# Data Cleaning (Data Analytics)

> Missing values, duplicates, outliers, business rules, transformation, data quality

- 20 interview questions
- Junior
- [Interview Questions: Data Analytics](https://sharpskill.dev/en/technologies/data-analytics/interview-questions.md)

## 1. What is a missing value in a dataset?

**Answer**

A missing value represents absent or unfilled data in a field. It can appear as an empty cell, NULL in a database, or NaN in a DataFrame. Identifying missing values is the first step in data cleaning because they can distort statistical analyses and aggregations.

## 2. What is the difference between a NULL value and an empty string in a database?

**Answer**

NULL means the value is unknown or does not exist, while an empty string is a known value that happens to be empty. This distinction is fundamental in SQL because NULL cannot be compared with the = operator (IS NULL must be used), whereas an empty string can be compared normally with = ''.

## 3. What is a duplicate in a dataset?

**Answer**

A duplicate is a record that appears more than once in a dataset, either exactly (all columns identical) or partially (certain key columns identical). Duplicates distort counts, sums, and averages. Their detection typically relies on identifying key columns that should be unique.

## 17 more questions available

- Which technique allows detecting exact duplicates in SQL?
- What is an outlier in a dataset?

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## Other Data Analytics interview topics

- [Google Sheets - Fundamentals](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/google-sheets-basics.md): 20 questions, Junior
- [Google Sheets - Advanced Formulas](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/google-sheets-advanced.md): 20 questions, Junior
- [SQL - Fundamentals](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/sql-basics.md): 25 questions, Junior
- [SQL - Aggregations and Grouping](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/sql-aggregations.md): 20 questions, Junior
- [SQL - Joins](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/sql-joins.md): 20 questions, Junior
- [BigQuery - Fundamentals](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/bigquery-fundamentals.md): 20 questions, Junior
- [KPIs and Business Metrics](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/kpis-metrics.md): 20 questions, Junior
- [Descriptive Statistics](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/descriptive-statistics.md): 20 questions, Junior
- [Zapier and No-Code Automation](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/zapier-automation.md): 20 questions, Junior
- [Data Visualization Principles](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/data-viz-principles.md): 20 questions, Junior
- [Python & Pandas - Fundamentals](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/python-pandas-basics.md): 20 questions, Junior
- [Google Sheets - Automated Dashboards](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/google-sheets-dashboards.md): 20 questions, Mid-Level
- [SQL - Subqueries and CTEs](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/sql-subqueries-ctes.md): 20 questions, Mid-Level
- [SQL - Window Functions](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/sql-window-functions.md): 20 questions, Mid-Level
- [BigQuery - Advanced Features](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/bigquery-advanced.md): 20 questions, Mid-Level
- [Data Modeling](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/data-modeling.md): 20 questions, Mid-Level
- [Funnel and Conversion Analysis](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/funnel-analysis.md): 20 questions, Mid-Level
- [Cohort and Retention Analysis](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/cohort-retention.md): 20 questions, Mid-Level
- [Google Tag Manager and Tracking](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/gtm-tracking.md): 20 questions, Mid-Level
- [APIs and Webhooks](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/apis-webhooks.md): 20 questions, Mid-Level
- [dbt - Fundamentals](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/dbt-fundamentals.md): 20 questions, Mid-Level
- [AB Testing and Applied Statistics](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/ab-testing.md): 20 questions, Mid-Level
- [Looker Studio (Google Data Studio)](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/looker-studio.md): 20 questions, Mid-Level
- [Power BI - Fundamentals](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/power-bi-fundamentals.md): 20 questions, Mid-Level
- [SQL - Advanced Analytical Queries](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/sql-advanced-analytics.md): 20 questions, Senior
- [dbt - Advanced Features](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/dbt-advanced.md): 20 questions, Senior
- [Power BI - DAX and Advanced Dashboards](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/power-bi-dax.md): 20 questions, Senior
- [Python Analytics - Advanced Analysis and ML](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/python-analytics-advanced.md): 20 questions, Senior

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