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Key Skills: Mastery in machine learning frameworks like PyTorch or TensorFlow is essential, along with a solid foundation in unsupervised learning methods. Stanford AI Lab recommends proficiency in deeplearning, especially if working in experimental or cutting-edge areas.
Unsupervised Learning: Finding patterns or insights from unlabeled data. DeepLearning: Neural networks with multiple layers used for complex pattern recognition tasks. Tools and Technologies Python/R: Popular programming languages for data analysis and machine learning.
Skills and Tools of Data Scientists To excel in the field of Data Science, professionals need a diverse skill set, including: Programming Languages: Python, R, SQL, etc. Machine Learning: Supervised and unsupervised learning techniques, deeplearning, etc. Big Data Technologies: Hadoop, Spark, etc.
Unsupervised Learning Exploring clustering techniques like k-means and hierarchical clustering, along with dimensionality reduction methods such as PCA (Principal Component Analysis). Students should understand how to identify patterns in unlabeled data. Students should learn about neural networks and their architecture.
I contributed by providing data insights, developing predictive models, and presenting findings, ultimately leading to more targeted marketing strategies and increased customer engagement. DataGovernance and Ethics Questions What is datagovernance, and why is it important? Access to IBM Cloud Lite account.
Von Big Data über Data Science zu AI Einer der Gründe, warum Big Data insbesondere nach der Euphorie wieder aus der Diskussion verschwand, war der Leitspruch “S**t in, s**t out” und die Kernaussage, dass Daten in großen Mengen nicht viel wert seien, wenn die Datenqualität nicht stimme.
Eine bessere Idee ist es daher, Event Logs nicht in einzelnen Process Mining Tools aufzubereiten, sondern zentral in einem dafür vorgesehenen Data Warehouse zu erstellen, zu katalogisieren und darüber auch die grundsätzliche DataGovernance abzusichern.
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