DevOps Artisan – Data Science in Python Fundamentals

Cui i se adresează?

Cursul DevOps Artisan – Data Science in Python Fundamentals se adresează începătorilor în domeniul Data Science.

Ce vei învăța?

În cadrul acestui curs, participanții vor învăța cum să configureze mediul, cum să scrie primele linii de cod in Python folosind biblioteci numerice și tehnici de vizualizare a datelor. Acest curs poate fi văzut ca fiind o sursa de idei de sine stătătoare sau un pas spre Learning Path-ul de Machine Learning.

Cerințe preliminare:

Pentru a putea participa în cadrul acestui curs nu sunt necesare cerințe preliminare.

Agenda cursului:

Materialele de curs sunt în limba Engleză. Predarea se face în limba Română.

Module 1: Setting up

Jupyter Notebook
Jupyter Notebook magic
Google Colab

Module 2: Basic Python Syntax

Variables
Strings
Arithmetic Operators
If statement
Hands-on Lab: Writing a “Hello World” program, working with all the Python basic data types, using basic module operations, getting familiar with input and output
Functions
Lists
Dictionaries
Tuples
For and While statements
Hands-on Lab: Writing our first Python function, understanding the similarities and differences between the different kinds of collections, using the specific Python syntax for doing collection search and iteration.
Modules
Error handling
Exceptions
Hands-on Lab: Learning how to import modules in different ways, how to create our own module, how to do exception handling an elegant way and how to declare and raise our own custom exceptions.

Module 3: Basic Python OOP

Classes and Objects
Attributes and Methods
Abstraction and Encapsulation
Inheritance
Polymorphism
Hands-on Lab: Writing our first Python class, getting familiar with dunders, creating a basic class hierarchy; learning the difference between class aggregation and inheritance and some use cases for each; learning the
Python philosophy around private class attributes and methods

Module 4: Data Visualization

Matplotlib
Grids
Formatting Axes
Bar Plot
Histograms
Pie Chart
Scatter Plot
Contour Plot
Box Plot
Violin Plot
Hands-on Lab: Ploting basic polynomes and mathematical functions, playing with the number of discrete points in the plot, modifying different style and color parameters, finishing up the plot with labels and a legend and exporting it to different formats.

Module 5: Pandas

Series
DataFrame
Indexing
Sorting
Aggregations
Hands-on Lab: Getting some experience in declaring and working with Series and DataFrames, reading DataFrames from real files, converting numpy arrays to Series and back, Indexing by boolean indexes
GroupBy
Missing Data
Merging and Joining
Concatenation
Visualization
Hands-on Lab: Learning different basic methods of data cleaning and preprocessing. Getting some statistical measurements of the data. Trying to correlate data from multiple DataFrames.

Module 6: NumPy

Data Types
Ndarray
Indexing
Slicing
Broadcasting
Hands-on Lab: Working out the syntax and conceptual differences between python lists and numpy arrays, converting from one to the other, analyzing the different kind of data types and array shapes, learning to think through problems in a vectorized way.
Binary Operators
Mathematical Functions
Statistical Functions
Basic Linear Algebra
Hands-on Lab: Learning how to apply functions and operators directly on vectors, computing some classical statistical aggregates efficiently, trying to index and apply operations on multiple vectors in the easiest way
Project: Preprocessing Pipeline for Machine Learning Algorithms