Python Programming

60 Hours

Intermediate

10 Modules

LE 5,499.00
LE 7,000.00
LE 5,499.00
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Python is a versatile, high-level programming language known for its readability and ease of use. Its clean syntax makes it an excellent choice for both beginners and experienced developers. Python supports various programming paradigms, including procedural, object-oriented, and functional programming. Its extensive standard library and community-contributed packages make it ideal for a wide range of applications, from web development and automation to scientific computing and data analysis. Some core features of Python include: Simple Syntax: Easy to read and write, Python code resembles pseudocode, allowing developers to focus more on solving problems than on language-specific intricacies. Interpreted Language: Python is an interpreted language, meaning code execution happens line-by-line, making it easier to debug. Extensive Libraries: Python has libraries like NumPy, Pandas, and Matplotlib that are powerful tools for data manipulation and visualization, as well as SciPy and Scikit-learn for machine learning.
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Vendor: Cloud Learning Academy
Categories: Python,
Product Type: Python Courses
    • Course Outline
    • What you will learn
    • Audience profile
    Overview of Python and its applications Installing Python and setting up the environment Using Python interpreters and IDEs Basic syntax, comments, and keywords
    Primitive data types (integers, floats, strings, booleans) Variables and memory allocation Typecasting and type checking Working with None and null values
    Arithmetic, comparison, and logical operators Assignment and bitwise operators Using expressions and operator precedence
    Conditional statements: if, elif, else Looping structures: for, while, nested loops Using break, continue, and pass statements
    Lists, tuples, dictionaries, and sets Accessing, modifying, and iterating through data structures List comprehensions and dictionary comprehensions Working with collections and nested data structures
    Defining and calling functions Parameters, arguments, and return values Lambda functions and functional programming Scope and namespaces, closures, and decorators
    Understanding exceptions and error types Using try, except, else, and finally Custom exception handling and raising errors
    Reading from and writing to files Working with file paths and directories Context managers for file handling Introduction to CSV and JSON file handling
    Understanding classes and objects Attributes, methods, and constructors Inheritance, polymorphism, encapsulation, and abstraction Working with class and static methods
    Importing and using modules Creating and structuring packages Introduction to popular standard libraries (e.g., math, datetime) Using third-party packages and pip
    Understand Python syntax, variables, and data types. Learn essential data structures like lists, dictionaries, tuples, and sets
    Learn to work with Pandas DataFrames and Series for organizing and manipulating datasets. Perform data cleaning, filtering, grouping, and aggregation to prepare data for analysis.
    Use statistical and exploratory data analysis techniques to derive insights. Calculate metrics, summarize data, and manage large datasets efficiently.
    Utilize Matplotlib and Seaborn to create various types of visualizations. Gain skills in presenting data visually for effective storytelling and insights sharing.
    Work with NumPy arrays for efficient data processing. Apply mathematical operations to datasets and perform fast array manipulations.
    Import and export data from multiple file formats, such as CSV, Excel, and SQL databases.
    Apply data analysis skills to real-world datasets and case studies. Work on end-to-end projects to strengthen your understanding and application of Python in data analysis.
    Individuals looking to build a career in data science or analytics and need foundational skills in Python for analyzing, manipulating, and visualizing data.
    Business analysts, financial analysts, or other professionals already working with data who want to enhance their skills in Python for more efficient and scalable data handling.
    Those studying data science, statistics, or related fields who require practical, hands-on skills in Python to support their academic projects or research work.

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