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DataCorpo Python® Certifications
We have several sample tests to help you easily obtain your certification.
At DataCorpo, we are committed to offering top-tier certifications for professionals eager to enhance their programming and data analysis expertise. We provide two Python® certifications tailored to meet the diverse needs of aspiring data scientists and analysts.
DataCorpo Python® Standard Certification: This certification is designed for beginners and intermediate users of Python®. It covers the foundational concepts and essential features of the language, enabling you to write efficient code and automate tasks. By earning this certification, you will learn how to work with variables, data types, loops, and functions. This comprehensive training will provide you with a strong foundation in Python programming, crucial for any career in data science and software development.
DataCorpo Python® Data Analysis Certification: Aimed at data professionals and analysts who want to specialize in data analysis using Python®, this advanced certification guides you through specific techniques for data manipulation, visualization, and statistical analysis. You will explore libraries like Pandas, Matplotlib, and NumPy to analyze and visualize complex datasets. Building on the skills acquired with the standard certification, this training will enable you to perform data-driven analyses and make informed decisions based on your findings.
By certifying with DataCorpo, you validate your skills, enhance your professional credibility, and establish yourself as an expert in Python programming and data analysis. Join us and take a decisive step towards becoming a proficient Python programmer.
We offer two Python® certifications that cater to the diverse needs of the market: the DataCorpo Python® Standard Certification, which provides you with a comprehensive understanding of the core features of Python® and equips you with the essential skills to write efficient code and perform fundamental programming tasks; and the DataCorpo Python® Data Analysis Certification, which offers specialized training focused on applying Python® in data manipulation, analysis, and visualization, preparing you to turn data insights into actionable strategies.
Welcome to the Python course section at DataCorpo! Our courses are designed to help you master Python, a versatile programming language widely used in data analysis, software development, and scientific research. Whether you’re a beginner or looking to advance your skills, our curriculum covers everything from basic installation and syntax to advanced data manipulation, analysis, and automation techniques. Join us to unlock the full potential of Python and enhance your programming and data analysis capabilities.
DataCorpo Python® Courses Program
1. Introduction to Python
Objectives:
– Understand the fundamentals of Python: its origin, evolution, characteristics, and advantages.
– Install and configure Python.
– Write and execute programs in Python.
– Master the basic concepts of Python.
1.1. Overview of Python
– History of Python: Evolution of major versions.
– Key Features of Python.
– Applications of Python and Comparison with Other Languages.
1.2. Installation of Python and Environment Setup
– Downloading, Installing, and Verifying Python Installation.
– Python IDEs (PyCharm, Visual Studio Code, and Jupyter Notebook).
– Using the Python Interpreter.
1.3. Writing and Executing the First Program
– Writing a Simple Script with Python.
– Executing the Script via the Terminal using Python.
1.4. Introduction to Variables and Data Types
– Variables in Python: Declaration, Initialization, and Naming Rules.
– Data Types and Type Conversion in Python.
– Basic Operations: Arithmetic Operations and String Operations in Python.
2. Basic Syntax and Control Structures
Objectives:
– Understand and use fundamental Python syntax.
– Manipulate variables and understand basic data types in Python.
– Use arithmetic, logical, and comparison operators in Python.
– Implement conditional structures in Python.
– Utilize loops to repeat code blocks in Python.
– Apply list comprehensions in Python.
2.1. Basic Syntax in Python
– Indentation: (spaces or tabs) in Python.
– Single-line and multi-line comments in Python.
– Code Lines in Python.
2.2. Variables and Data Types in Python
– Variables: Declaration, initialization, and naming rules in Python.
– Data Types and Type Conversion in Python.
2.3 Operators in Python
– Arithmetic Operators in Python.
– Comparison Operators in Python.
– Logical Operators in Python.
– Assignment Operators in Python.
2.4. Conditional Structures in Python
– `if` Statement: Basic syntax in Python.
– `else` and `elif` Statements: Adding alternative conditions in Python.
2.5. Loops in Python
– `for` Loop in Python.
– `while` Loop in Python.
– Loop Control: `break`, `continue`, and `else` with `for` and `while` in Python.
2.6. List Comprehensions in Python
– Basic Syntax: Simple list comprehension in Python.
– Practical Examples: Filtering even numbers in a list in Python.
3. Functions and Modules in Python
Objectives:
– Understand the concept of functions in Python.
– Create and use functions to structure Python code.
– Manage parameters, return values, and exceptions in Python functions.
– Utilize and import standard and custom modules in Python.
– Document functions and organize modules into packages in Python.
3.1. Introduction to Functions in Python
– Basic syntax for defining a function in Python
– Function calls in Python
– Parameters and Arguments in Python
3.2. Parameters and Return Values in Python
– Positional Parameters in Python
– Named Parameters in Python
– Default Parameter Values in Python
– Variable Arguments in Python
3.3. Modules and Imports in Python
– What is a Module in Python?
– Importing Modules in Python
– Standard Modules in Python
– Creating Custom Modules in Python
3.4. Packages in Python
– Structure of a Package in Python
– Importing Packages in Python
– Using `__init__.py` in Python
3.5. Error Handling in Functions
– Exception Handling in Python
– Raising Custom Exceptions with `raise` in Python
3.6. Function Documentation in Python
– Using Documentation Strings in Python
– Docstring Syntax in Python
– Accessing Function Documentation in Python
4. Data Structures in Python
Objectives:
– Understand data structures in Python.
– Use lists, tuples, sets, and dictionaries in Python.
– Create data structures with comprehensions in Python.
– Manipulate immutable and unique elements in Python.
– Explore nested structures like lists and dictionaries in Python.
4.1. Introduction to Data Structures in Python
– Definition of data structures in Python.
– Importance of data structures in Python.
4.2. Lists in Python
– Definition and creation of lists in Python.
– Accessing list elements in Python.
– Manipulating lists in Python.
– Using slicing in Python.
4.3. Tuples in Python
– Definition and creation of tuples in Python.
– Accessing tuple elements in Python.
– Properties of tuples in Python.
4.4. Sets in Python
– Definition and creation of sets in Python.
– Manipulating sets in Python.
4.5. Dictionaries in Python
– Definition and creation of dictionaries in Python.
– Accessing dictionary elements in Python.
– Manipulating dictionaries in Python.
4.6. Advanced Data Structures in Python
– Nested lists in Python.
– Nested dictionaries in Python.
4.7. Comprehensions for Lists, Sets, and Dictionaries in Python
– List comprehensions in Python.
– Set comprehensions in Python.
– Dictionary comprehensions in Python.
5. File Management in Python
Objectives:
– Understand the fundamental concepts of file management in Python.
– Learn how to properly open, read, write, and close files.
– Handle potential errors during file operations.
– Work with CSV and JSON files using the appropriate modules.
– Create and use temporary files for storing temporary data.
5.1. Introduction to File Management in Python
5.2. Opening and Closing Files in Python
– Opening a File in Python
– Closing a File in Python
5.3. Reading Files in Python
– Reading an Entire File in Python
– Reading Line by Line in Python
– Reading Using `readlines` in Python
5.4. Writing to Files in Python
– Writing to a File in Python
– Appending to a File in Python
– Writing in Binary Mode in Python
5.5. Error Handling in Python
– Exception Handling in Python
– Using `with` in Python
5.6. Handling CSV Files in Python
– Reading CSV Files in Python
– Writing to CSV Files in Python
5.7. Handling JSON Files in Python
– Reading JSON Files in Python
– Writing to JSON Files in Python
5.8. Creating and Using Temporary Files in Python
6. Exceptions and Error Handling in Python
Objectives:
– Understand exceptions and their importance in programming.
– Learn the basics of error handling in Python.
– Use `try`, `except`, `else`, and `finally` blocks to manage exceptions.
– Raise exceptions to signal errors in the code.
– Handle multiple types of exceptions within a single `try` block.
– Apply best practices for effective error management.
6.1. Introduction to Exceptions in Python
6.2. Raising Exceptions in Python
– Using `raise` in Python
– Raising Predefined Exceptions in Python
– Creating Custom Exceptions in Python
6.3. Handling Exceptions with `try`, `except`, `else`, and `finally`
– Basic Structure for Catching Exceptions in Python
– Code to Execute if No Exception is Raised in Python
– Code to Execute Regardless of Exceptions in Python
6.4. Handling Multiple Exceptions
– Managing Different Types of Exceptions in Python
– Handling Generic Exceptions in Python
6.5. Using Exceptions for Debugging
– Obtaining Complete Information About the Exception in Python
– Retrieving Exception Details in Python
6.6. Best Practices for Exception Handling
– Avoid Hiding Exceptions in Python
– Specify Which Exceptions to Catch in Python
– Use `finally` Blocks for Cleanup in Python
6.7. Handling Exceptions in Functions in Python
– Managing Exceptions at the Function Level in Python
– Propagating Exceptions in Python
7. Object-Oriented Programming (OOP) in Python
Objectives:
– Master the fundamentals of object-oriented programming (OOP) in Python.
– Create and manage classes, objects, constructors, and destructors.
– Apply encapsulation to protect data and inheritance to structure classes.
– Use polymorphism to handle different objects uniformly.
– Implement abstract classes and interfaces to define contracts in code.
7.1 Introduction to Object-Oriented Programming in Python
– Definition of OOP in Python
– Advantages of OOP in Python
7.2. Classes and Objects
– Creating a Class with Attributes and Methods in Python
– Object Creation in Python
– Accessing Attributes and Methods in Python
7.3. Constructors and Destructors in Python
– Constructor (`__init__`) in Python
– Destructor (`__del__`) in Python
7.4. Encapsulation in Python
– Public and Private Attributes in Python
– Accessors and Mutators in Python
7.5. Inheritance in Python
– Definition and Usage in Python
– Calling the Parent Class Constructor
7.6. Polymorphism in Python
– Definition and Usage in Python
– Method Overloading in Python
7.7. Special Methods in Python
– Special Instance Methods in Python
– Other Special Methods in Python
7.8. Abstract Classes and Interfaces in Python
– Abstract Classes in Python
– Implementing Abstract Methods in Python
8. External Libraries and Modules in Python
Objectives:
– Install, manage, and update external modules in Python.
– Import and use specific modules, functions, and popular libraries in Python.
– Explore and use the standard modules provided by Python.
– Create and structure your own modules in Python.
– Create and manage virtual environments to isolate project dependencies.
8.1. Introduction to Libraries and Modules in Python
– Definition: Libraries and Modules in Python
– Advantages of Libraries and Modules in Python
8.2. Installing and Managing External Modules in Python
– Using `pip` in Python
-`requirements.txt` File in Python
8.3. Importing and Using Modules
– Basic Import in Python
– Dynamic Import in Python
8.4. Standard Modules in Python
– `os` Module in Python
– `sys` Module in Python
– `datetime` Module in Python
– `json` Module in Python
8.5. Popular External Modules in Python
– `requests`: Making HTTP Requests in Python
– `numpy`: Numerical Computation and Array Manipulation in Python
– `pandas`: Data Analysis and Manipulation in Python
– `matplotlib`: Creating Graphs and Visualizations in Python
8.6. Creating Custom Modules in Python
– Module Structure in Python
– Creating a Package in Python
8.7. Using Virtual Environments in Python
– Creating a Virtual Environment in Python
– Activating the Virtual Environment in Python
– Managing Dependencies in Python
8.8. Managing Dependencies with `pipenv` and `poetry` in Python
– `pipenv`: Installation and Usage in Python
– `poetry`: Installation and Dependency Management in Python
9. Data Processing with Pandas (Python)
Objectives:
– Understand what Pandas is and its use for data processing.
– Learn the basic concepts and main functionalities of Pandas.
– Install Pandas and set up the environment for its use.
– Work with key data structures: Series and DataFrames.
– Read, write, and manipulate data, including cleaning and visualizing, using Pandas.
9.1. Introduction to Pandas in Python
– Definition of Pandas in Python
– Advantages of Pandas in Python
9.2. Installation and Configuration of Pandas in Python
– Installing Pandas via `pip` in Python
– Verifying Pandas Installation in Python
9.3. Data Structures in Pandas (Python)
– Series in Pandas (Python)
– DataFrames in Pandas (Python)
9.4. Data Import and Export with Pandas in Python
– Reading Data in Pandas (Python)
– Writing Data in Pandas (Python)
9.5. Data Manipulation with Pandas in Python
– Data Selection in Pandas (Python)
-Data Manipulation in Pandas (Python)
9.6. Data Cleaning with Pandas in Python
– Handling Missing Values in Python
– Removing Duplicates in Python
– Correcting Inconsistencies in Python
9.7. Data Analysis with Pandas in Python
– Descriptive Statistics in Python
– Grouping and Aggregation in Python
9.8. Data Visualization with Pandas in Python
-Basic Graphs in Python
– Customizing Graphs in Python
9.9. Merging and Joining Data in Python
– Merging DataFrames in Python
– Joining DataFrames in Python
9.10. Advanced Data Processing with Pandas in Python
– Pandas Profiling in Python
– Converting Columns to Datetime in Python
– Analyzing Time Series in Python
10. Data Visualization with Matplotlib and Seaborn in Python
Objectives:
– Install and set up Matplotlib and Seaborn for data visualization.
– Create and customize basic charts using Matplotlib.
– Utilize Seaborn for advanced statistical visualizations.
– Integrate Matplotlib and Seaborn with Pandas DataFrames
– Explore advanced chart types and components for complex visualizations.
10.1. Introduction to Data Visualization in Python
– Importance of Data Visualization in Python
– Matplotlib: The Basic Library for Visualization in Python
– Seaborn: Advanced Graphics Library in Python
10.2. Installation and Setup of Matplotlib and Seaborn
– Installing Matplotlib and Seaborn via pip (Python)
– Verifying the Installation of Matplotlib and Seaborn (Python)
10.3. Visualization in Python with Matplotlib
– Line Chart in Python with Matplotlib
– Histogram in Python with Matplotlib
– Bar Chart in Python with Matplotlib
– Pie Chart in Python with Matplotlib
10.4. Customizing Charts in Python with Matplotlib
– Customizing Axes in Python with Matplotlib
– Styles and Colors in Python with Matplotlib
– Annotations in Python with Matplotlib
– Legends in Python with Matplotlib
10.5. Visualization with Seaborn
– Distribution Plot in Python with Seaborn
– Scatter Plot in Python with Seaborn
– Box Plot in Python with Seaborn
– Pair Plot in Python with Seaborn
10.6. Customizing Seaborn Charts
– Themes in Python with Seaborn
– Color Palettes in Python with Seaborn
– Titles and Labels in Python with Seaborn
10.7. Integration with Pandas (Python)
– Visualizing Pandas Data in Python
– Visualization with Seaborn and Pandas in Python
10.8 Advanced Charts and Components in Python
– Subplots in Python
– Facets with Seaborn in Python
11. Network Programming in Python
Objectives:
– Understand the fundamental concepts of network programming.
– Learn about the OSI model layers and their relevance to network programming.
– Use Python’s `socket` library to create network applications.
– Implement and utilize communication protocols such as HTTP, FTP, and SMTP.
– Handle asynchronous operations, ensure performance, and secure communication.
11.1. Introduction to Network Programming
– Basic Concepts: Protocol, Port, IP Address in Python
– OSI Model in Python
11.2. Using Sockets in Python
– Creating a Socket Server in Python
– Creating a Socket Client in Python
11.3. Communication Protocols in Python
– HTTP with `requests` in Python
– FTP with `ftplib` in Python
– SMTP with `smtplib` in Python
11.4. Asynchronous Programming and Concurrency in Python
– Asynchronous Programming with `asyncio` in Python
– Concurrency with `threading` and `multiprocessing` in Python
11.5. Network Security in Python
– SSL/TLS with `ssl` in Python
– Authentication and Authorization in Python
11.6. Error Handling and Network Debugging in Python
– Learning to handle errors and debug network applications
– Exception Handling in Python
– Debugging in Python
12. Concurrent and Asynchronous Programming in Python
Objectives:
– Apply asynchronous programming using `asyncio` in Python.
– Understand the fundamentals of concurrent programming.
– Use threads to execute tasks simultaneously in Python.
– Execute tasks in parallel using processes in Python.
– Master synchronization and communication between threads and processes.
12.1. Introduction to Concurrent Programming in Python
– Concurrency, Parallelism, and Threads, Processes in Python
– Race Conditions and Locking in Python
12.2. Programming with Threads in Python
– Creating Threads in Python
– Sharing Data between Threads in Python
12.3. Programming with Processes in Python
– Creating Processes in Python
– Sharing Data between Processes in Python
12.4. Synchronization and Communication in Python
– Synchronization Mechanisms in Python
– Communication between Threads and Processes in Python
12.5. Asynchronous Programming with `asyncio` in Python
– Asynchronous vs. Concurrent and Event Loop in Python
– Creating Asynchronous Functions in Python
– Managing Asynchronous Tasks in Python
12.6. Using Asyncio for Network Programming in Python
– Asynchronous Server in Python
– Asynchronous Client in Python
13. Web Development with Flask/Django
Objectives:
– Learn to create web applications using Django.
– Implement authentication and authorization features.
– Understand the steps to deploy web applications in production.
– Test and debug web applications.
– Master best practices for securing web applications.
13.1. Introduction to Web Development in Python
– Web Fundamentals: HTTP/HTTPS, URLs, Routes
– Flask vs Django: Comparison of Flask and Django
13.2. Web Development with Flask in Python
– Installation and Configuration with Flask in Python
– Routes and Views in Python
– Templates with Jinja2 in Python
– Form Handling in Python
– Error Handling and Debugging in Python
13.3. Web Development with Django
– Installation and Configuration of Django in Python
– Routes and Views Configuration in Python
– Templates with Django in Python
– Form Handling in Python
– Model Management in Python
13.4. Authentication and Authorization in Python
– Flask in Python
– Django in Python
13.5. Web Application Deployment in Python
– Deployment with Flask in Python
– Deployment with Django in Python
– Gunicorn with Nginx and PostgreSQL in Python
13.6. Testing and Debugging in Python
– Testing with Flask in Python
– Testing with Django in Python
13.7. Protection Against CSRF Attacks with Flask-WTF in Python
14. Testing and Debugging in Python
Objectives:
– Understand the importance of testing and automated testing.
– Learn how to write and execute unit tests using the unittest module.
– Use pytest for more advanced and flexible testing.
– Master debugging with pdb and the features of IDEs like PyCharm and VSCode.
– Perform integration and functional tests to validate interactions between components.
14.1. Introduction to Testing in Python
– Why Test in Python?
– Types of Tests in Python
14.2. Unit Testing with unittest in Python
– Installation and Configuration of unittest in Python
– Writing Unit Tests in Python
– Using Assertions in Python
– Running Tests in Python
14.3. Testing with pytest in Python
– Installing pytest in Python
– Writing Tests with pytest in Python
– Advanced Features: Fixtures in Python
– Running Tests via the Command Line in Python
14.4. Debugging with pdb in Python
– Introduction to pdb in Python
– Basic Usage in Python
14.5. Debugging with IDE with Python
– PyCharm with Python
– Visual Studio Code with Python
14.6. Integration and Functional Testing in Python
– Integration Tests on Flask and Django in Python
– Functional Tests: Selenium for Testing in Python
14.7. Advanced Testing Methods in Python
– Performance Testing in Python
– Security Testing in Python
– Compatibility Testing in Python
14.8. Dependency Management and Testing Environments in Python
– Virtual Environments in Python
– Dependency Management in Python
15. Dependency and Virtual Environment Management in Python
Objectives:
– Manage Python dependencies with pip and poetry.
– Understand the importance of dependencies in Python.
– Create and manage virtual environments.
– Design robust CLI applications.
– Configure development and production environments.
15.1. Introduction to Dependency Management in Python
– What is a Dependency in Python?
– Common Issues in Python
– Dependency Management Tools: pip, pipenv, poetry in Python
15.2. Using pip for Dependency Management in Python
– Installing Packages in Python
– Updating Packages in Python
– Uninstalling Packages in Python
– Listing Installed Packages in Python
15.3. Creating and Managing Virtual Environments
– Introduction to Virtual Environments in Python
– Creating a Virtual Environment with venv in Python
– Using virtualenv in Python
15.4. Dependency Management with pipenv in Python
– Installing pipenv in Python
– Creating an Environment with pipenv in Python
– Managing Dependencies in Python
– Using Pipfile and Pipfile.lock in Python
15.5. Dependency Management with poetry in Python
– Installing poetry in Python
– Creating and Configuring a Project in Python
– Managing Versions with poetry in Python
– Running and Deploying with poetry in Python
15.6. Dependency Management for Python Projects
– Dependency Files and Environment Separation in Python
– Reproducibility: Implementation and Verification in Python
15.7. Managing Development and Production Environments in Python
– Development Environment in Python
– Production Environment in Python
16. CLI Application Development with Python
Objectives:
– Understand CLI applications and their use
– Master argparse for command-line argument parsing
– Handle errors and validate user inputs in CLI apps.
-Deploy CLI applications for direct terminal execution.
– Provide documentation and help for CLI users.
16.1. Introduction to CLI Applications in Python
– What is a CLI Application in Python?
– Advantages of CLI Applications in Python
16.2. Using argparse for CLI Applications in Python
– Introduction to argparse in Python
– Creating a Simple CLI Script in Python
– Options and Arguments
– Advanced Options for CLI Applications in Python
16.3. Using click for Advanced CLI Applications
– Introduction to click in Python
– Installing click in Python
– Creating a CLI Script with click in Python
– Advanced Options for CLI Applications in Python
16.4. Error Handling and Validation in Python
– Error Handling in Python
– Input Validation in Python
16.5. Deployment and Execution of CLI Applications
– Creating an Executable Script in Python
– Installing Scripts in Python
– Executing and Distributing CLI Applications in Python
16.6. Documentation and Help in Python
– Built-in Documentation in Python
– Help Messages in Python
17. Security and Cryptography in Python
Objectives:
– Understand the basics of application security.
– Identify and protect against common threats.
– Use cryptography to secure data.
– Implement robust authentication and authorization.
– Protect user sessions and conduct security audits.
17.1. Introduction to Application Security in Python
– Basics of Application Security in Python
– Common Security Threats in Python
– Security Measures in Python
17.2. Cryptography in Python
– Introduction to Cryptography in Python
– Using the cryptography Library in Python
– Symmetric Cryptography in Python
– Asymmetric Cryptography in Python
17.3. Key Management and Secure Storage in Python
– Key Storage in Python
– Cryptographic Key Rotation in Python
– Protecting Cryptographic Keys in Python
17.4. Authentication and Authorization in Python
– Authentication in Python
– Authorization in Python
17.5. Session and Cookie Security in Python
– Using Secure Sessions with Web Frameworks in Python
– Secure Cookies (HttpOnly, Secure, and SameSite) in Python
17.6. Protection Against Common Attacks in Python
– SQL Injection in Python
– Cross-Site Scripting (XSS) in Python
– Cross-Site Request Forgery (CSRF) in Python
17.7. Security Audits and Testing in Python
– Using Vulnerability Testing Tools in Python
– Security Audits in Python
18. Deployment of Python Applications
Objectives:
– Use Docker to containerize Python applications.
– Deploy Python applications on cloud platforms.
– Automate deployment with CI/CD tools.
– Configure logging and monitoring.
– Manage versions and security in production.
18.1. Introduction to Python Application Deployment
– What is Python Application Deployment?
– Deployment, Testing, and Production Environments in Python
– Objectives of Python Application Deployment
18.2. Preparing a Python Application for Production
– Configuring the Python Application
– Managing API Keys, Passwords, and Secret Managers in Python Applications
– Optimizing Python Application Performance
18.3. Containerizing Python Applications with Docker
– Introduction to Docker for Python
– Creating a Dockerfile for Python
– Building and Running the Container for Python
18.4. Deploying Python Applications on Cloud Platforms
– Deploying Python Applications on AWS
– Deploying Python Applications on Heroku
– Deploying Python Applications on Google Cloud Platform (GCP)
18.5. Deployment with CI/CD Tools
– Introduction to CI/CD for Python Applications
– Setting Up CI/CD Pipelines for Python Applications
18.6. Logging and Monitoring Python Applications
– Logging Python Applications
– Monitoring Python Applications
18.7. Python Application Release and Version Management
– Releasing Python Applications to Production
– Version Management for Python Applications
18.8. Securing Python Application Deployment
– Securing Environments in Python
– Managing Secrets for Python Applications
– Auditing and Compliance for Python Applications