#87. What is feature engineering?
A: Feature engineering is the process of using domain knowledge to create new features (predictor variables) from raw data. The goal is to improve the performance of machine learning models. Examples include combining features, creating dummy variables from categorical data, or extracting components from a date.

#88. How would you handle a very large dataset that doesn't fit into your computer's memory?
A:
• Chunking: Read and process the data in smaller chunks using options in pd.read_csv (chunksize).
• Data Types: Optimize data types to use less memory (e.g., using int32 instead of int64).
• Cloud Computing: Use cloud-based platforms like AWS, GCP, or Azure that provide scalable computing resources (e.g., using Spark with Databricks).
• Sampling: Work with a representative random sample of the data for initial exploration.

#89. Where do you go to stay up-to-date with the latest trends in data analysis?
A: I actively read blogs like Towards Data Science on Medium, follow key data scientists and analysts on LinkedIn and Twitter, and listen to podcasts. I also enjoy browsing Kaggle competitions to see how others approach complex problems and occasionally review documentation for new features in libraries like pandas and Scikit-learn.

#90. What is a key performance indicator (KPI)?
A: A KPI is a measurable value that demonstrates how effectively a company is achieving key business objectives. Organizations use KPIs to evaluate their success at reaching targets. For a data analyst, it's crucial to understand what the business's KPIs are in order to align analysis with business goals.
---
#91. What is the difference between structured and unstructured data?
A:
• Structured Data: Highly organized and formatted in a way that is easily searchable in relational databases (e.g., spreadsheets, SQL databases).
• Unstructured Data: Data that has no predefined format or organization, making it more difficult to collect, process, and analyze (e.g., text in emails, images, videos, social media posts).

#92. Why is data cleaning important?
A: Data cleaning (or data cleansing) is crucial because "garbage in, garbage out." Raw data is often messy, containing errors, inconsistencies, and missing values. If this data is not cleaned, it will lead to inaccurate analysis, flawed models, and unreliable conclusions, which can result in poor business decisions.

#93. Tell me about a time you had to work with ambiguous instructions or unclear data.
A: "I was once asked to analyze 'user engagement'. This term was very broad. I scheduled a meeting with the stakeholders (product manager, marketing lead) to clarify. I asked questions like: 'What business question are we trying to answer with this analysis?', 'Which users are we most interested in?', and 'What actions on the platform do we consider valuable engagement?'. This helped us collaboratively define engagement with specific metrics (e.g., likes, comments, session duration), which ensured my analysis was relevant and actionable."

#94. What is the difference between a dashboard and a report?
A:
• Report: A static presentation of data for a specific time period (e.g., a quarterly sales report). It's meant to inform.
• Dashboard: A dynamic, interactive BI tool that provides a real-time, at-a-glance view of key performance indicators. It's meant for monitoring and exploration.

#95. What is statistical power?
A: Statistical power is the probability that a hypothesis test will correctly reject the null hypothesis when the null hypothesis is false (i.e., the probability of avoiding a Type II error). In A/B testing, higher power means you are more likely to detect a real effect if one exists.

#96. How do you know if your sample is representative of the population?
A: The best way is through proper sampling techniques. Random sampling is the gold standard, where every member of the population has an equal chance of being selected. You can also use stratified sampling, where you divide the population into subgroups (strata) and then take a random sample from each subgroup to ensure all groups are represented proportionally.

#97. What is your favorite data visualization and why?
A: "I find the box plot to be incredibly powerful and efficient. In a single, compact chart, it visualizes the distribution of data, showing the median, quartiles (25th and 75th percentiles), and potential outliers. It's excellent for comparing distributions across multiple categories and is much more informative than a simple bar chart of means."

#98. What is survivorship bias?
A: Survivorship bias is a logical error where you concentrate on the people or things that "survived" some process and inadvertently overlook those that did not because of their lack of visibility. A classic example is analyzing the habits of successful startup founders without considering the thousands who failed, which can lead to flawed conclusions about what it takes to succeed.

#99. You are given two datasets. How would you figure out if they can be joined?
A: I would first inspect the columns in both datasets to look for a common key or field. This field should ideally be a unique identifier (like user_id, product_id). I would check that the data types of these key columns are the same. Then, I would check the overlap of values between the key columns to understand how many records would match in a join.

#100. Why do you want to be a data analyst?
A: "I am passionate about being a data analyst because I enjoy the process of transforming raw data into actionable insights that can drive real business decisions. I love the blend of technical skills like SQL and Python with the problem-solving and storytelling aspects of the role. I find it incredibly rewarding to uncover hidden patterns and help a company grow by making data-informed choices."

━━━━━━━━━━━━━━━
By: @DataScienceT ✨

🔹 Title: Continuous Autoregressive Language Models

🔹 Publication Date: Published on Oct 31

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.27688
• PDF: https://arxiv.org/pdf/2510.27688
• Project Page: https://shaochenze.github.io/blog/2025/CALM/
• Github: https://shaochenze.github.io/blog/2025/CALM

🔹 Datasets citing this paper:
No datasets found

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

🔹 Title: ThinkMorph: Emergent Properties in Multimodal Interleaved Chain-of-Thought Reasoning

🔹 Publication Date: Published on Oct 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.27492
• PDF: https://arxiv.org/pdf/2510.27492
• Project Page: https://thinkmorph.github.io/
• Github: https://github.com/ThinkMorph/ThinkMorph

🔹 Datasets citing this paper:
• https://huggingface.co/datasets/ThinkMorph/Jigsaw_Assembly
• https://huggingface.co/datasets/ThinkMorph/Visual_Search
• https://huggingface.co/datasets/ThinkMorph/Chart_Refocus
• https://huggingface.co/datasets/ThinkMorph/Spatial_Navigation

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

🔹 Title: Phased DMD: Few-step Distribution Matching Distillation via Score Matching within Subintervals

🔹 Publication Date: Published on Oct 31

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.27684
• PDF: https://arxiv.org/pdf/2510.27684

🔹 Datasets citing this paper:
No datasets found

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

🔹 Title: Visual Backdoor Attacks on MLLM Embodied Decision Making via Contrastive Trigger Learning

🔹 Publication Date: Published on Oct 31

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.27623
• PDF: https://arxiv.org/pdf/2510.27623
• Project Page: https://zqs1943.github.io/BEAT/

🔹 Datasets citing this paper:
No datasets found

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

🔹 Title: Dual-Stream Diffusion for World-Model Augmented Vision-Language-Action Model

🔹 Publication Date: Published on Oct 31

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.27607
• PDF: https://arxiv.org/pdf/2510.27607

🔹 Datasets citing this paper:
No datasets found

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

🔹 Title: HyperClick: Advancing Reliable GUI Grounding via Uncertainty Calibration

🔹 Publication Date: Published on Oct 31

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.27266
• PDF: https://arxiv.org/pdf/2510.27266

🔹 Datasets citing this paper:
No datasets found

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

🔹 Title: The Denario project: Deep knowledge AI agents for scientific discovery

🔹 Publication Date: Published on Oct 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.26887
• PDF: https://arxiv.org/pdf/2510.26887
• Github: https://github.com/AstroPilot-AI/Denario

🔹 Datasets citing this paper:
No datasets found

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

🔹 Title: INT v.s. FP: A Comprehensive Study of Fine-Grained Low-bit Quantization Formats

🔹 Publication Date: Published on Oct 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.25602
• PDF: https://arxiv.org/pdf/2510.25602
• Github: https://github.com/ChenMnZ/INT_vs_FP

🔹 Datasets citing this paper:
No datasets found

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

🔹 Title: OS-Sentinel: Towards Safety-Enhanced Mobile GUI Agents via Hybrid Validation in Realistic Workflows

🔹 Publication Date: Published on Oct 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.24411
• PDF: https://arxiv.org/pdf/2510.24411
• Github: https://github.com/OS-Copilot/OS-Sentinel

🔹 Datasets citing this paper:
No datasets found

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

🔹 Title: Defeating the Training-Inference Mismatch via FP16

🔹 Publication Date: Published on Oct 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.26788
• PDF: https://arxiv.org/pdf/2510.26788
• Github: https://github.com/sail-sg/Precision-RL

🔹 Datasets citing this paper:
No datasets found

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

🔹 Title: Higher-order Linear Attention

🔹 Publication Date: Published on Oct 31

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.27258
• PDF: https://arxiv.org/pdf/2510.27258
• Project Page: https://yifanzhang-pro.github.io/HLA
• Github: https://github.com/yifanzhang-pro/HLA

🔹 Datasets citing this paper:
No datasets found

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

🔹 Title: Spatial-SSRL: Enhancing Spatial Understanding via Self-Supervised Reinforcement Learning

🔹 Publication Date: Published on Oct 31

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.27606
• PDF: https://arxiv.org/pdf/2510.27606
• Github: https://github.com/InternLM/Spatial-SSRL

🔹 Datasets citing this paper:
No datasets found

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

🔹 Title: π_RL: Online RL Fine-tuning for Flow-based Vision-Language-Action Models

🔹 Publication Date: Published on Oct 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.25889
• PDF: https://arxiv.org/pdf/2510.25889
• Project Page: https://rlinf.readthedocs.io/en/latest/rst_source/examples/pi0.html
• Github: https://github.com/RLinf/RLinf

🔹 Datasets citing this paper:
No datasets found

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

🔹 Title: Mask-to-Height: A YOLOv11-Based Architecture for Joint Building Instance Segmentation and Height Classification from Satellite Imagery

🔹 Publication Date: Published on Oct 31

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.27224
• PDF: https://arxiv.org/pdf/2510.27224

🔹 Datasets citing this paper:
No datasets found

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

🔹 Title: Agent Lightning: Train ANY AI Agents with Reinforcement Learning

📝 Summary:
Agent Lightning is a flexible RL framework for training LLMs in any AI agent. It uniquely decouples agent execution from training, allowing seamless integration with diverse existing agents with minimal code changes. This enables robust training for complex interactions and shows stable performan...

🔹 Publication Date: Published on Aug 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.03680
• PDF: https://arxiv.org/pdf/2508.03680
• Project Page: https://www.microsoft.com/en-us/research/project/agent-lightning/
• Github: https://github.com/microsoft/agent-lightning

==================================

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

🔹 Title: Kimi Linear: An Expressive, Efficient Attention Architecture

📝 Summary:
Kimi Linear is a new hybrid linear attention architecture that, for the first time, outperforms full attention across various contexts. It achieves superior performance and efficiency, reducing KV cache and increasing decoding throughput, making it a powerful drop-in replacement.

🔹 Publication Date: Published on Oct 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.26692
• PDF: https://arxiv.org/pdf/2510.26692
• Github: https://github.com/MoonshotAI/Kimi-Linear

🔹 Models citing this paper:
• https://huggingface.co/moonshotai/Kimi-Linear-48B-A3B-Instruct
• https://huggingface.co/moonshotai/Kimi-Linear-48B-A3B-Base
• https://huggingface.co/aiqtech/Kimi-Linear-48B-A3B-Instruct

==================================

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

🔹 Title: Emu3.5: Native Multimodal Models are World Learners

📝 Summary:
Emu3.5 is a multimodal world model natively predicting vision and language states. Trained on vast video data, it uses Discrete Diffusion Adaptation for 20x faster image inference. It excels at multimodal generation, world modeling, and performs competitively.

🔹 Publication Date: Published on Oct 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.26583
• PDF: https://arxiv.org/pdf/2510.26583
• Project Page: https://emu.world/
• Github: https://github.com/baaivision/Emu3.5

🔹 Models citing this paper:
• https://huggingface.co/BAAI/Emu3.5
• https://huggingface.co/BAAI/Emu3.5-Image
• https://huggingface.co/BAAI/Emu3.5-VisionTokenizer

==================================

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

🔹 Title: olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Models

📝 Summary:
olmOCR is an open-source toolkit using a fine-tuned vision language model to convert diverse PDFs into clean, structured plain text. It preserves formatting like tables and equations, and is optimized for cost-effective large-scale batch processing, unlocking tokens for language model training.

🔹 Publication Date: Published on Feb 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2502.18443
• PDF: https://arxiv.org/pdf/2502.18443
• Github: https://github.com/allenai/olmocr

🔹 Datasets citing this paper:
• https://huggingface.co/datasets/davanstrien/test-olmocr2
• https://huggingface.co/datasets/davanstrien/newspapers-olmocr2
• https://huggingface.co/datasets/stckmn/ocr-output-Directive017-1761355297

==================================

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT