Learning_python

Why Python is widely used in data science?

• Versatility: Python is a versatile programming language that allows data scientists to perform a wide range of tasks, from data manipulation and analysis to machine learning and visualization. It has a rich ecosystem of libraries and tools specifically designed for data science, making it an ideal choice for the field.

• Abundant Libraries: Python boasts a wealth of data science libraries, such as NumPy, Pandas, SciPy, and Matplotlib, which provide powerful and efficient tools for data manipulation, analysis, and visualization. Additionally, libraries like scikit-learn, TensorFlow, and PyTorch are widely used for machine learning and deep learning tasks.

• Community Support: Python has a large and active community of data scientists and developers who contribute to the development and maintenance of data science libraries. This community support results in regularly updated and improved tools, as well as extensive documentation and online resources.

• Data Visualization: Excellent data visualization libraries, such as Matplotlib, Seaborn, and Plotly, enable data scientists to create informative and visually appealing graphs and plots for data exploration and presentation.

• Integration: Can easily integrate with other languages and tools, making it convenient for data scientists to work with data stored in various formats and databases. It can also be integrated with big data technologies like Hadoop and Spark.

• Scalability: Python is known for its scalability, allowing data scientists to work with both small datasets on their local machines and large datasets on remote servers or distributed computing clusters.

• Accessibility: Python is an open-source language, meaning it's free to use and has a low barrier to entry for beginners. Data scientists can quickly learn and start using Python without significant licensing costs or restrictions.

• Web Development: Python is not just for data analysis; it's also used for web development. This allows data scientists to build web-based data dashboards and applications to share their findings with others.

• Machine Learning and AI: Python is a preferred language for machine learning and artificial intelligence applications. Its machine learning libraries and frameworks, like scikit-learn, TensorFlow, and PyTorch, simplify the development and deployment of machine learning models.

• Active Development: Python continues to evolve and adapt to the needs of the data science community. New libraries and tools are regularly being developed, making Python a dynamic language for data science.

• Readability and ease of use.

Learning 'Python from basics.ipynb' file contain topics such as:-

.Comment
.Print Function
.Data types
.Variables
.Keywords
.Identifiers
.Taking user input
.Type Conversion
.Literals
.Operators
.Indentation
.if..else, elif, nested if
.While Loop
.Guessing game
.For loop
.Nested Loop
.Break,continue,Pass
.Built_in_functions
.Built_in_Modules
.Strings
.List
.Tuple
.Set
.Dictionary

.Function

.OOP

.File Handling
.Exception Handling