucial to establish a strategy for handling missing or null values. This strategy should align with your data and the analysis you intend to perform.

The best techniques for complex data analysis are statistics, machine learning, and data visualization. These help find patterns, make predictions, and present data clearly for informed decisions.

Before any data cleaning is started, the first step is to understand where the data is coming from and understanding the data context. There may be null or empty values that may support why a particular field is the way it is. Sometimes it is worth keeping null values rather than changing them. For example: you have a field called Is Completed with some empty values. Could mean that it hasn’t been recorded properly. Doesn’t mean that it isn’t done. Once you understand the data source and the data context, you can use this information to validate.

Analyzing complex data sets involves techniques like descriptive statistics, data visualization, correlation analysis, dimensionality reduction, clustering, regression, machine learning, and domain expertise. It's an iterative process, with an emphasis on flexibility, ethical considerations, and interactive exploration.

Effective data communication involves adopting a perspective that combines visual storytelling, customized messaging, and clear interpretation of complex data. Providing context, fostering engagement, and addressing ethical considerations are key elements in ensuring that data insights are understood and actionable for diverse audiences.

When presenting your data results, my consistent advice is to adhere to the K.I.S.S. principle: Keep It Simple, Stupid. This means avoiding convoluted language and opting for clear and straightforward communication. Additionally, make effective use of meaningful charts and dashboards that can convey the information concisely and intuitively. Furthermore, actively encourage your audience to ask questions and engage in the discussion to foster better understanding and collaboration.

In my experience, the best way to communicate data is to build it in a way that would incentivize yourself to use it. We often overlook our own work that may not be as appealing to others. After completing a dashboard, ask yourself if you would use this. Is there any unclear visuals/information that may be asked? 2 years from now, would I still use this dashboard? These are just a couple of example questions that would improve your data communication.