📘 HiTechPlus Career Guide
Study Base SAS vs PROC SQL Logic
Top 100 Data Analyst Interview Questions & Answers: Entry Level to Advanced (2026)
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🚀 Start Practice QuizLanding a job as a Data Analyst requires mastering technical tools and statistical logic. We have compiled the top 100 interview questions categorized by topic to help you prepare effectively.
📈 Section 1: Statistics & Foundations (1-20)
1. What is Data Cleansing?Fixing errors, duplicates, and inconsistencies in raw data.
2. Define an Outlier.A data point that differs significantly from others in the dataset.
3. Difference between Mean and Median?Mean is average; Median is the middle value (best for skewed data).
4. What is a P-Value?Probability that results happened by chance; <0 .05="" is="" significant.="" span="">
5. Correlation vs Causation?Correlation is a relationship; Causation means one thing causes another.
6. What is Normal Distribution?A bell curve where data is symmetrical around the mean.
7. Descriptive vs Predictive Analytics?Descriptive summarizes the past; Predictive forecasts the future.
8. Central Limit Theorem?Large samples follow normal distribution regardless of population shape.
9. Univariate vs Bivariate Analysis?Analyzing one variable vs two variables relationship.
10. Standard Deviation?Measure of how much data varies from the mean.
11. What is Data Profiling?Initial assessment of data quality and structure.
12. Qualitative vs Quantitative?Descriptive (colors, names) vs Numerical (age, price).
13. Define Metadata.Data that provides information about other data.
14. Type I vs Type II Error?False Positive vs False Negative.
15. Data Warehouse?Central system for storing integrated data from multiple sources.
16. Key Performance Indicator (KPI)?Quantifiable measure of success over time.
17. Data Mining?Finding hidden patterns in large datasets.
18. Structured vs Unstructured Data?Fixed format (SQL) vs no fixed format (Videos/Social media).
19. Confidence Interval?Range of values where the true population parameter is likely to fall.
20. Importance of EDA?Helps understand data patterns before building models.
Must Read: Learn the critical differences between Healthcare IT vs Clinical Healthcare.
💻 Section 2: SQL & Database Skills (21-45)
21. What is a Primary Key?Unique ID for a record in a table.
22. INNER JOIN vs LEFT JOIN?Inner (matches both); Left (all from left + matches from right).
23. WHERE vs HAVING?Where filters rows; Having filters groups.
24. SQL Subquery?A query inside another query.
25. Database Normalization?Process of reducing data redundancy.
26. SQL Index?Structure to retrieve data faster.
27. UNION vs UNION ALL?Union removes duplicates; Union All keeps them.
28. ACID Properties?Atomicity, Consistency, Isolation, Durability.
29. DELETE vs TRUNCATE?Delete (row-wise, slow); Truncate (full table, fast).
30. Foreign Key?Link between two tables.
31. SQL View?Virtual table based on query results.
32. Stored Procedure?Reusable precompiled SQL code.
33. COALESCE() function?Returns the first non-null value.
34. Window Functions?Calculates across set of rows (RANK, ROW_NUMBER).
35. Common Table Expression (CTE)?Temporary named result set.
36. SQL Injection?Attack where malicious code is inserted into queries.
37. DDL vs DML?Data Definition (Create, Drop) vs Manipulation (Insert, Update).
38. Self Join?Joining a table with itself.
39. How to find duplicates?Using Group By and Having Count(*) > 1.
40. Database Sharding?Dividing large database into smaller pieces.
41. Cross Join?Cartesian product of two tables.
42. Trigger?Code that runs automatically on events.
43. Composite Key?Primary key made of two or more columns.
44. Full Outer Join?Returns all records when there is match in either table.
45. Aggregate Functions?SUM, AVG, MIN, MAX, COUNT.
🐍 Section 3: Python & Excel Tools (46-70)
46. Python Libraries for Data?Pandas, NumPy, Matplotlib, Seaborn.
47. What is a DataFrame?2D labeled data structure in Pandas.
48. loc vs iloc?Label-based vs Index-based selection.
49. Excel Pivot Table?Summarizes large data instantly.
50. XLOOKUP vs VLOOKUP?XLOOKUP is faster and more flexible.
51. Pandas fillna()?Replaces missing values.
52. Lambda function?Anonymous one-line function.
53. List vs Tuple?Mutable vs Immutable.
54. Power Query?Excel engine for data transformation.
55. Dictionary in Python?Key-Value pair collection.
56. NumPy Array vs List?Arrays are faster and memory efficient.
57. Merge vs Concat?Join based on column vs stacking.
58. Seaborn vs Matplotlib?Matplotlib (basic) vs Seaborn (statistical viz).
59. Pandas drop_duplicates()?Removes duplicate rows.
60. List Comprehension?Shorter syntax to create lists.
61. apply() function?Applies a function across dataframe axis.
62. Scikit-learn purpose?Library for Machine Learning models.
63. Vectorization?Performing operations on whole arrays.
64. Heatmap?Visual representation of data density.
65. CSV vs Excel file?Plain text vs binary spreadsheet format.
66. Python Decorator?Modifies behavior of function/class.
67. Pickling in Python?Serializing and de-serializing objects.
68. Handling Date/Time in Pandas?Using to_datetime() function.
69. Conditional Formatting?Applying styles based on cell values.
70. Excel Macro?Recording repetitive tasks for automation.
🏥 Section 4: Clinical & Industry Specific (71-90)
71. What is SAS?Standard tool for clinical data reporting.
72. SDTM and ADaM?CDISC standards for raw and analysis data.
73. What are TLFs?Tables, Listings, and Figures.
74. CDM vs SAS?Data entry vs Statistical analysis.
75. Case Report Form (CRF)?Tool to collect patient data in trials.
76. Pharmacovigilance (PV)?Monitoring drug safety and side effects.
77. Adverse Event (AE)?Unwanted medical occurrence during treatment.
78. Serious Adverse Event (SAE)?AE resulting in death or hospitalization.
79. MedDRA?Global medical dictionary for coding.
80. Case Processing?Workflow of safety report handling.
81. Triage in PV?Prioritizing cases based on seriousness.
82. Clinical Study Report (CSR)?Final document of trial results.
83. Electronic Health Records (EHR)?Digital chart of patient history.
84. HIPAA purpose?Protecting sensitive patient health info.
85. Signal Detection?Identifying new risks in drug safety data.
86. Argus Safety?Widely used drug safety database.
87. Real World Evidence (RWE)?Data from clinics/hospitals outside trials.
88. Clinical Trial Phases?Phase I, II, III, and IV for testing drugs.
89. Blinded Study?Hiding treatment details to prevent bias.
90. CDISC standards?Global framework for data exchange.
🚀 Section 5: Advanced & Behavioral (91-100)
91. What is ETL?Extract, Transform, and Load process.
92. A/B Testing?Comparing two versions for best performance.
93. Overfitting?Model fitting noise instead of patterns.
94. Data Storytelling?Communicating insights through narrative.
95. Big Data 3 Vs?Volume, Velocity, Variety.
96. Logistic Regression?Predicting binary (Yes/No) outcomes.
97. Feature Engineering?Creating new variables to improve models.
98. Data Governance?Managing data availability and security.
99. Time Series Analysis?Analyzing data points over time.
100. Why Data Analytics?Focus on your ability to find value in numbers.
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