Intensive Python Course for Beginners
About
Target group
- The course is designed for those who want to gain theoretical and practical skills in applied programming and application development in the Python programming language, and are also considering the possibility of a career as a software development engineer.
Learning outcome
- 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
Training methods
- 105 academical hours (9 weeks)
Course information
Time of conduct
8 weeks
Course length
Training takes place in the center of Tallinn at Tartu mnt. 18. All educational materials are included in the course price. A laptop is provided
Format and place of conduct:span>
english
Training language
1,995.00 EUR + VAT
Price
105 ак. ч.
Total course volume
Course program
| Module | Main topics | Volume |
|---|---|---|
| 1. Getting familiar with Python | Why it is needed to use Python? Strengths and weaknesses of Python | 2 ac. hours |
| 2. Getting started with Python | Python installation. Standard interactive mode and IDE. Using Python shell window in IDLE. | 2 ac. hours |
| 3. Short review of Python | Python general description. Builtin datatypes. Control structures. Creating a module. Object-oriented programming. | 4 ac. hours |
| 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 ac. hours |
| 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 ac. hours |
| 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 ac. hours |
| 7. Dictionaries | Dictionary operations. Counting words. Using a key. Sparse matrices. Dictionary as a cache. Dictionary effectiveness. | 2 ac. hours |
| 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 ac. hours |
| 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 ac. hours |
| 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 ac. hours |
| 11. Python programs | Creating a simple program. Direct scripting in UNIX. Scripting in macOS. Windows scripting possibilities. Programs and modules. Python application spreading. | 4 ac. hours |
| 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 ac. hours |
| 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 ac. hours |
| 14. Working with exceptions | Exceptions in Python. Context managers and 'with' key word. Practical assignment: Advanced language features | 6 ac. hours |
| 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 ac. hours |
| 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 ac. hours |
| 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 ac. hours |
| 18. Packets | Packet samples. __all__ attribute. Correct use of packets. | 4 ac. hours |
| 19. Using Python libraries | Standard library. Installing additional Python libraries. Python libraries installation using PIP and venv. PyPI (CheeseShop). Practical assignment with data. | 6 ac. hours |
| 20. File data processing | Endless stream of data files. Scenario samples.Process organisation. Space saving: compress and delete | 4 ac. hours |
| 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 ac. hours |
| 22. Data transmission over the network. | Getting files. Getting data from API. Ctructured data formats. Web data extraction. | 4 ac. hours |
| 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 ac. hours |
| 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 ac. hours |
Information about training in this course
Requirements for students:
- secondary education
- confident PC user
- proficiency in English sufficient for reading technical documentation (approximately corresponding to A2/B1 level)
- It is desirable to have a personal laptop (Windows/Mac, 8 GB RAM, screen size > 13.3 inches); a laptop will be provided for the duration of the training if needed.
Evaluation criteria for learning outcomes:
- Learning outcomes are assessed based on independently completed practical work.
Evaluation methods:
- Upon successful completion, practical and homework assignments receive a "pass" grade.
Course completion conditions:
- To successfully complete the course and receive a certificate, it is necessary to achieve a "pass" grade on 75% of the homework assignments.
Additional information:
Ask a question:
training@gamma-intelligence.com
Call us:
(+372)55581521
Tutors
Roman Kutselepa
Gamma Intelligence OÜ lecturer
Qualification:
Over 5 years in software development. Specialization: web design, JavaScript development, effective use of software products in the company
Specialization:
web design, JavaScript development, effective use of software products in the company
Education:
Anglia Ruskin University 2010 (England)