core analyst Interview Questions and Answers

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100 Core Analyst Interview Questions and Answers

  1. What is the difference between a core analyst and a data analyst?

    • Answer: While both roles involve data analysis, core analysts typically focus on the underlying business processes and systems, often working with large, complex datasets and contributing to strategic decision-making. Data analysts might focus more on specific projects, reporting, and visualizations, with a potentially narrower scope.

  2. Explain your experience with SQL.

    • Answer: [Describe your experience level – beginner, intermediate, advanced – and provide specific examples of SQL queries you've written, including joins, subqueries, aggregations, and window functions. Mention specific databases you've worked with (e.g., MySQL, PostgreSQL, SQL Server).]

  3. How do you handle missing data in a dataset?

    • Answer: Missing data can be handled in several ways, depending on the context and the amount of missing data. Methods include deletion (listwise or pairwise), imputation (mean, median, mode, k-NN imputation, multiple imputation), or using algorithms that handle missing data natively. The best approach depends on the nature of the data and the potential impact of the chosen method on the analysis.

  4. What are some common data visualization techniques you use?

    • Answer: I use a variety of techniques depending on the data and the message I want to convey. These include bar charts, histograms, scatter plots, line graphs, box plots, heatmaps, and treemaps. I also understand the importance of choosing appropriate visualizations for different types of data and audiences.

  5. Describe your experience with data mining techniques.

    • Answer: [Describe your experience with techniques like clustering, classification, regression, association rule mining, etc. Give specific examples of projects where you used these techniques and the results you achieved. Mention specific algorithms used (e.g., k-means, linear regression, decision trees).]

  6. What is your experience with statistical analysis?

    • Answer: [Describe your experience with hypothesis testing, regression analysis, ANOVA, t-tests, chi-squared tests, etc. Mention any statistical software packages you are proficient in (e.g., R, Python with Statsmodels/SciPy).]

  7. How do you ensure the quality of your data?

    • Answer: Data quality is paramount. I use various techniques to ensure data quality, including data profiling, data validation, data cleansing, and outlier detection. I also work closely with data sources to understand data lineage and potential issues. I document my data quality checks and remediation steps.

  8. Explain your experience with Excel.

    • Answer: [Describe your proficiency level and provide specific examples of tasks you've performed in Excel, such as using formulas (VLOOKUP, INDEX/MATCH, SUMIFS), pivot tables, macros, charts, and data manipulation techniques.]

  9. What programming languages are you proficient in?

    • Answer: [List the programming languages you know, focusing on those relevant to data analysis like Python (with libraries such as pandas, NumPy, scikit-learn), R, or Java. Describe your proficiency level for each language and provide examples of your work.]

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