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VERSION:2.0
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DTSTAMP:20260505T022928Z
DTSTART;VALUE=DATE:20250811
DTEND;VALUE=DATE:20250815
SUMMARY:Python – a crash course for central bankers
TRANSP:TRANSPARENT
UID:2025_07_21_phyton_for_data_science_940744
DESCRIPTION:Duration\n\n5 days\n\nApplication deadline\n\n25 July 2025\n\
 nObjective\n\nThis course provides an introduction to the application of
  Python for data science and machine learning. In recent years\, data sc
 ience and machine learning have had a significant impact on the way cent
 ral banks perform tasks such as analysing markets\, assessing risk\, ant
 i-money laundering and banking supervision. Python\, as one of the leadi
 ng programming languages for data science\, plays a crucial role in this
  area.\n\nIn this course\, participants will learn how to use Python to 
 analyse data\, create machine learning models and support business decis
 ions. A fairly basic introduction to Python will be provided\, and follo
 wing an introduction to the principles\, we will swiftly move on to the 
 application of Python in data science and machine learning. NumPy\, Pand
 as\, Scikit-learn and Matplotlib are the main libraries we will be using
  in the course\, with others playing a lesser role.\n\nProgramming tasks
  and small projects will be carried out in practical exercises\, indepen
 dently or in groups\, and participants will be encouraged to collaborate
  with others.\n\nAt the end of the course\, participants should be able 
 to use Python to analyse data\, build models and autonomously extend the
  knowledge they have gained so that it can be applied to tasks and issue
 s at their central banks. To this end\, they will also learn where to fi
 nd resources on the internet that will allow them to independently disco
 ver and apply further approaches and methods.\n\nContent\n\nIntroduction
  to Python\nBasics and fundamental concepts\nData structures in Python\n
 Data visualisation with Python\n\nData preparation and analysis\nData re
 trieval and cleaning\nDescriptive statistics in Python\n\nMachine learni
 ng\nRegression and classification\nArtificial neural networks\nMachine l
 earning for central bank tasks\n\nCase studies and projects\nPractical a
 pplication of data science at central banks\nProject work and programmin
 g exercises\n\nStarting on the second day\, there will be a Q&A slot bef
 ore every session\, during which participants will have the opportunity 
 to discuss the content of the previous day and the challenges they encou
 ntered with the practical exercises.\n\nTarget group\n\nThe course is de
 signed for staff working in information technology\, statistics or resea
 rch departments at central banks and regulatory and supervisory authorit
 ies with an interest in applying machine learning methods with Python. I
 t is not aimed at those who already have extensive and advanced knowledg
 e of Python.\n\nIndeed\, little prior knowledge is expected\, and the ne
 cessary Python skills are covered at the beginning\, making the course s
 uitable for previously inexperienced staff. It may be interesting for th
 ose who wish to switch from the programming language R to Python.\n\nPle
 ase note that we will not discuss the statistical background of the meth
 ods used in any great depth.\n\nTechnical requirements\n\nParticipants s
 hould be able to work on their own computers with an existing Python ins
 tallation (e.g. Anaconda). If necessary\, assistance with the installati
 on will be provided in advance. Two screens will make participation and 
 coursework much easier.
LOCATION:Online platform
CONTACT:Deutsche Bundesbank – CIC\, tzk@bundesbank.de\, +49 69 9566-36605
 
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