Python for data analytics -8 classes(60 min each)
Papun Rout
Python for data analytics -8 classes(60 min each)
- Python is a popular and versatile programming language that is widely used in the field of data science. It offers a range of tools and libraries for data manipulation, analysis, and visualization. Here are some of the essential topics that you should learn to master Python for data science:
- - **Data types and variables**: You should be familiar with the basic data types in Python, such as integers, floats, strings, and booleans, and how to assign and use variables to store and manipulate data.
- - **Compound data structures**: You should learn how to use compound data structures, such as lists, tuples, and dictionaries, to store and access multiple items of data in an organized and efficient way.
- - **Conditionals, loops, and functions**: You should learn how to use conditional statements, such as if, elif, and else, to control the flow of your program based on certain conditions. You should also learn how to use loops, such as for and while, to repeat a block of code for a certain number of times or until a condition is met. You should also learn how to define and use functions, which are reusable blocks of code that perform a specific task.
- - **Object-oriented programming**: You should learn the basics of object-oriented programming, which is a paradigm that organizes data and behavior into classes and objects. You should learn how to create and use classes, objects, methods, attributes, inheritance, and polymorphism in Python.
- - **External libraries**: You should learn how to use external libraries, which are collections of code that provide additional functionality and features for Python. Some of the most important libraries for data science are:
- - **NumPy**: It is a library for numerical computing that provides fast and efficient operations on arrays and matrices.
- - **Pandas**: It is a library for data manipulation and analysis that provides high-level data structures, such as Series and DataFrame, and various tools for data cleaning, processing, and visualization.
- - **Matplotlib**: It is a library for data visualization that provides various types of plots, such as line, bar, scatter, histogram, and pie charts, to display and explore data