Advanced Python

During the three-day course, students will learn advanced techniques of the Python programming language, object-oriented programming, parallel programming, and how to use Python for data analysis and processing.

The aim of the course is to acquire the skills needed to automate operational processes and to learn how to use Python for advanced data processing and data analysis.

We recommend the training to professionals who want to improve their Python skills, professionals who work with data and who want to learn about the data analysis and data processing techniques provided by Python.

Required background: python basics with experience.

• Python haladó ismeretek
  • Haladó objektum orientált programozás: dekorátor, többes öröklés
  • Haladó technikák használata: map, reduce, filter, itertools
  • Párhuzamos programozás: multiprocesszing, multithreading, aszinkron
  • HTTP kezelése
  • Hibakezelés, tesztelés
• Adatfeldolgozás
  • Fejlesztői eszközök áttekintése (Pandas, Jupyter notebook)
  • Források beszerzése: excel, web, adatbázis
  • BigData kezelés python alapon elérhető eszközökkel (numpy, pandas, scypi, sklearn, nltk)
  • Vizualizáció (Matplotlib, Seaboarn)
  • Adatok minőségének vizsgálata a megismert eszközök segítségével
  • Adattisztítás, bevezetés a feature engineeringbe
  • Accuracy, Consistency, Completeness
  • NLP: lowercasing, stopwords, punctuation, non-words, tokenizing, lemmatizing, stemming, url, html tags, abbreviations
  • ML: hiányzó adatok, kiugró értékek kezelése, Dropping columns

Syllabus in English

Advanced Python skills

• Advanced object-oriented programming: decorator, multiple inheritance
• Advanced techniques: map, reduce, filter, itertools
• Parallel programming: multiprocessing, multithreading, asynchronous
• Managing HTTP 
• Troubleshooting, testing

Data processing

• Developer tools overview (Pandas, Jupyter notebook)
• Obtaining sources: excel, web, database
• BigData management with python-based tools (numpy, pandas, scypi, sklearn, nltk)
• Visualization (Matplotlib, Seaboarn)
• Data cleaning, introduction to feature engineering
• Accuracy, Consistency, Completeness
• NLP: lowercasing, stopwords, punctuation, non-words, tokenizing, lemmatizing, stemming, url, html tags, abbreviations
• ML: missing data, handling of outliers, Dropping columns