Python course for beginners

Tallinn:

27.05.2024 - 29.07.2024

The focus of the Python course for beginners to quickly learn how to use Python for solving practical tasks.

After the course, you will learn how to quickly develop applications with Python programming language using modern standards and algorithms (Python 3 - PEP8). Knowledge achieved during the course can be used to automate computer daily tasks, collection of data, and data analysis.

The course is held in the centre of Tallinn, Tartu mnt. 18. All learning materials are included in the price. If needed a laptop can be provided for the length of the course.

You can register yourself for the course in the following ways:

By sending registration application to training@gamma-intelligence.com (name, surname, contact phone number, email, name of the course, studying period and time)

By phone +(372)55581521

Course description:

Target group: everyone, who would like to automate everyday tasks, programm for data analytics or e-document flow specialists.

Teacher: Roman Kutselepa

Course duration: 8 weeks

Language: English

Participants: up to 8 people

Course volume: 105 academical hours (9 weeks)

Price: 1995 EUR + VAT

Participation requirements:

secondary education

confident PC user

English B1/B2

It is desirable to have own PC (Windows). If needed a laptop can be provided for the length of the course.

Skills:

After completing the course you will learn:

how to quickly develop applications with Python programming language using modern standards and algorithms (Python 3 - PEP8)

how use achieved skills for computer daily tasks automation

how to use Python programming language for data collection and analysis

how to create application using Python standard libraries

Additional information:

General rules of training organisation (in Estonian)
General rules of providing training process quality (in Estonian)

Module	Module content	Duration
1. Getting familiar with Python	Why it is needed to use Python? Strengths and weaknesses of Python	2 ACH
2. Getting started with Python	Python installation. Standard interactive mode and IDE. Using Python shell window in IDLE.	2 ACH
3. Short review of Python	Python general description. Builtin datatypes. Control structures. Creating a module. Object-oriented programming.	4 ACH
4. Basics	Indentation and decoration of blocks. Comments. Variables and assignment. Expressions. Strings. Numbers. None value. Getting data from a user. Builtin operators. General programming style in Python.	4 ACH
5. Lists, tuples and sets	List and array similarities. List indexes. List modification. List sorting. Other widespread list operations. Nested lists and deep copying. Tuples. Sets.	4 ACH
6. Strings	String as symbol sequences. General string operations. Special symbols and sequence screening. String methods. Transformation of objects into strings. Using the format method. Formating strings using a % symbol. String interpolation. Byte strings.	4 ACH
7. Dictionaries	Dictionary operations. Counting words. Using a key. Sparse matrices. Dictionary as a cache. Dictionary effectiveness.	2 ACH
8. Control structures	While cycle. if-elif-else command. For cycle. String and dictionary generator. Commands, blocks, and indents. Logical values and expressions. Practical assignment: creating simple application for text file analysis.	6 ACH
9. Functions	General function definitions. Function parameters. Changeable objects as arguments. Local, nonlocal, and global variables. Assigning functions to variables. Lambda function. Generator functions. Decorators.	4 ACH
10. Modules and visibility area rules	Module definition. First module. Import command. Module searching path. Private names inside modules. Libraries and third-party modules. Area of visibility rules and name spaces in Python.	4 ACH
11. Python programs	Creating a simple program. Direct scripting in UNIX. Scripting in macOS. Windows scripting possibilities. Programs and modules. Python application spreading.	4 ACH
12. Working with file system	os and os.path versus pathlib. Paths and names. Getting information about files. File system operations. Processing all files in catalog tree.	4 ACH
13. File reading and writing	Opening files and file objects. Closing files. Opening files for writing or other modes. File and binary data reading and writing functions. Reading and writing using pathlib. Screen input/output and redirections. Structured binary data reading using struct module. Specialization and pickle module. Shelve module.	4 ACH
14. Working with exceptions	Exceptions in Python. Context managers and 'with' key word. Practical assignment: Advanced language features	6 ACH
15. Object-oriented programming in Python	Class definition. Instance variables. Methods. Class variables. Static and class methods. Inheritance. Inheritance for class and instance variables. General class capabilities in Python. Private variables and private methods. Using @property to create more flexible instance variables. Area of visibility and namespace rules for class instances. Destructors and memory management. Numerous inheritance.	4 ACH
16. Regular expressions	Python regular expressions basics. Regular expressions with special symbols. Regular expressions and unformatted strings. Matched text extraction from strings. Text replacement using regular expressions.	4 ACH
17. Data types as objects	Using typification. Types and user classes. Special attribute method. Object behavior as a list. Special attribute item __getitem__. Full emulation of lists by objects. Subclassing integrated types. Using special attribute methods.	4 ACH
18. Packets	Packet samples. __all__ attribute. Correct use of packets.	4 ACH
19. Using Python libraries	Standard library. Installing additional Python libraries. Python libraries installation using PIP and venv. PyPI (CheeseShop). Practical assignment with data.	6 ACH
20. File data processing	Endless stream of data files. Scenario samples.Process organisation. Space saving: compress and delete	4 ACH
21. Data files processing	Getting familiar with the ETL concept. Reading text files. Excel files. Data cleaning. Writing file data. Sending data using a network.	4 ACH
22. Data transmission over the network.	Getting files. Getting data from API. Ctructured data formats. Web data extraction.	4 ACH
23. Storing data	Relational databases. SQLite: using SQLite 3 database. MySQL, PostgreSQL, and other relational databases. Simple database management with ORM. NoSQL databases. Keyword-value pair storing in Redis. Documents in MongoDB.	4 ACH
24. Data analysis in Python	Python standard tools for data analysis. Jupyter Notebook. Pandas. Data cleaning. Aggregation and transformation of data. Graphical presentation of data. Practical assignment.	6 ACH