Python for Data Science I – Beginner:
- Pendahuluan (1 Jam):
* The Zen of Python
* Python “distro” : Python, Anaconda/miniconda, WinPython, etc.
* Editor Jupyter & Spyder
* Python VS (R, Julia, Matlab, Java, C, PHP, etc)
* beberapa kelemahan & kelebihan Python
* Google Colab - Dasar Python (1 Jam)
* Syntax Format (indenting, multiline, import, deklarasi/inisialisasi)
* Code descriptor & Comments
* integer, float, Bytes, Boolean
* list, tuple, dictionary
* (Frozen) Set
* types : Beginner Pitfall
* Slicing in Python - Python Logic (3 Jam)
* (Nested – hierarchical) if Logic
* Looping For (& list comprehension)
* Iterator VS Iterable
* Looping while
* Breaking Loop
* Python Exception
* TQDM - Penggunaan & Instalasi Modul (1 Jam)
* Full and Partial Import
* Import all functions as first level implicitly
* Personal Library
* Conda/pip/easy_install
* Adding repository modul
* Automatic update all modules
* Pure Python vs compiled Modules
* Module’s Wheels
* Check modules dependency
* Installing from Source
* Installing modul from script - Going Deeper in Python (2 jam)
* Deeper with Print Function
* Reference/pointer to variable(s)
* Deeper with Python string
* List/Dictionary comprehension
* Zipping List
* List again : Kelebihan List di Python
* Optimal Python Data Type use case - Python Function (2 Jam)
* Fungsi di Python
* Global & local variable
* vars, dirs**
* Recursive in Python
* Lamda Function - Python as Numerical computing & (simple) Visualizations (2 Jam)
* Numpy Matrix:
* List VS Arrays/Matrix: best use scenarios, etc.
* Linear Algebra Functions
* Numpy Operations, etc.
* DataFrame Basics
* MatplotLib & Seaborn: Visualisasi dasar di Python
Scatter plot, histogram, barchart, boxplot, etc.
