Materialsammlung
Ziele
Data Science: wir erstellen eine Gesamtschau, was es in DS für Entwicklungen gibt; insbesondere durch Sammlung und Bewertung relevanter Wissens-Ressourcen in einem Wiki
besonderes Interesse JB: DS und Semantic Web, Ontologien. Aber wie bekommt man das unter? Das scheint seit 10 Jahren nicht zu fliegen.
Adressaten des Status Quo
- Ziel: Curriculum für Master-Kurs "Wissensmanagement im Betrieb" ab WS 2018
- Experten-Workshop Dagstuhl; Teilnehmer des Workshops: Autoren des Papiers "Was ist Ontologie?"
Kaggle-Datensätze
https://www.kaggle.com/c/titanic
- https://www.kaggle.com/c/titanic#tutorials
- dazu den Kernel https://www.kaggle.com/startupsci/titanic-data-science-solutions forken?
https://www.kaggle.com/kaggle/kaggle-survey-2017
- Ergebnisse einer Umfrage, die Kaggle vor ein paar Monaten an seine Nutzer gemacht hat: "The survey received over 16,000 responses and we learned a ton about who is working with data, what’s happening at the cutting edge of machine learning across industries, and how new data scientists can best break into the field".
- https://www.kaggle.com/kaggle/kaggle-survey-2017/kernels
bestehende Curriulua DS
Komplettkurs in Berkley
https://www.codecademy.com/learn/learn-python (neue Kursversion ab 13.3.2018)
- https://www.kaggle.com/learn/overview : we highly recommend the Learn Python sequence at Codecademy. Sections 1-8 on their syllabus will prepare you for the Kaggle Machine Learning sequence. They have paid content, which is listed as 'pro'. You don't need that material to do the Learn Machine Learning series on Kaggle.
(Daten-) Quellen
Übersichten und Sammlungen
- https://medium.freecodecamp.org/the-best-data-science-courses-on-the-internet-ranked-by-your-reviews-6dc5b910ea40
Kaggle didaktische Datensätze
- https://www.kaggle.com/c/titanic
- https://www.kaggle.com/unsdsn/world-happiness
- World Happiness Report
- Happiness scored according to economic production, social support, etc.
Kaggle education
- https://www.kaggle.com/jbusse/an-interactive-data-science-tutorial/edit?unified=1
- basierend auf: https://www.kaggle.com/helgejo/an-interactive-data-science-tutorial?scriptVersionId=775411
- https://www.kaggle.com/sohier/whirlwind-tour-of-python-index
- https://www.kaggle.com/crawford/humble-intro-to-analysis-with-pandas-and-seaborn/
- https://www.kaggle.com/kanncaa1/data-sciencetutorial-for-beginners
- Intro in Python Programmierung
- https://www.kaggle.com/ldfreeman3/a-data-science-framework-to-achieve-99-accuracy
- https://www.kaggle.com/jeffd23/scikit-learn-ml-from-start-to-finish
weitere einzelne Quellen
- https://www.datacamp.com/courses/intro-to-python-for-data-science/
- https://pandas.pydata.org/pandas-docs/stable/
- pandas is built on top of NumPy
- Note: This documentation assumes general familiarity with NumPy. If you haven’t used NumPy much or at all, do invest some time in learning about NumPy first.
- https://docs.scipy.org/doc/numpy/user/quickstart.html
- https://pandas.pydata.org/pandas-docs/stable/ecosystem.html
- https://developers.google.com/machine-learning/crash-course/
weitere Datensätze
- https://www.kaggle.com/c/donorschoose-application-screening
- Acknowledgments: Machine Learning Crash Course was created by Google's engineering education team in partnership with numerous Machine Learning subject matter experts across Google.
Berufsbilder
Data Steward
Data Analyst
etc.