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DTSTAMP:20260429T164518Z
DTSTART;VALUE=DATE:20260802
DTEND;VALUE=DATE:20260807
SUMMARY:Python - a crash course for central bankers
TRANSP:TRANSPARENT
UID:2026_08_03_python_centralbankers_967722
DESCRIPTION:Objective\n\nThis course offers an introduction to the applic
 ation of Python for Data Science and Machine Learning. In recent years\,
  Data Science and Machine Learning have significantly influenced how cen
 tral banks perform tasks such as market analysis\, risk assessment and b
 anking supervision. Python\, as one of the leading programming languages
  for Data Science\, plays a pivotal role in these areas.\n\nParticipants
  will learn how to use Python to analyse data\, build machine learning m
 odels and support business decisions. The course begins with a basic int
 roduction to Python\, followed by a swift transition to its application 
 in Data Science and Machine Learning. The primary libraries covered incl
 ude NumPy\, Pandas\, Scikit-learn and Matplotlib\, with occasional use o
 f additional libraries.\n\nProgramming tasks and smaller projects will b
 e conducted through practical exercises\, either independently or in gro
 ups\, fostering collaboration among participants.\n\nBy the end of the c
 ourse\, participants will be equipped to use Python for data analysis\, 
 model building and extending their knowledge autonomously. This will ena
 ble them to apply their skills to tasks and challenges in their respecti
 ve central banks. Additionally\, they will learn how to access online re
 sources to independently explore and implement further approaches and me
 thods.\n\nContents\n\nIntroduction to Python\n\n\nBasics and fundamental
  concepts\n\n\nData structures in Python\n\n\nData visualisation with Py
 thon\n\nData preparation and analysis\n\n\nData retrieval and cleaning\n
 \n\nDescriptive statistics in Python\n\nMachine Learning\n\n\nRegression
  and Classification\n\n\nArtificial Neural Networks\n\n\nMachine learnin
 g for central bank tasks\n\nCase studies and projects\n\n\nPractical app
 lication of data science in central banks\n\n\nProject work and programm
 ing exercises\n\nStarting on the second day\, each session will begin wi
 th a Q&A slot\, providing participants with the opportunity to discuss t
 he previous day's content and address any challenges encountered during 
 the practical exercises.\n\nTarget group\n\nThe course is designed for s
 taff working in information technology\, statistics\, or research depart
 ments within central banks\, as well as regulatory and supervisory autho
 rities with an interest in applying machine learning methods with Python
 . It is not intended for individuals with extensive or advanced Python k
 nowledge.\n\nMinimal prior knowledge is required\, as the necessary Pyth
 on skills will be covered at the start of the course\, making it suitabl
 e for participants with little to no experience. The course may also app
 eal to those looking to transition from the programming language R to Py
 thon.\n\nPlease note that the course will not delve deeply into the stat
 istical foundations of the methods used.\n\nTechnical requirements\nComp
 uter with microphone\, camera\, speakers\, or headphones\nAn up-to-date 
 internet browser.\n\nParticipants should have access to their own comput
 ers with Python pre-installed (e.g.\, via Anaconda). Assistance with ins
 tallation will be provided in advance if needed. Using two screens is hi
 ghly recommended to facilitate participation and coursework.
LOCATION:Online platform
CONTACT:Deutsche Bundesbank – CIC\, tzk@bundesbank.de\, +49 69 9566-36605
 
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