# Data Science & ML面接の質問

> 実際に聞かれる質問。詳細な回答付き。

Pythonによるデータ分析と人工知能のためのデータサイエンスと機械学習

675 問題数

## Data Science & MLの全トピックを探索

- [Pythonの基礎](https://sharpskill.dev/ja/technologies/data-science/interview-questions/python-basics.md): 25問, Junior
- [Pythonオブジェクト指向プログラミング](https://sharpskill.dev/ja/technologies/data-science/interview-questions/python-oop.md): 20問, Junior
- [Pythonのデータ構造](https://sharpskill.dev/ja/technologies/data-science/interview-questions/python-data-structures.md): 20問, Junior
- [Git の基礎](https://sharpskill.dev/ja/technologies/data-science/interview-questions/git-fundamentals.md): 18問, Junior
- [SQLの基礎](https://sharpskill.dev/ja/technologies/data-science/interview-questions/sql-basics.md): 20問, Junior
- [NumPyの基礎](https://sharpskill.dev/ja/technologies/data-science/interview-questions/numpy-fundamentals.md): 22問, Junior
- [Pandasの基礎](https://sharpskill.dev/ja/technologies/data-science/interview-questions/pandas-basics.md): 22問, Junior
- [Jupyter & Google Colab](https://sharpskill.dev/ja/technologies/data-science/interview-questions/jupyter-colab.md): 16問, Junior
- [SQL Joinsと高度なクエリ](https://sharpskill.dev/ja/technologies/data-science/interview-questions/sql-joins-advanced.md): 22問, Mid-Level
- [Pandas応用](https://sharpskill.dev/ja/technologies/data-science/interview-questions/pandas-advanced.md): 24問, Mid-Level
- [Matplotlib & Seabornによる可視化](https://sharpskill.dev/ja/technologies/data-science/interview-questions/matplotlib-seaborn.md): 20問, Mid-Level
- [Plotlyによるインタラクティブな可視化](https://sharpskill.dev/ja/technologies/data-science/interview-questions/plotly-interactive.md): 18問, Mid-Level
- [記述統計](https://sharpskill.dev/ja/technologies/data-science/interview-questions/statistics-descriptive.md): 20問, Mid-Level
- [推測統計学](https://sharpskill.dev/ja/technologies/data-science/interview-questions/statistics-inferential.md): 24問, Mid-Level
- [Web Scraping](https://sharpskill.dev/ja/technologies/data-science/interview-questions/web-scraping.md): 18問, Mid-Level
- [BigQuery & Cloud Data](https://sharpskill.dev/ja/technologies/data-science/interview-questions/bigquery-cloud.md): 18問, Mid-Level
- [Feature Engineering](https://sharpskill.dev/ja/technologies/data-science/interview-questions/feature-engineering.md): 22問, Mid-Level
- [教師あり機械学習：回帰](https://sharpskill.dev/ja/technologies/data-science/interview-questions/ml-supervised-regression.md): 24問, Mid-Level
- [教師あり機械学習：分類](https://sharpskill.dev/ja/technologies/data-science/interview-questions/ml-supervised-classification.md): 24問, Mid-Level
- [決定木とアンサンブル](https://sharpskill.dev/ja/technologies/data-science/interview-questions/ml-trees-ensembles.md): 24問, Mid-Level
- [教師なしML](https://sharpskill.dev/ja/technologies/data-science/interview-questions/ml-unsupervised.md): 22問, Mid-Level
- [MLパイプラインと検証](https://sharpskill.dev/ja/technologies/data-science/interview-questions/ml-pipelines-validation.md): 22問, Mid-Level
- [時系列と予測](https://sharpskill.dev/ja/technologies/data-science/interview-questions/time-series-forecasting.md): 22問, Mid-Level
- [Deep Learningの基礎](https://sharpskill.dev/ja/technologies/data-science/interview-questions/deep-learning-fundamentals.md): 24問, Senior
- [TensorFlow & Keras](https://sharpskill.dev/ja/technologies/data-science/interview-questions/tensorflow-keras.md): 22問, Senior
- [CNN と画像分類](https://sharpskill.dev/ja/technologies/data-science/interview-questions/cnn-image-classification.md): 24問, Senior
- [RNNとシーケンス](https://sharpskill.dev/ja/technologies/data-science/interview-questions/rnn-sequences.md): 22問, Senior
- [TransformersとAttention](https://sharpskill.dev/ja/technologies/data-science/interview-questions/transformers-attention.md): 24問, Senior
- [NLPとHugging Face](https://sharpskill.dev/ja/technologies/data-science/interview-questions/nlp-huggingface.md): 24問, Senior
- [GenAIとLangChain](https://sharpskill.dev/ja/technologies/data-science/interview-questions/genai-langchain.md): 24問, Senior
- [MLOps とデプロイ](https://sharpskill.dev/ja/technologies/data-science/interview-questions/mlops-deployment.md): 24問, Senior

---

Source: SharpSkill (https://sharpskill.dev), tech interview preparation for your real stack.
HTML version of this page: https://sharpskill.dev/ja/technologies/data-science/interview-questions