Python day 3

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🚨 Data Engineer Hiring Drive — Do not miss this unique opportunity

AccioJob is conducting a drive with Medha AI

💻 Show your skills first. Don’t wait for your resume to get shortlisted.

🔹 Role: Data Engineer
🔹 CTC: ₹ 8 LPA
🔹 Location: Bangalore (Onsite)
🔹 Graduation Year: 2025 & 2026
🔹 Degree: • B.Tech / BE

⏳ Limited slots — apply fast
👉 Apply now: https://go.acciojob.com/aSXCXr

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Cisco is hiring Software Engineer
For 2024, 2025 gards
Location: Multiple
https://careers.cisco.com/global/en/job/CISCISGLOBAL2011632EXTERNALENGLOBAL/Software-Engineer

Greedy Games is hiring Full Stack Developer Intern
For 2024, 2025 gards
Location: Bangalore
https://www.linkedin.com/jobs/view/4407342475/

IBM is hiring Associate System Engineer
For 2024, 2025 gards
Location: Multiple
https://careers.ibm.com/en_US/careers/JobDetail/Associate-System-Engineer/103807

Certify Me is hiring Full Stack Developer Intern
For 2026, 2027 gards
Location: Bangalore
https://www.linkedin.com/jobs/view/4406297081/

Oracle
Position: Software Developer 1
Qualification: Bachelor’s/ Master’s Degree
Experience: 0 - 2 (Years)
Location: Bengaluru; Hyderabad, India
📌Apply Now: https://careers.oracle.com/en/sites/jobsearch/jobs/preview/321672/?keyword=Software+Developer+1&location=India&locationId=300000000106947

𝐖𝐢𝐩𝐫𝐨
Position: Trainee
Qualifications: Bachelor’s/ Master’s degree
Experience: Freshers
Location: Gurugram, India
📌Apply Now: https://careers.wipro.com/job/TRAINEE/165563-en_US
𝐖𝐢𝐩𝐫𝐨
Position: Trainee
Qualifications: Bachelor’s/ Master’s degree
Experience: Freshers
Location: Gurugram, India
📌Apply Now: https://careers.wipro.com/job/TRAINEE/165563-en_US

NVIDIA
Position: Test and Tools Development Engineer
Qualifications: Bachelor’s Degree
Experience: Freshers
Location: Pune, India
📌Apply Now: https://jobs.nvidia.com/careers/job/893394964582?domain=nvidia.com&hl=en

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🚀 *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?
57. How do you merge/join multiple DataFrames?
58. How do you create basic visualizations with matplotlib / seaborn?
59. How do you save processed data back to CSV or database?
60. How do you write reusable Python functions for common analysis patterns?

*🔍 Advanced Analytics & SQL Patterns*

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

*🧠 Behavioral & Business‑Sense Questions*

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

*📊 Real‑World Case‑Study / Scenario‑Style Questions*

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

*🧠 Tooling, Processes & Best Practices*

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

🚀 *Double Tap ♥️ For More*

Python day 5