Lecture

Data Science: Python for Engineers - Part II

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In course

ENG-209: Data science for engineers with Python

Ce cours propose une immersion progressive et complète dans le domaine de la Data Science à travers le langage Python. Il guide les étudiants depuis la manipulation de données brutes jusqu'à la modélisation et l'extraction de connaissances utiles à partir ...

Description

This lecture covers advanced topics in data science using Python for engineers, focusing on data wrangling with Pandas, numerical data handling with Numpy, and scientific visualization with Matplotlib. The lecture also delves into the interdisciplinary nature of data science, requiring a combination of hacking skills, math and statistics knowledge, and substantive expertise in a scientific field.

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Instructors (2)

Olivier Verscheure

SWISS DATA SCIENCE CENTER Housed at both EPFL and ETH Zurich, the newly created Swiss Data Science Center (SDSC) will address the fragmentation within today's data and analytics landscapes. The SDSC will be composed of a distributed multi-disciplinary team of data scientists and experts in domains including personalized health and personalized medicine, transportation, earth and environmental science, social science and digital humanities, and economics. The center aims to federate data providers, data and

Jean-Philippe Pellet

Teaching & PhD Courses Data science for engineers with Python ENG-209 This course offers a gradual and comprehensive introduction to the field of Data Science through the Python programming language. It guides students from raw data manipulation to modeling and the extraction of meaningful insights from real-world data.

Official source

Related concepts (32)

Engineering

Engineering is the practice of using natural science, mathematics, and the engineering design process to solve problems, increase efficiency and productivity, and improve systems. Modern engineering comprises many subfields which include designing and creating infrastructure, machinery, vehicles, electronics, materials, and energy. The discipline of engineering encompasses a broad range of more specialized fields of engineering, each with a more specific emphasis on particular areas of applied mathematics, applied science, and types of application.

Data wrangling

Data wrangling, sometimes referred to as data munging, is the process of transforming and mapping data from one "raw" data form into another format with the intent of making it more appropriate and valuable for a variety of downstream purposes such as analytics. The goal of data wrangling is to assure quality and useful data. Data analysts typically spend the majority of their time in the process of data wrangling compared to the actual analysis of the data.

Data transformation (computing)

In computing, data transformation is the process of converting data from one format or structure into another format or structure. It is a fundamental aspect of most data integration and data management tasks such as data wrangling, data warehousing, data integration and application integration. Data transformation can be simple or complex based on the required changes to the data between the source (initial) data and the target (final) data. Data transformation is typically performed via a mixture of manual and automated steps.

Systems engineering

Systems engineering is an interdisciplinary field of engineering and engineering management that focuses on how to design, integrate, and manage complex systems over their life cycles. At its core, systems engineering utilizes systems thinking principles to organize this body of knowledge. The individual outcome of such efforts, an engineered system, can be defined as a combination of components that work in synergy to collectively perform a useful function.

Data cleansing

Data cleansing or data cleaning is the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data. Data cleansing may be performed interactively with data wrangling tools, or as batch processing through scripting or a data quality firewall. After cleansing, a data set should be consistent with other similar data sets in the system.

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