If you want to learn Data Science, you should know these things

Data science is the study of data. It involves developing methods of recording, storing, and analyzing data to effectively extract useful information. Data science is more closely related to the mathematics field of Statistics, which includes the collection, organization, analysis, and presentation of data.

Python:- Python is an easy, useful, powerful, interpreted, high-level, general-purpose programming language. Created by Guido van Rossum and first released in 1991, Python’s design philosophy emphasizes code readability with its notable use of significant whitespace.

Basically python is an easy vast library language, if you are from the non-tech or programming background you can also easily learn python and write code.

Pip:- Pip is a de facto standard package-management system used to install and manage software packages written in Python.

PyPI:- Find, install and publish Python packages with the Python Package Index. The Python Package Index (PyPI) is a repository of software for the Python programming language. PyPI helps you find and install software developed and shared by the Python community.

Anaconda:- Anaconda is a free and open-source distribution of the Python and R programming languages for scientific computing (data science, machine learning applications, large-scale data processing, predictive analytics, etc.), that aims to simplify package management and deployment.

Jupyter:- Jupyter is the Data Science platform powered by python. The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations, and explanatory text. Uses include data cleaning and transformation, numerical simulation, statistical modeling, machine learning and much more.

The Jupyter Notebook is an incredibly powerful tool for interactively developing and presenting data science projects. Users can change and visualize their data in a very easy way.

Scikit-learn:- Scikit-learn is a free machine learning library for Python. It features various algorithms like support vector machine, random forests, and k-neighbours, and it also supports Python numerical and scientific libraries like NumPy and SciPy.

NumPy:- NumPy is the fundamental package for scientific computing with Python. It contains among other things:

• a powerful N-dimensional array object
• sophisticated (broadcasting) functions
• tools for integrating C/C++ and Fortran code
• useful linear algebra, Fourier transform, and random number capabilities

Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.

Pandas:- In computer programming, pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series.

Kaggle:- Kaggle, a subsidiary of Google LLC, is an online community of data scientists and machine learning practitioners. Kaggle allows users to find and publish data sets, explore and build models in a web-based data-science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.

Kaggle got its start by offering machine learning competitions and now also offers a public data platform, a cloud-based workbench for data science, and Artificial Intelligence education.

Enjoy Data Science. 😃

Feel free to ask any questions or queries in the comment section or you can ping me on Facebook.