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DTSTAMP:20260505T060228Z
DTSTART;VALUE=DATE:20240826
DTEND;VALUE=DATE:20240830
SUMMARY:Python for Data Science – A crash course
TRANSP:TRANSPARENT
UID:2024_08_26_python_for_data_science_917798
DESCRIPTION:Deadline for application\n\n09 August 2024\n\nObjective\n\nTh
 is course provides an introduction to the application of Python for Data
  Science and Machine Learning. In recent years\, Data science and Machin
 e Learning have had a significant impact on the way Central Banks perfor
 m tasks such as analysing markets\, assessing risk\, anti-money launderi
 ng and banking supervision. Python\, as one of the leading programming l
 anguages for Data Science\, plays a crucial role in this area.\n\nIn thi
 s course\, participants will learn how to use Python to analyse data\, c
 reate machine learning models and support business decisions. A fairly b
 asic introduction to Python will be provided\, and following an introduc
 tion to the principles\, we will move on quite quickly to the applicatio
 n of Python for Data Science and Machine Learning. NumPy\, Pandas\, Scik
 it-learn and Matplotlib are the main libraries used in the course\, with
  others appearing  more sporadically. \n\nProgramming tasks and smaller 
 projects will be carried out in practical exercises\, independently or i
 n groups\, and participants will be encouraged to collaborate with other
 s.\n\nAt the end of the course\, participants should be able to use Pyth
 on to analyse data\, build models and autonomously extend the knowledge 
 gained so that it can be transferred to tasks and issues in their centra
 l banks. To this end\, they will also learn where to find resources on t
 he internet that will allow them to independently discover and apply fur
 ther approaches and methods.\n\nContents\n\nIntroduction to Python\nBasi
 cs and fundamental concepts\nData structures in Python\nData visualisati
 on with Python\n\nData preparation and analysis\nData retrieval and clea
 ning\nDescriptive statistics in Python\n\nMachine Learning\nRegression a
 nd Classification\nArtificial Neural Networks\nMachine learning for cent
 ral bank tasks\n\nCase studies and projects\nPractical application of da
 ta science in central banks\nProject work and programming exercises\n\nS
 tarting on the second day\, there will be a Q & A slot before every sess
 ion\, during which participants will have the opportunity to discuss the
  content of the previous day and the challenges they encountered with th
 e practical exercises.\n\nTarget group\n\nThe course is designed for sta
 ff working in information technology\, statistics or research department
 s in central banks and regulatory and supervisory authorities with an in
 terest in applying machine learning methods with Python. It is not aimed
  at those who already have extensive and advanced knowledge of Python.\n
 \nIndeed\, little prior knowledge is expected\, and the necessary Python
  skills are covered at the beginning\, making the course suitable for pr
 eviously inexperienced staff. It may be interesting for those who wish t
 o switch from the programming language R to Python. \n\nPlease note that
  we will not discuss the deeper statistical backgrounds of the methods u
 sed in any great depth.\n\nTechnical requirements\n\nComputer with micro
 phone\, camera\, speakers\, or headphones\; an up-to-date internet brows
 er.\n\nParticipants should be able to work on their own computers with a
 n existing Python installation (e.g. Anaconda). If necessary\, assistanc
 e with the installation will be given 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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