Want to get started in data science with Python?

Here are some essential concepts to learn! πŸ“ˆπŸ‘©β€πŸ’»

1/ Data Types and Structures:

Python provides various data types and structures like lists, tuples, dictionaries, and sets.

It's crucial to understand how these data types work and how to manipulate them for data analysis.
2/ Control Structures:

Control structures like loops and conditional statements are essential for data processing and analysis.

Mastering these concepts will help you efficiently manipulate large datasets and perform calculations.
3/ Functions and Modules:

Python has a vast standard library, and learning how to create and use functions and modules can help you streamline your code and make it more reusable.
4/ NumPy:

NumPy is a powerful library for numerical computing in Python. It provides support for multidimensional arrays, mathematical functions, and linear algebra operations.

Learn numpy from @DataKwery πŸ‘‡
5/ Pandas:

Pandas is another popular library for data manipulation and analysis in Python.

It provides data structures for working with tabular data, including data cleaning, filtering, and merging.

Learn pandas from @DataKweryπŸ‘‡
6/ Visualization:

Python has several visualization libraries, including Matplotlib and Seaborn, that allow you to create charts, graphs, and other visualizations to help you analyze and present your data.

Learn data viz from @DataKwery πŸ‘‡
7/ Machine Learning:

Once you have a good understanding of the above concepts, you can dive into machine learning.

Scikit-learn is a popular library for machine learning in Python and includes a range of algorithms for classification, regression, clustering, and more.
Check out the top python resources using the link given below and don't forget to SIGN UP to stay updated on the latest updates.

Click the link below to learn moreπŸ‘‡
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Sumanth @Sumanth_077 Β· Mar 20, 2023
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Great share Avi!