Vorlesungsverzeichnis 

Vorlesung im Detail

Programming Course: Data Science with Python

Nummer
010562C, SS24
Dozentinnen und Dozenten
Veranstaltungstyp (SWS)
Kompaktkurs (3)
Ort und Zeit
  • M/CIP Fr 14:00 2h
  • The course is elective as a ``Programming Course`` for the MD 3 module (Data Science in Practice). ca. 3-4 hours per week (April-July)
Modul-Zugehörigkeit (ohne Gewähr)
  • DPL:A:-:-
  • DPL:F:-:1
Sprechstunde zur Veranstaltung
by agreement
Anmeldung?
ohne Angabe
Inhalt
The course is elective as a 'Programming Course' for the MD 3 module (Data Science in Practice) ca. 3-4 hours per week (October-February). Data Science with Python Course description This course will help you raise your level from a beginner’s to an advanced one using the programming language Python. You’ll be learning about libraries like NumPy, SciPy and Pandas and how to visualize your Dataset with Matplotlib and Seaborn. Not to mention learning about the many important statistical concepts in the Data Science Domain and it’s application using your knowledge about Python. Lastly you’ll learn about Machine learning using SciKit-Learn. Practical sessions will teach you how to apply your knowledge and you will get more and more confident in using it.
Course content:
* Introduction to jupyter notebook
* Basics of Python
** Variables and simple data types
** Lists
** Dictionaries
** Tuples
** Set and Booleans
** Comparison Operators

* Python Statements:
**If, elif and else Statements
** For Loops
** While loops

* Methods
* Functions
* Files and Exceptions
* Modules


Object Oriented Programming
Python for Data Science
** Numpy
** Pandas
** Scipy

Data Visualization:
** Matplotlib
** Seaboarn

Python Statistical analysis:
* Characterising
** Intrdouction to summary statistics
** Central Tendency
** Dispersion
** Joint Variability
** Empirical CDF
** Scikit-Learn:
* Dataset Preparation
* Machine Learning Algorithm
** Supervised Learning
** Unsupervised Learning
** Algorithm Evaluations
Bemerkungen
Link zu den Modulbeschreibungen im Service
Empfohlene Literatur
  • will be announced

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