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🎯 Practice Questions

1. Create a list of numbers from 1 to 20.

2. Create a list containing the squares of numbers from 1 to 10.

3. Create a list containing only odd numbers from 1 to 20.

4. Convert a list of names to lowercase.

5. Replace all negative values in a list with zero using a list comprehension.

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✅ Conditional expressions allow values to be modified during list creation.

✅ List comprehensions are widely used in data cleaning, feature engineering, and machine learning workflows.

Mastering list comprehensions will help you write cleaner, more Pythonic code and prepare you for technical interviews and real-world Data Science projects.

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MemSlides: A Hierarchical Memory Driven Agent Framework for Personalized Slide Generation with Multi-turn Local Revision

📅 Publication Date: Jun 15, 2026

📑 Paper: https://arxiv.org/pdf/2606.17162.pdf

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Orca: The World is in Your Mind

📅 Publication Date:  Jun 29, 2026

📑 Paper: https://arxiv.org/pdf/2606.30534.pdf

🔗 Code: https://github.com/huggingface

📝 Description:

Orca establishes a unified world latent space through next-state-prediction modeling using multimodal data and demonstrates superior performance in downstream tasks compared to specialized baselines.Debugging processes across container boundaries on Kubernetes

Thankfully on Kubernetes, updates to the ephemeral containers feature and the addition of the --profile and --target options to kubectl debug have greatly simplified this process. There is now a fairly easy means of creating a privileged process context that shares a pid namespace with a specific target container.

https://www.enterprisedb.com/blog/debugging-processes-across-container-boundaries-kubernetes


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N - Normal Distribution: A symmetrical bell-shaped distribution that is commonly used in statistical analysis.

O - Outlier Detection: The process of identifying and removing data points that are significantly different from the rest of the dataset.

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Q - Quantitative Analysis: The process of analyzing numerical data to draw conclusions and make decisions.

R - Random Forest: An ensemble learning algorithm that builds multiple decision trees to improve prediction accuracy.

S - Support Vector Machine (SVM): A supervised learning algorithm used for classification and regression tasks.

T - Time Series Analysis: A statistical technique used to analyze and forecast time-dependent data.

U - Unsupervised Learning: A type of machine learning where the model learns patterns and relationships in data without labeled outputs.

V - Validation Set: A subset of data used to evaluate the performance of a model during training.

W - Web Scraping: The process of extracting data from websites for analysis and visualization.

X - XGBoost: An optimized gradient boosting algorithm that is widely used in machine learning competitions.

Y - Yield Curve Analysis: The study of the relationship between interest rates and the maturity of fixed-income securities.

Z - Z-Score: A standardized score that represents the number of standard deviations a data point is from the mean.


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Explanatory Data Analysis Process


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𝟰. 𝗖𝗢𝗗𝗘𝗖𝗛𝗘𝗙 𝗗𝗶𝘀𝗰𝘂𝘀𝘀𝗶𝗼𝗻 𝗚𝗿𝗼𝘂𝗽:

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𝟱. 𝗖𝗢𝗗𝗜𝗡𝗚 𝗡𝗜𝗡𝗝𝗔𝗦 𝗗𝗜𝗦𝗖𝗨𝗦𝗦𝗜𝗢𝗡 𝗚𝗿𝗼𝘂𝗽:

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𝟳. 𝗡𝗘𝗪𝗧𝗢𝗡 𝗦𝗖𝗛𝗢𝗢𝗟 𝗗𝗜𝗦𝗖𝗨𝗦𝗦𝗜𝗢𝗡 𝗚𝗥𝗢𝗨𝗣:

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