Lecture

Digital History Projects: Design and Analysis

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Description

This lecture introduces the concept of projects in digital history, focusing on designing, conducting, and reflecting on research projects. It covers understanding digital history, examples of research projects, and the use of data processing pipelines. The lecture explores the importance of social relationships in historical research, the challenges of data preprocessing, and the visualization of networks. It also delves into the evolution of drainage technology in the U.S. and provides guidance on starting a project, selecting datasets, and utilizing tools for network analysis and text mining.

This video is available exclusively on Mediaspace for a restricted audience. Please log in to MediaSpace to access it if you have the necessary permissions.

Watch on Mediaspace

Official source

Related concepts (32)

Data mining

Data mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal of extracting information (with intelligent methods) from a data set and transforming the information into a comprehensible structure for further use. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD.

Data Preprocessing

Data preprocessing can refer to manipulation or dropping of data before it is used in order to ensure or enhance performance, and is an important step in the data mining process. The phrase "garbage in, garbage out" is particularly applicable to data mining and machine learning projects. Data collection methods are often loosely controlled, resulting in out-of-range values, impossible data combinations, and missing values, amongst other issues. Analyzing data that has not been carefully screened for such problems can produce misleading results.

Data science

Data science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from noisy, structured, and unstructured data. Data science also integrates domain knowledge from the underlying application domain (e.g., natural sciences, information technology, and medicine). Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession.

Data and information visualization

Data and information visualization (data viz or info viz) is the practice of designing and creating easy-to-communicate and easy-to-understand graphic or visual representations of a large amount of complex quantitative and qualitative data and information with the help of static, dynamic or interactive visual items.

Data processing

Data processing is the collection and manipulation of digital data to produce meaningful information. Data processing is a form of information processing, which is the modification (processing) of information in any manner detectable by an observer. The term "Data Processing", or "DP" has also been used to refer to a department within an organization responsible for the operation of data processing programs. Data processing may involve various processes, including: Validation – Ensuring that supplied data is correct and relevant.

Related lectures (64)

Digital Transformation: Solutions and Data

Explores digital transformation opportunities, big data, analytics, and technology innovations in business and research.

Introduction to Applied Data Analysis

Introduces the Applied Data Analysis course at EPFL, covering a broad range of data analysis topics and emphasizing continuous learning in data science.

Data Science Essentials

Covers the essentials of data science, including data handling, visualization, and analysis, emphasizing practical skills and active engagement.

LabVIEW: Data Processing and Visualization

Introduces LabVIEW for data processing and visualization, covering topics like waveform synchronization and color lookup tables.

Data Preparedness: Improving Response through Information Management MOOC: A Resilient Future: Science and Technology for Disaster Risk Reduction