Data Analytics Interview Questions – Excellence Technology Data Analytics Interview Questions – Excellence Technology

Data Analytics
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Data Analytics Interview Questions

Data analytics involves analyzing and interpreting data to extract insights, trends, and patterns. While data science encompasses a broader range, including machine learning and predictive modeling, data analytics focuses on examining data to inform decision-making.

Structured data is organized and easily queryable, often residing in databases with a clear schema. Unstructured data lacks a predefined data model and includes information like text, images, or videos.

Data cleaning involves handling missing values, addressing outliers, and ensuring consistency. Data preparation includes transforming and standardizing variables, encoding categorical features, and scaling numeric values for analysis.

A pivot table is a data summarization tool that allows users to reorganize and analyze data in a tabular form. It is useful for exploring relationships between variables, aggregating values, and generating insights.

Correlation measures the statistical association between two variables. The correlation coefficient, often denoted as "r," ranges from -1 to 1. A positive value indicates a positive correlation, negative for negative correlation, and 0 for no correlation.

I would use clear and concise visualizations such as charts or graphs to convey key findings. Narrating the story behind the data, focusing on actionable insights, and avoiding technical jargon are crucial when communicating with non-technical stakeholders.

KPIs are measurable values that demonstrate how effectively a company is achieving its key business objectives. Selecting relevant KPIs involves aligning them with business goals, ensuring they are specific, measurable, achievable, relevant, and time-bound (SMART).

Outliers can be detected through statistical methods or visualization techniques. Addressing outliers is important because they can skew results, impact the accuracy of statistical analyses, and affect the reliability of predictive models.

Hypothesis testing is used to make inferences about a population based on a sample of data. It helps determine whether observed effects are statistically significant and provides a basis for making informed decisions or recommendations.

Utilize this question to showcase a specific project from your experience, covering aspects like problem definition, data analysis methods, challenges faced, and the impact of your work on business decisions or outcomes.

Cohort analysis involves grouping users who share a common characteristic and analyzing their behavior over time. It is often used to study user retention, engagement, and patterns, particularly in areas like customer segmentation and product usage analysis.

Descriptive statistics summarize and describe the main features of a dataset, while inferential statistics make inferences and predictions about a population based on a sample of data. Descriptive statistics include measures like mean and median, while inferential statistics involve hypothesis testing and confidence intervals.

I would prioritize tasks based on the project's critical path, communicate realistic expectations regarding what can be delivered within the given timeframe, and focus on delivering actionable insights even if a comprehensive analysis is not possible within the tight deadline.

Describe a situation where data discrepancies or inconsistencies were identified. Addressing such issues involves collaborating with data sources, implementing data validation checks, and establishing data quality standards. It's crucial to document the steps taken to ensure transparency.

I stay informed through continuous learning, attending conferences, participating in online forums, and reading industry publications. Engaging in hands-on projects and collaborating with peers also contribute to staying current in the rapidly evolving field of data analytics.

Common pitfalls include confirmation bias, incomplete data, and overfitting models. To avoid these, I emphasize thorough exploratory data analysis, employ robust statistical methods, and regularly validate assumptions. Collaboration with domain experts also helps in ensuring a comprehensive analysis.

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