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DTSTAMP:20260925T021328Z
DTSTART;VALUE=DATE:20270801
DTEND;VALUE=DATE:20270806
SUMMARY:Python – a Crash Course for Central Bankers
TRANSP:TRANSPARENT
UID:2027_08_02_python_crash_course_1007636
DESCRIPTION:Objective\n\nThis course offers an introduction to the applic
 ation of Python in Data Science and Machine Learning. In recent years\, 
 Data Science and Machine Learning have significantly influenced how cent
 ral banks perform tasks such as market analysis\, risk assessment\, and 
 banking supervision. Python\, as one of the leading programming language
 s for Data Science\, plays a pivotal role in these areas.\n\nParticipant
 s will learn how to use Python to analyse data\, build machine learning 
 models\, and support informed decision-making. The course begins with a 
 basic introduction to Python\, followed by a rapid transition to its app
 lication in Data Science and Machine Learning. The primary libraries cov
 ered include NumPy\, Pandas\, Scikit-learn\, and Matplotlib\, with occas
 ional use of additional libraries.\n\nProgramming tasks and smaller proj
 ects will be completed through practical exercises\, either independentl
 y or in groups\, fostering collaboration among participants.\n\nBy the e
 nd of the course\, participants will be equipped to use Python for data 
 analysis\, model building\, and to expand their knowledge independently.
  This will enable them to apply their skills to tasks and challenges in 
 their respective central banks. Additionally\, they will learn how to ac
 cess online resources to independently explore and implement further app
 roaches and methods.\n\nContents\n\nIntroduction to Python\n\nBasics and
  fundamental concepts\n\nData structures in Python\n\nData visualisation
  with Python\n\nData preparation and analysis\n\nData retrieval and clea
 ning\n\nDescriptive statistics in Python\n\nMachine Learning\n\nRegressi
 on and Classification\n\nArtificial Neural Networks\n\nMachine learning 
 for central bank tasks\n\n \n\nCase studies and projects\n\n- Practical 
 application of data science in central banks\n\n- Project work and progr
 amming exercises\n\n \n\nStarting on the second day\, each session will 
 begin with a Q&A slot\, providing participants with the opportunity to d
 iscuss the previous day's content and address any challenges encountered
  during the practical exercises.\n\n \n\nTarget group\n\n \n\nThe course
  is designed for staff working in information technology\, statistics\, 
 or research departments at central banks\, as well as at regulatory and 
 supervisory authorities\, who are interested in applying machine-learnin
 g methods using Python. It is not intended for participants with extensi
 ve or advanced Python experience.\n\n \n\nThe essential Python skills wi
 ll be introduced at the beginning of the course\, making it suitable for
  participants with little or no previous experience. The course may also
  be of interest to those wishing to transition from R to Python.\n\n \n\
 nPlease note that the course will not cover the statistical foundations 
 of the methods in depth.\n\n \n\nTechnical requirements\nComputer with m
 icrophone\, camera\, speakers\, or headphones\nAn up-to-date internet br
 owser.\n\nParticipants should have access to their own computers with Py
 thon pre-installed (e.g.\, via Anaconda). Assistance with installation w
 ill be provided in advance if needed. Using two screens is highly recomm
 ended to facilitate participation and coursework.
LOCATION:Online platform
CONTACT:Deutsche Bundesbank – CIC\, tzk@bundesbank.de\, +49 69 9566-36605
 
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