VSC | Vlaams Supercomputer Centrum’s Post

📢 Upcoming Python training for scientist! 🏫 Next week, join the two hybrid training sessions organised by VSC | Vlaams Supercomputer Centrum. The Scientific Python course will cover a number of modules that are useful for data preparation, analyzing data, visualization, and machine learning. The following training to the use of Python in data sciences: Many high quality libraries are available as building blocks in a wide variety of scientific domains. Scientific Python 🗓️ When: 17 Mar 2026, 09:00 – 13:00 CET 🌍 Where: ICTS KU Leuven (hybrid), Willem de Croylaan 52/bus 5580, 3001 Leuven, Belgium ✅ Info&registration: https://lnkd.in/e3wBNBqS Python for data science 🗓️ When: 18 Mar 2026, 09:00 – 13:00 CET 🌍 Where: ICTS KU Leuven (hybrid), Willem de Croylaan 52/bus 5580, 3001 Leuven, Belgium ✅ Info&registration: https://lnkd.in/eUuNm_D8

Python for Data Science – VSC Training Python is one of the dominant languages in data science. This training covers a number of modules that are useful for data preparation, analyzing data, visualization, and machine learning. 📅 March 18, 2026 ⏰ 9:00 AM – 1:00 PM 📍 Hybrid (online or on-site at ICTS, KU Leuven, Heverlee) 👨🏫 Trainer: Geert Jan Bex During this session, participants will work with Python tools commonly used in data science workflows, including: • pandas – representing, transforming, and querying data • seaborn – data visualization • Parsing data and regular expressions • Machine learning with scikit-learn and keras This training is intended for participants who already have experience in Python programming. 🔗 Register here: https://lnkd.in/eUuNm_D8 Discover how Python tools can support data preparation, analysis, visualization, and machine learning in research. #DataScience #Python #MachineLearning #AI #DataAnalytics #hpc #supercomputer

Looking to build practical Python skills for data science, machine learning, and biological data analysis? PR Stats offers a series of Python training courses designed for researchers, analysts, and professionals who want to apply Python to real datasets using widely adopted tools and workflows. Our live online courses include: Advanced Python for Ecologists and Evolutionary Biologists (APYB01) https://lnkd.in/eeVj5rNg Python for Biological Data Exploration and Visualization (PYBD01) https://lnkd.in/e29PaSD3 Python for Data Science and Statistical Computing (PYDS01) https://lnkd.in/ej2czKVZ Deep Learning Using Python (DLUP01) https://lnkd.in/evAMq9Tk Machine Learning for Time Series (MLTP01) https://lnkd.in/eHuaQYJX We also offer recorded courses for flexible, self-paced learning: Introduction to Python for Ecologists and Evolutionary Biologists (IPYBPR) https://lnkd.in/e2XsS_TA Machine Learning using Python https://lnkd.in/erFAAmRA Machine Vision using Python https://lnkd.in/eqNkZYmn All courses are built around practical application, giving participants skills they can use directly in research and data-driven projects. For course enquiries, contact oliver@prstats.org #Python #DataScience #MachineLearning #DeepLearning #Bioinformatics #ScientificComputing

Any plans for the summer holiday? Maybe this is one to explore: https://lnkd.in/gK4zGQJY A great session delivered by Professor Stephen Lynch through the Institute of Mathematics and its Applications, highlighting the value of bringing programming into mathematics. It is about learning how to explore data, test ideas, build intuition, and equip ourselves for a world where mathematics, computing, and real-life problem-solving increasingly go together. Small steps in education can create impact across generations. #Mathematics #Python #IMA

💡 Teaching Python programming with DOAJ's journal dataset Christof Schöch from the University of Trier, Germany details how the DOAJ journal dataset is used at the University to teach Python programming for the Machine Learning in a Digital Humanities Master's program. Students learn practical skills like data cleaning, analysis, and classification. The real-world complexity of the dataset provides essential experience beyond simple "toy datasets" and serves to debunk common myths about open access. ✍ Guest Blog post: https://lnkd.in/gWcUjrTi #PythonProgramming #APCs #DOAJ #data #DataClassiication #DataCleaning #MachineLearning #dataset

The thing about open access, is that because you share things open, so much reuse can happen without you ever knowing about it. In a way, it is a little magical (I need to credit Brendan O. for this exact thought during a recent conversation - more on that in a later blog post😎). By a happy accident, I learned about Christof Schöch and how he is using DOAJ data for teaching students about complex datasets while busting some OA myths! And not only that, he has written a guest blog post for DOAJ, where he shares exactly how this is done 😄 Ps. The blog post is fantastic - fascinating and inspiring, I highly recommend. Thank you, Christof, for sharing your experience so openly 🤗

Applied My Python Learning to a Simple Data Project. As part of my learning journey with TechCrush I recently completed a beginner-friendly project: a Student Score Analyzer using Python. Using dictionaries and basic Python operations, I analyzed a dataset of student scores and derived the following insights: Average Score: 81.4 Highest Score: 92 Lowest Score: 67 This exercise highlighted an important principle in data science: Data only becomes valuable when it is properly structured, analyzed, and interpreted. What stood out to me was how a few lines of code can transform raw numbers into meaningful insights that can support decision-making—especially in an educational context. With my background in mathematics and teaching, this is an exciting step toward applying programming to real-world problem solving. Here’s a snippet of the code and output from the project 👇 #Python #DataScienceJourney #LearningByDoing #Mathematics #Analytics

New Instats livestreaming seminar: Plotting and Programming in Python Join our hands-on workshop on programming and plotting in Python for researchers—taught by The Carpentries and tailored for PhD students, postdocs, and research staff who work with tabular and longitudinal data. You’ll learn to set up a reliable scientific Python environment, wrangle and clean data with pandas, write reusable functions and modular code, apply plotting principles to produce publication-quality figures with Matplotlib/Seaborn, and adopt reproducible analysis workflows that reduce errors and accelerate manuscript-ready results; sessions blend conceptual depth with practical, Gapminder-based exercises so you leave with concrete, immediately applicable skills. Live seminars run via Zoom, with recordings, materials and a monitored Q&A forum available for 30 days afterward (on-demand access with the same 30-day materials window is also offered), and participants receive an Instats certificate of completion (ECTS equivalent shown for eligible European students). Spaces are limited—learn more and enroll today. #DataScience #ComputerScience #Education #Statistics #Python #Research #ResearchTraining #Instats

Certification Course on Applied Machine Learning with Python For II Semester Students. Resource Person: Name Niladri Roy Designation: Machine Learning Engineer at Analogica Day 1: Python for ML Recap & Foundations 6 Hours Focus:. Recall and strengthen Python skills required for Machine Learning practice. Content Coverage: ● Python recap: data types, loops, functions ● Working with files & CSVs ● numpy & pandas refresher ● Data cleaning & preprocessing basics ● Exploratory Data Analysis mindset ● ML workflow orientation #pythonforml #machinelearningbasics #pythonrecap #datasciencejourney #mlfoundations #learnpython #klebca #klebcakhanapur

25 must know algorithms for every programmer Learn programming and computer science https://lnkd.in/dPkQP6Bt Recommended courses Python for Everybody https://lnkd.in/dw3T2MpH CS50’s Introduction to Programming with Python https://lnkd.in/dkK-X9Vx Machine Learning by Andrew Ng https://lnkd.in/dmPtiWK8 Searching Linear Search Binary Search Depth First Search Breadth First Search Sorting Insertion Sort Heap Sort Selection Sort Merge Sort Quick Sort Counting Sort Graph algorithms Kruskal Algorithm Dijkstra Algorithm Bellman Ford Algorithm Floyd Warshall Algorithm Topological Sort Flood Fill Algorithm Lee Algorithm Array and string algorithms Kadane Algorithm Floyd Cycle Detection Algorithm Knuth Morris Pratt KMP Algorithm Quickselect Algorithm Boyer Moore Majority Vote Algorithm Core algorithms Huffman Coding Compression Algorithm Euclid Algorithm Union Find Disjoint Set Union Learning these algorithms improves problem solving and prepares you for technical interviews. #Algorithms #Programming #CodingInterview #ComputerScience #ProgrammingValley