Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Lecture

Data Science Essentials: Python, Numpy, Pandas, and Scikit-learn

In course

COM-490: Large-scale data science for real-world data

This hands-on course covers tools and methods used by data scientists, from researching solutions to scaling prototypes on Spark clusters. Students engage with the full data engineering and data science pipeline, from data acquisition to extracting insight ...

Description

This lecture provides a general introduction to Data Science, covering the course organization, a Python crash course, and data science using the Python scientific stack. The exercises include extracting information from DNA sequences, one-hot encoding DNA sequences, and operating on dataframes for DNA sequences classification with random forests.

Instructors (3)

Sofiane Sarni

Pamela Isabel Delgado Borda

I am a PhD student in the School of Computer and Communication Sciences at EPFL. I am part of the Operating Systems Laboratory and my advisor is Prof. Willy Zwaenepoel. I received my Bachelor's degree in Systems Engineering from Universidad Catolica Boliviana, Bolivia in 2008 and Master's degree in Computer Science, specialization Foundations of Software from EPFL in 2012.

Olivier Verscheure

Official source

Related concepts (32)

Scikit-learn

scikit-learn (formerly scikits.learn and also known as sklearn) is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. Scikit-learn is a NumFOCUS fiscally sponsored project. The scikit-learn project started as scikits.

Python (programming language)

Python is a high-level, general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation. Python is dynamically typed and garbage-collected. It supports multiple programming paradigms, including structured (particularly procedural), object-oriented and functional programming. It is often described as a "batteries included" language due to its comprehensive standard library.

Renku

, or haikai no renga, is a Japanese form of popular collaborative linked verse poetry. It is a development of the older Japanese poetic tradition of ushin renga, or orthodox collaborative linked verse. At renku gatherings participating poets take turns providing alternating verses of 17 and 14 morae. Initially haikai no renga distinguished itself through vulgarity and coarseness of wit, before growing into a legitimate artistic tradition, and eventually giving birth to the haiku form of Japanese poetry.

Hokku

is the opening stanza of a Japanese orthodox collaborative linked poem, renga, or of its later derivative, renku (haikai no renga). From the time of Matsuo Bashō (1644–1694), the hokku began to appear as an independent poem, and was also incorporated in haibun (in combination with prose). In the late 19th century, Masaoka Shiki (1867–1902) renamed the standalone hokku as "haiku", and the latter term is now generally applied retrospectively to all hokku appearing independently of renku or renga, irrespective of when they were written.

Kireji

are a special category of words used in certain types of Japanese traditional poetry. It is regarded as a requirement in traditional haiku, as well as in the hokku, or opening verse, of both classical renga and its derivative renku (haikai no renga). There is no exact equivalent of kireji in English, and its function can be difficult to define. It is said to supply structural support to the verse. When placed at the end of a verse, it provides a dignified ending, concluding the verse with a heightened sense of closure.

Related lectures (52)

General Introduction to Data Science

Offers a comprehensive introduction to Data Science, covering Python, Numpy, Pandas, Matplotlib, and Scikit-learn, with a focus on practical exercises and collaborative work.

Critical Data Studies: Reproducibility and Renku

Explores the significance of reproducibility in data science and introduces Renku, a platform for managing data-driven projects.

Data Wrangling with Hadoop: Storage Formats and Hive

Explores data wrangling with Hadoop, emphasizing storage formats and Hive for big data processing.

Elements of Collaborative Data Science

Introduces collaborative data science tools like Jupyter notebooks, Docker, and Git, emphasizing data versioning and containerization.

Collaborative Data Science