Data Analyst Interview Questions

Handling missing or inconsistent data involves multiple steps:

•Identifying the extent and nature of the missing data.

•Determining if data is missing at random or if there's a pattern.

•Imputing missing values using mean, median, mode, or more advanced techniques like regression or machine learning algorithms.

•Correcting inconsistent data by standardizing values and correcting typos or outliers.

•Documenting all modifications for transparency.

Data normalization is the process of scaling features to a standard range, often between 0 and 1 or -1 and 1. It's important because:

•It ensures that all features contribute equally to the model's performance.

•It can help algorithms converge faster.

•It can prevent numerical instability issues in some algorithms.

For datasets that don't fit into memory:

•Use database systems that allow for efficient querying of data.

•Implement online algorithms that process data in chunks or use streaming algorithms.

•Utilize distributed computing platforms like Apache Spark or Hadoop.

•Consider data sampling or aggregation to reduce the dataset's size for preliminary analyses.

•Use data compression techniques.

A pivot table is a data summarization tool that transforms data into a more organized format. It's often used when:

•Analyzing sales data to see performance across different regions or time periods.

•Summarizing survey results based on multiple criteria.

•Comparing performance metrics across different categories or groups.

•Any situation where aggregating, summarizing, and reorganizing data would provide meaningful insights.

When visualizing data, consider:

•The audience and their familiarity with the data.

•The main message or insight to convey.

•Choosing the right type of chart/graph.

•Avoiding clutter and ensuring readability.

•Ensuring accuracy and not misrepresenting data.

•Using consistent scales and colors.

•Providing context with labels, legends, and annotations.

Assessing data's validity involves checking whether the data accurately represents the real-world scenario it's supposed to. Reliability, on the other hand, refers to the consistency of data. To assess both:

•Check the source and methodology of data collection.

•Look for inconsistencies or anomalies in the data.

•Check for missing data and understand why it's missing.

•Use statistical tests to assess reliability (e.g., Cronbach's alpha for survey data).

•Validate data against known benchmarks or external sources when available.