Lecture

Data Analysis Replication & Creative Extension

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.

In course

CS-401: Applied data analysis

This course teaches the basic techniques, methodologies, and practical skills required to draw meaningful insights from a variety of data, with the help of the most acclaimed software tools in the data science world (pandas, scikit-learn, Spark, etc.) ...

Description

This lecture covers a project divided into two parts: replicating data analysis from a scientific paper individually and extending the analysis in a group. It includes details on project structure, grading, and deadlines. Students summarize and replicate assigned papers, prepare reports, and propose extensions. The lecture emphasizes the importance of replication in acquiring meta data science skills and critical thinking.

Instructors (2)

Robert West

Robert West is a tenure-track assistant professor of computer science at EPFL, where he heads the Data Science Lab. In his research, he develops and applies techniques in machine learning, computational social science, natural language processing, social network analysis, and data mining. Bob also collaborates closely with the Wikimedia Foundation, in his role as a Wikimedia Research Fellow. Bob’s work has won several awards, including best/outstanding paper awards at ICWSM’21, ICWSM’19, and WWW’13, a best-paper runner-up award at WWW’16, a Google Faculty Research Award, a Facebook Research Award, a Hewlett-Packard Graduate Fellowship, and a Facebook Graduate Fellowship. He is actively involved in the research community, e.g., as an Associate Editor of ICWSM and EPJ Data Science and as a co-founder of the Wiki Workshop (held at WWW and ICWSM) and the Applied Machine Learning Days. Bob received his PhD in Computer Science from Stanford University, his MSc from McGill University, Canada, and his undergraduate degree from Technische Universität München, Germany.[Last updated: 25 Aug 2021]

Maria Brbic

Maria Brbic is an assistant professor in computer science at EPFL. Prior to joining EPFL, Maria was a postdoctoral researcher in Computer Science at Stanford University working with Jure Leskovec. She received her PhD degree from University of Zagreb in 2019, while also researching at Stanford University and University of Tokyo. Her research was awarded with the Fulbright Scholarship, L’Oreal UNESCO for Women in Science Scholarship, Branimir Jernej award for outstanding publication in biology and biomedicine, and Josip Loncar Silver Plaque award for the best doctoral dissertation. She has been named a Rising Star in EECS by MIT in 2021. Her research is focused on developing new machine learning methods and applying her methods to advance biomedical research.

Official source

Related concepts (30)

Project management

Project management is the process of leading the work of a team to achieve all project goals within the given constraints. This information is usually described in project documentation, created at the beginning of the development process. The primary constraints are scope, time, and budget. The secondary challenge is to optimize the allocation of necessary inputs and apply them to meet pre-defined objectives. The objective of project management is to produce a complete project which complies with the client's objectives.

Time management

Time management is the process of planning and exercising conscious control of time spent on specific activities - especially to increase effectiveness, efficiency, and productivity. It involves of various demands upon a person relating to work, social life, family, hobbies, personal interests, and commitments with the finite nature of time. Using time effectively gives the person "choice" on spending or managing activities at their own time and expediency.

Work breakdown structure

A work-breakdown structure (WBS) in project management and systems engineering is a deliverable-oriented breakdown of a project into smaller components. A work breakdown structure is a key project management element that organizes the team's work into manageable sections. The Project Management Body of Knowledge defines the work-breakdown structure as a "hierarchical decomposition of the total scope of work to be carried out by the project team to accomplish the project objectives and create the required deliverables.

Product breakdown structure

In project management under the PRINCE2 methodology, a product breakdown structure (PBS) is a tool for analysing, documenting and communicating the outcomes of a project, and forms part of the product based planning technique. The PBS provides "an exhaustive, hierarchical tree structure of deliverables that make up the project, arranged in whole-part relationship" (Haughey, 2015). The PBS is identical in format to the work breakdown structure (WBS), but is a separate entity and is used at a different step in the planning process.

Project management software

Project management software (PMS) has the capacity to help plan, organize, and manage resource tools and develop resource estimates. Depending on the sophistication of the software, it can manage estimation and planning, scheduling, cost control and budget management, resource allocation, collaboration software, communication, decision-making, quality management, time management and documentation or administration systems. Numerous PC and browser-based project management software and contract management software products and services are available.

Related lectures (64)

Data Science Essentials

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

Project Management Essentials: Scope, Time, and Cost

Covers essential project management aspects like scope, time, and cost, along with resource allocation, schedule optimization, and risk management.

Introduction to ML for Behavioral Data

Covers machine learning approaches for personalization and their real-world application, emphasizing homework, projects, and feedback.

Introduction to Applied Data Analysis

Introduces the Applied Data Analysis course, focusing on data science tools and project work.

Big Data: Best Practices and Guidelines