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UID:20250401T200218EDT-8266Ul53Vb@132.216.98.100
DTSTAMP:20250402T000218Z
DESCRIPTION:Overview:\n\nIn this workshop\, we will delve into the art of c
 onducting Exploratory Data Analysis (EDA) on a given dataset. EDA encompas
 ses a broad spectrum of critical data analysis components\, which include\
 , but are not restricted to\, the following:\n\n\n	Data Preprocessing: This
  encompasses activities such as data cleaning\, summarization\, and wrangl
 ing\, ensuring that the dataset is in a usable and informative state.\n	Dat
 a Visualization: EDA entails univariate\, bivariate\, and multivariate ana
 lyses\, employing various visualization techniques to unveil underlying pa
 tterns and relationships within the data.\n	Hypothesis Formulation: EDA aid
 s in the generation of hypotheses\, setting the stage for further testing 
 and validation through statistical techniques.\n	Time Series Analysis: For 
 datasets with temporal aspects\, EDA includes the examination of trends\, 
 seasonality\, and patterns over time\, providing insights into data evolut
 ion.\n	Feature Selection: Identifying and selecting the most relevant featu
 res is a crucial step in EDA\, as it can significantly impact the success 
 of subsequent analyses and modeling.\n\n\nBy the end of this workshop\, st
 udents will have a solid foundation in conducting EDA\, a vital skill in t
 he realm of data analysis and decision-making.\n\nPrerequisites:\n\n- Intr
 oductory knowledge of Python\n\n-You need to bring your own laptop for thi
 s workshop. Install Anaconda on your computer. You can find installation i
 nstructions here. Please contact us (cdsi.science at mcgill.ca) if you are
  having trouble with installation.\n\nInstructor: Kiwon Lee\, Faculty Lect
 urer\, Department of Mathematics & Statistics\n\nRegistration: Register He
 re\n
DTSTART:20241113T150000Z
DTEND:20241113T170000Z
SUMMARY:Workshop: Exploratory Data Analysis in Python
URL:/cdsi/channels/event/workshop-exploratory-data-ana
 lysis-python-360400
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