Find Interview Questions for Aquaq analytics

Right Answer:
Descriptive statistics summarize and describe the main features of a dataset, using measures like mean, median, mode, and standard deviation. Inferential statistics use sample data to make predictions or inferences about a larger population, often employing techniques like hypothesis testing and confidence intervals.

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Supervised learning uses labeled data to train models, meaning the output is known, while unsupervised learning uses unlabeled data, where the model tries to find patterns or groupings without predefined outcomes.

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To handle missing data in a dataset, you can use the following methods:

1. **Remove Rows/Columns**: Delete rows or columns with missing values if they are not significant.
2. **Imputation**: Fill in missing values using techniques like mean, median, mode, or more advanced methods like KNN or regression.
3. **Flagging**: Create a new column to indicate missing values for analysis.
4. **Predictive Modeling**: Use algorithms to predict and fill in missing values based on other data.
5. **Leave as Is**: In some cases, you may choose to leave missing values if they are meaningful for analysis.

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SQL (Structured Query Language) is used in data analysis to query, manipulate, and manage data stored in relational databases. It allows analysts to retrieve specific data, perform calculations, filter results, and aggregate information to derive insights from large datasets.

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Data normalization is the process of organizing data in a database to reduce redundancy and improve data integrity. It involves structuring the data into tables and defining relationships between them. Normalization is important because it helps eliminate duplicate data, ensures data consistency, and makes it easier to maintain and update the database.

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Views in dimensional modeling serve as a way to simplify complex queries by presenting data in a more user-friendly format. They can encapsulate complex joins and aggregations, making it easier for users to access and analyze data without needing to understand the underlying database structure.

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Recursive relationships can lead to complexity and ambiguity in data modeling, making it difficult to enforce constraints and maintain data integrity. To resolve them, you can create a separate linking table to manage the relationships or use additional attributes to clarify the hierarchy or relationship type.

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The role of a QA (Quality Assurance) is to ensure that the software meets specified requirements and is free of defects by conducting testing, identifying issues, and verifying that fixes are implemented correctly.

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Fishbone analysis, also known as Ishikawa or cause-and-effect diagram, is a visual tool used to identify and organize potential causes of a problem. It helps teams analyze the root causes of issues by categorizing them into different branches, resembling the bones of a fish.

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**Difference between DWH and Data Mart:**
- A Data Warehouse (DWH) is a centralized repository that stores large volumes of data from multiple sources for analysis and reporting. A Data Mart is a subset of a Data Warehouse, focused on a specific business area or department.

**Difference between Views and Materialized Views:**
- A View is a virtual table that provides a way to present data from one or more tables without storing it physically. A Materialized View, on the other hand, stores the result of a query physically, allowing for faster access at the cost of needing to refresh the data periodically.

**Indexing:**
- Indexing is a database optimization technique that improves the speed of data retrieval operations on a database table. Common indexing techniques include B-tree indexing, hash indexing, and bitmap indexing.

Right Answer:
Varchar can store variable-length strings up to 8,000 bytes, while Varchar2 can store variable-length strings up to 4,000 bytes (or up to 32,767 bytes in some configurations) and is more efficient in terms of storage and performance.

Right Answer:
Yes, Django is open source.