- Basic Python and data analysis skills
Audience
- Python developer
- Data analysts
Python is a versatile programming language known for its simplicity and readability. Pandas is a Python package that provides data structures for working with structured (tabular, multidimensional, potentially heterogeneous) and time series data. Numpy provides fundamental support for numerical computing with its array operations. Together, they form a robust ecosystem for efficient data handling and analysis in Python.
This instructor-led, live training (online or onsite) is aimed at intermediate-level Python developers and data analysts who wish to enhance their skills in data analysis and manipulation using Pandas and NumPy.
By the end of this training, participants will be able to:
- Set up a development environment that includes Python, Pandas, and NumPy.
- Create a data analysis application using Pandas and NumPy.
- Perform advanced data wrangling, sorting, and filtering operations.
- Conduct aggregate operations and analyze time series data.
- Visualize data using Matplotlib and other visualization libraries.
- Debug and optimize their data analysis code.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Dzień 1
Wizualizacja danych
- Rysowanie diagramów za pomocą matplotlib
- Używanie matplotlib z poziomu Pandas
- Tworzenie diagramów jakości
- Wizualizacja danych w notatnikach Jupyter
- Inne biblioteki wizualizacji w Python
Data Analysis with Pandas
- Używanie wektorowych danych w Pandas
- Przekształcanie danych
- Sortowanie i filtrowanie danych
- Operacje zbiorcze
- Analiza szeregów czasowych
Wektoryzacja danych w Numpy
- Tworzenie tablic Numpy
- Typowe operacje na macierzach
- Używanie ufuncs
- Widoki i transmisja na tablicach Numpy
- Optymalizacja wydajności poprzez unikanie pętli
- Optymalizacja wydajności za pomocą cProfile
Dzień 2
Inne biblioteki Python do analizy danych
- scikit-learn
- SciPy
- statsmodel
- RPy2
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