Python Practical Course for Data Analysis, Big Data and High-Performance Computing
This three-day course introduces advanced topics and state-of-the-art tools in the Python programming language for Big Data and High-Performance Computing. The course is intended for users who are comfortable in Python and who want to break through the speed and memory limitations of their Python code to make use of native CPU performance and multiple processor cores.
- Learn data analysis with Python
- Learn Python techniques for high performance computing
- Learn efficient Python best practices for Big Data
- Learn parallelization with multiprocessing
- How to use Python's native C libraries
This special Python course for high performance computing teaches you step by step and in a practical way the best practices for efficient Python programming. You will learn the best practices from NumPy, Numba, Cython, Dask and also how to call native C libraries with Python and thus realize high performance computing.
The course covers a selection of the following topics:
- NumPy Array
- Large data sets
- Speending up array
- Parallelising code
- OpenMP Threads
- Using native C Libraries
- Jupiter Noteebooks
For more details, please see the agenda below.
The course is intended for Python programmers and data analysts. A good understanding of programming in Python is required. Ideally, previous knowledge of Python comparable to our Python for programmers course is already available.
Individual: we specifically address your needs and take into account your previous knowledge, desired topics and focal points
Structured and easy to understand
Take your career, studies or training to the next level: with certificate
Safely and independently develop programmes (whether private, professional or for your start-up)
Lots and lots of practice: immediately applicable results
Small groups: max. 8 participants in the 3-day course max. 12 participants in online coaching
Developed by experts according to the Raed Method® & geared to the requirements of tech companies in 2020
E-mail support even after the end of the course
The participants get instructive problems to solve using the newly learned techniques.
- Processing large data sets with NumPy Arrays
- Speeding up array computations with Numba
- Compiling Python to fast native code with Cython
- Parallelising code with Dask, multiprocessing, or native OpenMP threads
- Using native C libraries efficiently from Python
- Interactively developing and exploring Python code in Jupyter Notebooks
The Python course for High Performance Computing will be conducted by one of the following trainers:
Dr. Stefan Behnel
Expert: Python, Pytest, Unit Test und TDD, Clean Code, Clean Software Architektur, Fast Python, Cython
- Doctorate at the TU Darmstadt as Dr. Ing. in Software Architecture
References: 15 years of experience as a consultant, software developer and software architect in the financial services, automotive, publishing and tourism industries in the field of high-performance Python and open source, main developer of Cython, the data science library PANDA is based on Cython. Python training for Draeger, Apple, Sky Deutschland, IT companies, ...
Dr. Matthias Hölzl
- Doctorate at LMU in the field of Software Engineering
References: 30 years of teaching and industrial experience. Of which 18 years at Ludwig-Maximilians-Universität Munich, most recently as Professor for Software and Computational Systems Engineering. Training, technical coaching for machine learning, deep learning, process automation as well as review and improvement of software architecture in large IT projects. Python and Java trainings for Deutsche Bank, BMW, BA, VKB, etc. Editor and author of several books at Springer-Verlag and author of numerous scientific publications.
Expert: Java, Python, Clean Code, Clean SW-Architecture, Refactoring, Testing, Train The Trainer
- Doctorate at LMU in the field of programming languages (2022).
References: 17 years of teaching and industry experience, thereof 12 years lecturer at the Ludwig-Maximilians-University Munich for Java, Python, Efficient Algorithms, Multiple awardsfor outstanding teaching at the LMU, book author for Java & soon Python at Springer and Orelly Verlag, developer of the RAED-Teaching Method®, Train the Trainer instructor, team training in Java and Python for BMW, VW, BA, SIEMENS, AGFA-Healthcare, TÜV Süd, Schufa AG, ..
Prof. Dr. Peer Kröger
Expert: Artificial Intelligence, Data Science, Big Data, SQL/NoSQL Database, Python, Java
- Doctorate at LMU in the field of Database and Data Science
References: Many years of practical experience in the implementation of data science projects as well as in consulting and training in the automotive industry, financial service providers and SMEs, among others. Approx. 150 peer-reviewed publications (cited over 8000 times) on the topic of data science, data mining, machine learning and AI. Member of the AI competence centre Munich Center for Machine Learning (MCML) at LMU Munich and professor for information systems and data mining at CAU Kiel.
Of course, you will receive a certificate as a participant in the Python course for High Performance Computing. The prerequisite for this is the complete participation in all course units and programming tasks and the successful programming of a small final project. This, however, will give you more pleasure than stress after this intensive Python course.