Python course for beginners
Eesti keeles Русский языкTallinn:
08.11.2024 - 10.01.202529.11.2024 - 03.02.2025
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:
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:
Skills:
After completing the course you will learn: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 |