🔹 Title: AMO-Bench: Large Language Models Still Struggle in High School Math Competitions

🔹 Publication Date: Published on Oct 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2510.26768
• PDF: https://arxiv.org/pdf/2510.26768
• Project Page: https://amo-bench.github.io/
• Github: https://amo-bench.github.io/

🔹 Datasets citing this paper:
• https://huggingface.co/datasets/meituan-longcat/AMO-Bench

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🔹 Title: EHR-R1: A Reasoning-Enhanced Foundational Language Model for Electronic Health Record Analysis

🔹 Publication Date: Published on Oct 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.25628
• PDF: https://arxiv.org/pdf/2510.25628
• Github: https://github.com/MAGIC-AI4Med/EHR-R1

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🔹 Title: The Era of Agentic Organization: Learning to Organize with Language Models

🔹 Publication Date: Published on Oct 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.26658
• PDF: https://arxiv.org/pdf/2510.26658

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🔹 Title: OmniLayout: Enabling Coarse-to-Fine Learning with LLMs for Universal Document Layout Generation

🔹 Publication Date: Published on Oct 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.26213
• PDF: https://arxiv.org/pdf/2510.26213

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🔹 Title: Exploring Conditions for Diffusion models in Robotic Control

🔹 Publication Date: Published on Oct 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.15510
• PDF: https://arxiv.org/pdf/2510.15510
• Project Page: https://orca-rc.github.io/
• Github: https://orca-rc.github.io/

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🔹 Title: ChartAB: A Benchmark for Chart Grounding & Dense Alignment

🔹 Publication Date: Published on Oct 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.26781
• PDF: https://arxiv.org/pdf/2510.26781
• Project Page: https://huggingface.co/datasets/umd-zhou-lab/ChartAlignBench
• Github: https://github.com/tianyi-lab/ChartAlignBench

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🔹 Title: MIRO: MultI-Reward cOnditioned pretraining improves T2I quality and efficiency

🔹 Publication Date: Published on Oct 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.25897
• PDF: https://arxiv.org/pdf/2510.25897
• Project Page: https://nicolas-dufour.github.io/miro/
• Github: https://nicolas-dufour.github.io/miro/

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🔹 Title: Surfer 2: The Next Generation of Cross-Platform Computer Use Agents

🔹 Publication Date: Published on Oct 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.19949
• PDF: https://arxiv.org/pdf/2510.19949

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🔹 Title: CLASS-IT: Conversational and Lecture-Aligned Small-Scale Instruction Tuning for BabyLMs

🔹 Publication Date: Published on Oct 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.25364
• PDF: https://arxiv.org/pdf/2510.25364

🔹 Datasets citing this paper:
• https://huggingface.co/datasets/colinglab/CLASS_IT

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🔹 Title: The End of Manual Decoding: Towards Truly End-to-End Language Models

🔹 Publication Date: Published on Oct 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.26697
• PDF: https://arxiv.org/pdf/2510.26697
• Github: https://github.com/Zacks917/AutoDeco

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🔹 Title: MedVLSynther: Synthesizing High-Quality Visual Question Answering from Medical Documents with Generator-Verifier LMMs

🔹 Publication Date: Published on Oct 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.25867
• PDF: https://arxiv.org/pdf/2510.25867
• Project Page: https://ucsc-vlaa.github.io/MedVLSynther/
• Github: https://ucsc-vlaa.github.io/MedVLSynther/

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🔹 Title: CityRiSE: Reasoning Urban Socio-Economic Status in Vision-Language Models via Reinforcement Learning

🔹 Publication Date: Published on Oct 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.22282
• PDF: https://arxiv.org/pdf/2510.22282
• Github: https://github.com/tsinghua-fib-lab/CityRiSE

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🔹 Title: PORTool: Tool-Use LLM Training with Rewarded Tree

🔹 Publication Date: Published on Oct 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.26020
• PDF: https://arxiv.org/pdf/2510.26020

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🔹 Title: L^2M^3OF: A Large Language Multimodal Model for Metal-Organic Frameworks

🔹 Publication Date: Published on Oct 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.20976
• PDF: https://arxiv.org/pdf/2510.20976

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🔹 Title: Performance Trade-offs of Optimizing Small Language Models for E-Commerce

🔹 Publication Date: Published on Oct 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.21970
• PDF: https://arxiv.org/pdf/2510.21970

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🔹 Title: POWSM: A Phonetic Open Whisper-Style Speech Foundation Model

🔹 Publication Date: Published on Oct 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.24992
• PDF: https://arxiv.org/pdf/2510.24992

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Top 100 Data Analyst Interview Questions & Answers

#DataAnalysis #InterviewQuestions #SQL #Python #Statistics #CaseStudy #DataScience

Part 1: SQL Questions (Q1-30)

#1. What is the difference between DELETE, TRUNCATE, and DROP?
A:
• DELETE is a DML command that removes rows from a table based on a WHERE clause. It is slower as it logs each row deletion and can be rolled back.
• TRUNCATE is a DDL command that quickly removes all rows from a table. It is faster, cannot be rolled back, and resets table identity.
• DROP is a DDL command that removes the entire table, including its structure, data, and indexes.

#2. Select all unique departments from the employees table.
A: Use the DISTINCT keyword.

SELECT DISTINCT department
FROM employees;

#3. Find the top 5 highest-paid employees.
A: Use ORDER BY and LIMIT.

SELECT name, salary
FROM employees
ORDER BY salary DESC
LIMIT 5;

#4. What is the difference between WHERE and HAVING?
A:
• WHERE is used to filter records before any groupings are made (i.e., it operates on individual rows).
• HAVING is used to filter groups after aggregations (GROUP BY) have been performed.

-- Find departments with more than 10 employees
SELECT department, COUNT(employee_id)
FROM employees
GROUP BY department
HAVING COUNT(employee_id) > 10;

#5. What are the different types of SQL joins?
A:
• (INNER) JOIN: Returns records that have matching values in both tables.
• LEFT (OUTER) JOIN: Returns all records from the left table, and the matched records from the right table.
• RIGHT (OUTER) JOIN: Returns all records from the right table, and the matched records from the left table.
• FULL (OUTER) JOIN: Returns all records when there is a match in either the left or right table.
• SELF JOIN: A regular join, but the table is joined with itself.

#6. Write a query to find the second-highest salary.
A: Use OFFSET or a subquery.

-- Method 1: Using OFFSET
SELECT salary
FROM employees
ORDER BY salary DESC
LIMIT 1 OFFSET 1;

-- Method 2: Using a Subquery
SELECT MAX(salary)
FROM employees
WHERE salary < (SELECT MAX(salary) FROM employees);

#7. Find duplicate emails in a customers table.
A: Group by the email column and use HAVING to find groups with a count greater than 1.

SELECT email, COUNT(email)
FROM customers
GROUP BY email
HAVING COUNT(email) > 1;

#8. What is a primary key vs. a foreign key?
A:
• A Primary Key is a constraint that uniquely identifies each record in a table. It must contain unique values and cannot contain NULL values.
• A Foreign Key is a key used to link two tables together. It is a field (or collection of fields) in one table that refers to the Primary Key in another table.

#9. Explain Window Functions. Give an example.
A: Window functions perform a calculation across a set of table rows that are somehow related to the current row. Unlike aggregate functions, they do not collapse rows.

-- Rank employees by salary within each department
SELECT
    name,
    department,
    salary,
    RANK() OVER (PARTITION BY department ORDER BY salary DESC) as dept_rank
FROM employees;

#10. What is a CTE (Common Table Expression)?
A: A CTE is a temporary, named result set that you can reference within a SELECT, INSERT, UPDATE, or DELETE statement. It helps improve readability and break down complex queries.