International Certification Program Software Programmer Fundamentals + Python PDA

Topics

1.1. Algorithm concepts and their importance

- Definition of algorithms.

- Structure of an algorithm: Input, Process, and Output.

- Types of algorithms: iterative, recursive, and algorithmic.

- Examples of algorithms in real life.

1.2. Computational thinking and problem solving

- Breaking down problems into smaller steps.

- Pattern recognition and abstraction.

- Solution structure: Development and optimization.

- Designing efficient algorithms.

1.3. Practice: Solving simple problems with pseudocode

- Modeling real problems with pseudocode.

- Conversions between pseudocode and flowcharts.

- Practical exercise: Developing pseudocode to solve a mathematical problem.

2.1. Use case diagrams

- Identification of actors.

- Main and alternative scenario diagrams.

- Extended and general use cases.

2.2. Sequence diagrams

- Representation of the interaction between objects and events.

- Modeling of data flow and message sequence.

2.3. Activity diagrams

- Representation of activities, decisions, and alternative flows.

- Activity diagrams with partitions (swimlanes).

3.1. Data types and operators

- Primitive data types: integers, real numbers, Booleans, text strings.

- Arithmetic, logical, comparison, assignment, and bitwise operators.

- Data type conversion (casting).

3.2. Conditional control structures and loops

- Conditional statements: if, else, elif, switch.

- Counter-controlled loops: for and while.

- Condition-controlled loops: do-while.

- Concepts of recursion vs. iteration.

3.3. Declaration and use of functions

- Return and non-return functions.

- Parameters by value and by reference.

- Nested and recursive functions.

- Use of lambda functions. 3.4. Practice: Problem solving with functions - Functional decomposition exercises for problem solving. - Code modularization to improve reuse.

4.1. Lists, tuples, dictionaries, and sets

- Basic operations on data structures: creation, updating, deletion.

- Advanced methods and operations: append, pop, sort, reverse.

- Dictionaries and key-value pair management.

4.2. Sorting algorithms (bubble, selection)

- Step-by-step implementation of sorting algorithms.

- Time and space complexity: O(n^2), O(n log n).

- Comparison between sorting algorithms: efficiency and appropriate use.

4.3. Search algorithms (binary, linear)

- Implementation of sequential search and binary search.

- Conditions necessary for using binary search.

- Comparison of complexities.

4.4. Practice: Implementation of algorithms in Python

- Search and sorting exercises with lists and dictionaries.

- Search code optimization for large volumes of data.

5.1. Basic concepts of objects and classes
 - Definition of objects, classes, attributes, and methods.
 - Encapsulation, abstraction, and modularity.
5.2. Creating objects and methods
 - Creating and using objects in Python.
 - Public, private, and protected methods.
5.3. Inheritance and polymorphism
 - Concept of inheritance and class reusability.
 - Overridden methods and polymorphism.
5.4. Practice: Modeling objects and their interaction
 - Practical exercise for modeling entities using OOP.

6.1. Types of databases (relational and non-relational)

- Key differences between relational and non-relational databases.

- Examples of relational databases (MySQL, PostgreSQL) and non-relational databases (MongoDB).

- When to use a relational database vs. a non-relational database.

6.2. Database components: Tables, rows, columns

- Defining and creating tables.

- Relationships between tables: one-to-one, one-to-many, and many-to-many.

- Data types in SQL: VARCHAR, INT, DATE, etc.

7.1. Entity-relationship diagrams
 - Definition of entities, attributes, and relationships.
 - Creation of ER diagrams to represent data models.
7.2. Primary and foreign keys
 - Definition of primary keys and how they guarantee the uniqueness of records.
 - Foreign keys and their importance in linking tables.
7.3. Database normalization
 - Concept of normalization and its levels (1NF, 2NF, 3NF).
 - Practical exercises to avoid redundancy in databases.

8.1. Creating tables and relationships

- Basic SQL syntax: CREATE TABLE, ALTER TABLE.

- Defining constraints (PRIMARY KEY, UNIQUE, NOT NULL).

8.2. SQL queries: SELECT, INSERT, UPDATE, DELETE

- Read queries: SELECT with filters, operators, and sorting.

- Data insertion, modification, and deletion queries: INSERT INTO, UPDATE, DELETE.

8.3. Aggregation functions: COUNT, SUM, AVG

- Use of functions to calculate aggregates on data sets.

- Data grouping with GROUP BY and filtering with HAVING.

Topics

- What is Python?

- Variables and basic data types

- Reading keyboard input

- Departures

- Ticket collection

- If statement

- Else and elif statements

- And and or operators

- Strings in Python

- String methods in Python

- String formatting in Python

- Operators

- Numbers

- Converting strings to numbers and using absolute values

- Presenting lists

- Working with numbers in lists

- Manipulating list data

- While
- For
- Dictionaries
- Dynamic programming with dictionaries

- Functions

- Function arguments

- Keyword arguments

- Variable arguments

-Lambdas

-Regular expressions

-Data comprehension

- Classes and objects

- Class constructor

- Attributes

- Inheritance

- Encapsulation

- Polymorphism

- How to find errors

- Checking exceptions

- Generating exceptions

- Working with the file system

- Directories and files

- Creating a file, managing files