🚀 Top 100 Data Analyst Interview Questions

🧠 Data Analyst Role Basics

1. What does a data analyst do in a company?
2. What is the difference between a data analyst, data scientist, and BI analyst?
3. What is the typical workflow of a data analyst (from requirement to insight)?
4. What are the main goals of data analysis (descriptive, diagnostic, predictive, prescriptive)?
5. What is KPI and why is it important?
6. What is the difference between metrics and KPIs?
7. What is a dashboard vs a report?
8. What is exploratory data analysis (EDA)?
9. What is the difference between raw data and processed data?
10. How do you prioritize which analysis to work on first?

📊 SQL Databases

11. What is SQL and why is it critical for data analysts?
12. How do SELECT, WHERE, ORDER BY, LIMIT work?
13. How do you join two tables (INNER, LEFT, RIGHT, FULL joins)?
14. How do GROUP BY and aggregate functions (SUM, AVG, COUNT, MAX, MIN) work?
15. How do you write subqueries and CTEs?
16. How do you calculate running totals or rolling averages with window functions?
17. How do you clean and filter data directly in SQL?
18. How do you handle duplicates and NULL values in SQL?
19. How do you optimize a slow query?
20. How do you design a simple schema for a business domain (e.g., orders, users)?

🧮 Excel Spreadsheets

21. How do you use Excel for quick data cleaning and analysis?
22. How do you use SUMIF, COUNTIF, VLOOKUP / XLOOKUP in Excel?
23. How do you remove duplicates and standardize text in Excel?
24. How do you use PivotTables for summarizing data?
25. How do you build simple dashboards in Excel (charts + slicers)?
26. How do you use conditional formatting for insights?
27. How do you export data to CSV or share formatted reports?
28. How do you handle large datasets in Excel vs a database?
29. How do you avoid common Excel pitfalls (e.g., hard‑coded numbers, no labels)?
30. How do you document your Excel analyses?

📈 Data Visualization BI Tools

31. What is the purpose of data visualization?
32. When do you use bar charts, line charts, pie charts, histograms?
33. What are best practices for labeling, colors, and readability?
34. How do you design a dashboard for a non‑technical stakeholder?
35. What is the difference between a report and a self‑service dashboard?
36. How do you use Power BI / Tableau / Looker / Google Data Studio for dashboards?
37. How do you filter and slice data in a BI tool?
38. How do you handle measures and dimensions in BI tools?
39. How do you share dashboards and control access?
40. How do you tell a “data story” using charts and annotations?

📊 Descriptive Statistics EDA

41. What are mean, median, and mode?
42. What is standard deviation and variance?
43. What are quartiles and IQR?
44. How do you detect outliers and what should you do with them?
45. What is a distribution and how do you inspect it (histograms, boxplots)?
46. What is skewness and kurtosis?
47. How do you calculate growth rate, percentage change, CAGR?
48. How do you compute cohort‑style metrics (e.g., retention by signup month)?
49. How do you summarize categorical vs numerical data?
50. How do you structure an EDA notebook or report?

🛠️ Python (or R) for Data Analysis

51. Why do data analysts use Python instead of (or along with) Excel?
52. How do you load data from CSV or SQL into a pandas DataFrame?
53. How do you inspect the first/last rows, shape, data types, and missing values?
54. How do you clean missing values (dropna, fillna, interpolation)?
55. How do you filter, sort, and group data with pandas?
56. How do you calculate aggregates and pivots with groupby and pivot_table?

60. How do you compute month-on-month or week-on-week growth?
61. How do you write a query to calculate retention / churn?
62. How do you calculate LTV (lifetime value) conceptually?
63. How do you write a funnel analysis query (e.g., sign-up → activation → purchase)?
64. How do you handle time-based aggregations (daily, weekly, monthly)?
65. How do you compare cohorts (e.g., users by month of acquisition)?
66. How do you calculate lead-time, cycle-time, or other business-process metrics?
67. How do you implement A/B test-style analysis in SQL?
68. How do you approximate segmentation (RFM-style) in SQL?
69. How do you document and version your SQL queries?

🧠 Behavioral Business-Sense Questions

70. Walk me through a real-world analysis you did end-to-end.
71. Tell me about a time you presented insights to a non-technical audience.
72. Tell me about a time your analysis changed a decision or strategy.
73. Tell me about a time you found a data quality issue and how you fixed it.
74. How do you translate a vague business question into a concrete analysis?
75. How do you handle conflicting priorities from stakeholders?
76. How do you collaborate with product, marketing, and engineering teams?
77. How do you validate your analysis before sharing it?
78. How do you explain statistical or technical concepts in simple language?
79. How do you stay updated with data-analysis trends and tools?

📊 Real-World Case-Study / Scenario-Style Questions

80. Design an analysis to track product usage or feature adoption.
81. Design an analysis to evaluate marketing campaign performance.
82. Design a churn / retention dashboard for a SaaS product.
83. Design a sales-performance report for a regional team.
84. Design a customer-segmentation analysis (e.g., high-value vs low-value).
85. How would you analyze a sudden drop in website traffic or orders?
86. How would you analyze a pricing change or discount test?
87. How would you analyze customer support ticket volume and trends?
88. How would you design a simple A/B test and its success metrics?
89. How would you explain results and next steps to a manager?

🧠 Tooling, Processes Best Practices

90. What tools do you use most often as a data analyst?
91. How do you version your code and SQL (e.g., Git, folder structure)?
92. How do you document queries, dashboards, and assumptions?
93. How do you handle data privacy and PII in your analyses?
94. How do you manage permissions and access to dashboards?
95. How do you automate repetitive reports (scheduled exports, SQL jobs, etc.)?
96. How do you handle ad-hoc vs recurring analyses?
97. How do you get feedback on your dashboards and improve them?
98. What are your top 5 productivity shortcuts / habits as a data analyst?
99. What skills do you want to improve most in the next 6–12 months?

🚀 Double Tap ♥️ For Detailed Answers

ML o'rganaman deydiganlar uchun yaxshi kanal

Million inson uchun tizimlar qanday quriladi?

Videoda:
- Barcha kompaniyalarda uchraydigan kengayishdagi muammolar
- Kam xarajat bilan ko'plab insonlarga xizmat ko’rsatish
- Amazon, Facebook, Googledagi tizim dizaynidagi tajribalarim
- Tizim Dizaynini qanday tizimli o'rganish
haqida gapirdim.

Havola: youtu.be/g2g62uyBp88

menimcha it va biznes ($$) sohasidagi hammaga foydali bo'ladi

real data bilan ishlayman deganlar uchun (api bilan ishlasa ham bo'ladi ).

Below are the useful links for Regression:

https://www.seldon.io/machine-learning-regression-explained

🫱Linear Regression

https://towardsdatascience.com/mathematics-hidden-behind-linear-regression-431fe4d11969

https://christophm.github.io/interpretable-ml-book/limo.html

https://www.youtube.com/watch?v=PaFPbb66DxQ

https://www.youtube.com/watch?v=3dhcmeOTZ_Q&t=35s

https://christophm.github.io/interpretable-ml-book/limo.html

🫱Gradient Descent:
https://youtu.be/qg4PchTECck?si=kau7fBI5Lgb0kGo1

https://youtu.be/sDv4f4s2SB8?si=WxA38LHuXQf13nF-

https://d2l.ai/chapter_optimization/gd.html

https://machinelearningmastery.com/gradient-descent-for-machine-learning/

https://www.ruder.io/optimizing-gradient-descent/

🫱Polynomial Regression
https://www.analyticsvidhya.com/blog/2021/07/all-you-need-to-know-about-polynomial-regression/
https://www.youtube.com/watch?v=QptI-vDle8Y

🫱KNN
https://youtu.be/sTJApaBjong?si=gycuLY3cCYXMDnH4

🫱About metrics:
https://machinelearningmastery.com/regression-metrics-for-machine-learning/

https://www.youtube.com/watch?v=2AQKmw14mHM&ab_channel=StatQuestwithJoshStarmer

https://thestatsgeek.com/2013/10/28/r-squared-and-adjusted-r-squared/

https://stattrek.com/regression/residual-analysis

https://medium.com/@HalderNilimesh/understanding-residual-plots-in-linear-regression-models-a-comprehensive-guide-with-examples-2fb5a60daf26

https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_percentage_error.html

https://en.wikipedia.org/wiki/Mean_absolute_percentage_error

🫱Loss Function
https://www.analyticsvidhya.com/blog/2019/08/detailed-guide-7-loss-functions-machine-learning-python-code/#Difference_Between_Loss_Function_and_Cost_Function

https://heartbeat.comet.ml/5-regression-loss-functions-all-machine-learners-should-know-4fb140e9d4b0

https://www.youtube.com/watch?v=QBbC3Cjsnjg

🫱Core ML Concepts:
https://www.educative.io/answers/definition-model-fitting

https://machinelearningmastery.com/train-test-split-for-evaluating-machine-learning-algorithms/

https://ruveydakardelcetin.medium.com/the-importance-of-splitting-datasets-into-training-validation-and-test-sets-417caaeae91d

https://www.simplilearn.com/tutorials/machine-learning-tutorial/cross-validation

https://statology.org/leave-one-out-cross-validation/

https://medium.com/@mouadenna/time-series-splitting-techniques-ensuring-accurate-model-validation-5a3146db3088

https://youtu.be/EuBBz3bI-aA?si=aRSpNvSTcHTMYWGB

https://ruveydakardelcetin.medium.com/the-importance-of-splitting-datasets-into-training-validation-and-test-sets-417caaeae91d

https://www.simplilearn.com/tutorials/machine-learning-tutorial/regularization-in-machine-learning

https://learn.epam.com/myLearning/eur01.safelinks.protection.outlook.com/GetUrlReputation

https://www.geeksforgeeks.org/hyperparameter-tuning/

https://jakevdp.github.io/PythonDataScienceHandbook/05.03-hyperparameters-and-model-validation.html

https://statisticsbyjim.com/regression/model-specification-variable-selection/

🫱More Advanced

GLMs

https://christophm.github.io/interpretable-ml-book/extend-lm.html

https://learn.epam.com/myLearning/eur01.safelinks.protection.outlook.com/GetUrlReputation

https://online.stat.psu.edu/stat504/lesson/6/6.1

https://www.youtube.com/watch?v=6Nfv0Xr44y8

https://www.youtube.com/watch?v=i62gffPrZYA

Robust regression
https://machinelearningmastery.com/robust-regression-for-machine-learning-in-python/

https://online.stat.psu.edu/stat501/lesson/t/t.1/t.1.1-robust-regression-methods

https://scikit-learn.org/stable/modules/linear_model.html#different-scenario-and-useful-concepts

https://www.youtube.com/watch?v=ddHpy0ptUvI

https://www.youtube.com/watch?v=Vf7oJ6z2LCc

Data analist uchun ML ga asoslanib data asosida strategik qarorlar qabul qilish suv va havodek zarur bo'ladi (Menimcha) .Kelajakda har bir kasb egasi Kasb + AI biladigan bo'ladi . Shunday ekan bosinglar

Below are the useful links for Classification:

🫱Classification

https://www.datacamp.com/blog/classification-machine-learning

https://www.geeksforgeeks.org/machine-learning/getting-started-with-classification/

https://www.youtube.com/watch?v=ju3J7iRy6xI&list=PLoROMvodv4rOzrYsAxzQyHb8n_RWNuS1e&index=16

🫱Binary Classification

https://spotintelligence.com/2023/10/09/binary-classification/

🖐Core Models

🫱Logistic Regression

https://www.v7labs.com/blog/logistic-regression

https://medium.com/@premvardhankumar/a-deep-understanding-of-logistic-regression-with-geometric-probabilistic-and-loss-minimization-2ced042bdcc7

https://www.youtube.com/watch?v=yIYKR4sgzI8

https://www.youtube.com/watch?v=kr_Be9NVXOM&list=PLoROMvodv4rOzrYsAxzQyHb8n_RWNuS1e&index=16

https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression

🫱Support Vector Machines

https://www.skillcamper.com/blog/support-vector-machines-svm-from-hyperplanes-to-kernel-tricks

https://www.youtube.com/watch?v=efR1C6CvhmE

https://www.youtube.com/watch?v=Toet3EiSFcM

https://www.youtube.com/watch?v=Qc5IyLW_hns

https://www.youtube.com/watch?v=Op0OyOuDjcQ&list=PLoROMvodv4rOzrYsAxzQyHb8n_RWNuS1e&index=62

https://www.youtube.com/watch?v=02icdqOJsH4&list=PLoROMvodv4rOzrYsAxzQyHb8n_RWNuS1e&index=64

https://scikit-learn.org/stable/modules/svm.html

🫱 k-Nearest Neighbors

https://www.ibm.com/think/topics/knn

https://www.youtube.com/watch?v=HVXime0nQeI

https://www.youtube.com/watch?v=0pP4EwWJgIU

https://scikit-learn.org/stable/modules/neighbors.html#classification

🫱 Naiive Bayes CLassifier

https://towardsdatascience.com/naive-bayes-classifier-81d512f50a7c

https://www.youtube.com/watch?v=CPqOCI0ahss

https://scikit-learn.org/stable/modules/naive_bayes.html

🫱 Loss Functions

https://365datascience.com/tutorials/machine-learning-tutorials/cross-entropy-loss/

https://towardsdatascience.com/understanding-binary-cross-entropy-log-loss-a-visual-explanation-a3ac6025181a

https://www.geeksforgeeks.org/machine-learning/ml-common-loss-functions/#classification-loss-functions-in-machine-learning

https://www.kaggle.com/code/nghihuynh/understanding-loss-functions-for-classification/notebook

🖐Evaluation Metrics

🫱Classification threshold

https://scikit-learn.org/stable/modules/classification_threshold.html

https://www.iguazio.com/glossary/classification-threshold/

🫱Confusion Matrix

https://scikit-learn.org/stable/modules/model_evaluation.html#confusion-matrix

https://www.evidentlyai.com/classification-metrics/confusion-matrix

🫱Accuracy Precision Recall F1 F(beta)

https://www.evidentlyai.com/classification-metrics/accuracy-precision-recall

https://towardsai.net/p/l/f1-to-f-beta

https://medium.com/@douglaspsteen/beyond-the-f-1-score-a-look-at-the-f-beta-score-3743ac2ef6e3#:~:text=The%20F%2Dbeta%20score%20calculation,to%20the%20F%2D1%20Score.

https://scikit-learn.org/stable/modules/model_evaluation.html#classification-metrics

🫱 ROC and AUC

https://www.evidentlyai.com/classification-metrics/explain-roc-curve

https://www.youtube.com/watch?v=4jRBRDbJemM

https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html

🫱PR-Curve (Precision-Recall Curve)

https://medium.com/@douglaspsteen/precision-recall-curves-d32e5b290248

https://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html

🫱 Imbalanced Datasets

https://www.analyticsvidhya.com/blog/2021/06/5-techniques-to-handle-imbalanced-data-for-a-classification-problem/

https://www.analyticsvidhya.com/blog/2020/10/overcoming-class-imbalance-using-smote-techniques/

https://www.kdnuggets.com/2017/06/7-techniques-handle-imbalanced-data.html

🫱 Multiclass & Multilabel Classification

https://scikit-learn.org/stable/modules/multiclass.html

https://www.dataknowsall.com/multiclass.html

https://www.projectpro.io/article/multi-class-classification-python-example/547

https://www.geeksforgeeks.org/an-introduction-to-multilabel-classification/?ref=gcse

🫱 Micro vs. Macro Averaging

Source: How-To Geek
https://share.google/lMLzZECxCUBfuEsTg

Just do it

power bi ga datani olish usullari

«Иқтисодий хавфсизлик» блокини визуализация қилиш бўйича эксперт ечимини тайёрлаш.
Мазкур ечим қуйидагиларни ўз ичига олиши лозим:
асосий макроиқтисодий кўрсаткичлар ва молиявий барқарорлик индикаторларини визуал тақдим этиш концепцияси;
миллий индикаторлардан ҳудудий ва тармоқ даражаларигача интерактив деталлаштиришни таъминловчи бошқарув панеллари тузилмаси;
тезкор мониторинг тизими, жумладан «светофор» моделлари (KPI)ни визуализация қилиш ҳамда критик оғишлар бўйича хабардор қилиш механизмлари (алерт тизими);
фойдаланувчининг маълумотлар билан ўзаро ишлаш мантиғини акс эттирувчи демонстрацион макетлар (wireframe/UI-концепт).
Блок. Иқтисодий хавфсизлик
1. Макроиқтисодиёт:
ялпи ички маҳсулот — ЯИМ (динамикаси, таркиби);
инфляция (умумий, базавий, озиқ-овқат инфляцияси);
истеъмол нархлари индекси (CPI);
ўртача ва медиан иш ҳақи;
тўғридан-тўғри хорижий инвестициялар (FDI);
экспорт ва импорт (таркиби ва динамикаси).
2. Молиявий барқарорлик:
валюта захиралари ҳажми ва олтин-валюта фонди динамикаси;
миллий валютанинг асосий жаҳон валюталарига нисбатан курси динамикаси (USD, EUR, RUB, CNY);
давлат қарзи ва унинг таркиби (ташқи/ички);
бюджет баланси;
тўлов баланси дефицити/профицити;
банк тизими ликвидлиги;
муаммоли кредитлар улуши (NPL);
молиявий барқарорлик индекси. => #task