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SUMMARY:[Workshop] Practical Data Wrangling
DTSTART:20260915T070000Z
DTEND:20260916T100000Z
DTSTAMP:20260901T052200Z
UID:indico-event-498@events.hpc-portal.eu
DESCRIPTION:Overview\nData is essential in data-driven projects\, as it fo
 rms the foundation for all subsequent analysis\, modeling\, and decision-m
 aking. Depending on the specific task\, raw data may be collected from a w
 ide variety of sources such as databases\, APIs\, sensors\, logs\, documen
 ts\, or images. Before it can be effectively used for analysis or machine 
 learning\, raw data must be cleaned\, transformed\, validated\, and organi
 zed into a consistent and usable format. As data comes in many different f
 orms\, including numerical\, categorical\, time series\, text\, event/log\
 , and image data\, the tools and techniques used for data wrangling can va
 ry significantly depending on the data type and the requirements of the ta
 sk.\nIn this workshop\, we will cover practical data wrangling techniques 
 for numerical\, categorical\, time series\, text\, event/log\, and image d
 ata. Participants will learn how to detect and handle missing values\, out
 liers\, inconsistencies\, duplicates\, and formatting problems. We will de
 monstrate methods for transforming and encoding categorical variables\, pa
 rsing and aggregating temporal data\, processing unstructured text\, analy
 zing event logs\, and preparing image datasets for machine learning and an
 alytics. Each session combines concepts\, demonstrations\, and hands-on ex
 ercises using realistic datasets to help participants develop practical sk
 ills that can be applied immediately in downstream modeling tasks.\nWho is
  this workshop for?\nThis workshop is designed for\n\ndata practitioners w
 ho regularly work with raw or semi-structured data and need to prepare it 
 for analysis or modeling.\ndata analysts\, data scientists\, machine learn
 ing engineers\, and software engineers who want to strengthen their practi
 cal data preprocessing skills.\ngraduate students and researchers working 
 with real-world datasets who need a structured approach to data cleaning a
 nd transformation.\n\nPrerequisites\nTo ensure a smooth learning experienc
 e\, participants should have:\n\nbasic proficiency in Python programming (
 variables\, loops\, functions) and some libraries like NumPy\, Pandas\, an
 d Matplotlib/Seaborn.\nbasic familiarity with statistics (mean\, median\, 
 variance) and introductory machine learning concepts will make it easier t
 o follow the examples.\nbe comfortable reading and writing simple code and
  working with datasets in a notebook environment.\n\nKey Takeaways\nIn thi
 s workshop\, participants will learn how to systematically clean and struc
 ture different types of real-world data\, including tabular\, time series\
 , text\, event/log\, and image data.By the end of this workshop\, particip
 ants will:\n\ngain practical experience in building reproducible data wran
 gling pipelines that improve data quality and usability for downstream dat
 a analysis and machine learning.\nunderstand common pitfalls in messy data
 sets and how to address them effectively using standard techniques and too
 ls.\nbe able to confidently transform raw datasets into well-structured in
 puts suitable for downstream modeling and analysis tasks.\n\nTentative Sch
 edule (TBA)\nDay 1\n\nIntroduction\nData Types and Data Storage Formats\nN
 umerical Data Wrangling\nCategorical Data Wrangling\nTime Series Data Wran
 gling\n\nDay 2\n\nText Data Wrangling\nEvent and Log Data\, and Image Data
  Wrangling\nWrangling Other Data Types\nSummary and Key Takeaways\n\nRegul
 ations\nDue to EuroCC3 regulations\, we CAN NOT ACCEPT generic or private 
 email addresses. Please use your official university or company email addr
 ess for registration.\nThis training is for users that live and work in th
 e European Union or a country associated with Horizon 2020. You can read m
 ore about the countries associated with Horizon2020 HERE.\nContact\nFor qu
 estions regarding this workshop or general questions about ENCCS training 
 events\, please contact training@enccs.se.\n\nhttps://events.hpc-portal.eu
 /event/498/
LOCATION:Online
URL:https://events.hpc-portal.eu/event/498/
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