Handling missing data is a common challenge in data analytics. The first step is to identify the missing values, often represented as "NaN" or empty fields. Once identified, several strategies can be applied depending on the nature of the data and the analysis goals.

One approach is removing missing data, where rows or columns with missing values are dropped. This works best if the missing data is minimal and doesn't significantly impact the analysis. Another method is imputation, which involves filling in the missing values with appropriate estimates like the mean, median, or mode for numerical data, or the most frequent value for categorical data. For time-series data, techniques such as forward-fill or backward-fill can be used to propagate previous or future values.

A more sophisticated approach is using predictive models like regression or k-nearest neighbors to estimate missing values based on relationships within the dataset. Alternatively, data may be left as is if analysis methods can handle incomplete data.

Learning how to handle missing data effectively is a crucial skill in data analytics. To develop this expertise, you can explore data analysis courses for beginners that cover the necessary tools and techniques for real-world applications.