This article is all about Setup Python Environment for Data Science Using Anaconda Framework. Data science is a broad and dynamic field of study, combining computer science, statistics, and other fields to analyze data, extract insights, and create useful analytics. It’s also a hot one; according to the latest research from LinkedIn, demand for data scientists is growing almost three times faster than other software engineer roles. That means now is an excellent time to become a data scientist. If you want to get started with data science but aren’t sure where to begin or what tools you need, don’t worry! This article will walk you through setting up your very own Python data science environment and package library in no time. Let’s get started.

Install Anaconda

The first step in setting up your data science environment is setting up Anaconda. Anaconda is an open-source package manager, environment manager, and distributed computing environment. It’s what we’re going to use to set up our Python environment and create our libraries. Anaconda is free and cross-platform, so it doesn’t matter what type of computer you have or what operating system you use; you can set up your environment and libraries on any computer. We’ll be focusing on Python 3.7, but Anaconda also has packages and environments for Python 2.7 and Python 4.

Step 1: Install Python

First, you’ll want to install Python, which is the programming language we’ll be using. There are two main versions of Python: Python 2 and Python 3. Data scientists primarily use Python 3, so if you’re just starting out, we recommend using Python 3. You can download Python here or on any of the other sites that host it, such as the Python website, BitTorrent, or SourceForge. Because Python is open source, there are also a number of free online Python tutorials you can use to learn the basics.

Step 2: Install Scikit-learn and NumPy

Next, we’re going to install scikit-learn, which is a machine learning library for Python, and NumPy, a library for handling and analyzing large arrays of data, and matrices, and for performing mathematical operations on them. To install these libraries, we’ll use the Anaconda Navigator, a GUI for managing your Anaconda environment. Navigate to the Anaconda Navigator and search for scikit-learn and then for NumPy. Click Install beside both of the entries, and Anaconda will install the libraries for you.

Step 3: Install Pandas and SciPy

Next, we’ll install pandas and scipy, which are libraries for data manipulation and analysis. To do this, click the Install arrow in the top right corner of the Anaconda Navigator window. In the Install window, search for pandas and then scipy and click the Install button beside each of them. Pandas is a Python library that is used for data manipulation and analysis. It can be used to read, write, and rearrange data in various formats and also to perform mathematical operations on them. SciPy is a library used for mathematical operations on large arrays and matrices. After you install pandas and scipy, we’ll need to activate the libraries by clicking the dropdown next to their entries in the Anaconda Navigator and selecting the “Install ‘pandas’ and ‘scipy’ for the current environment” menu options.

Step 4: Install ML Tools

Next, we’ll need to install ML Tools, a library that contains a number of different algorithms and tools for machine learning. To do this, we’ll use the Anaconda Navigator again. Navigate to the Anaconda Navigator and search for “ML Tools.” Click Install beside the ML Tools entry, and Anaconda will install the library for you. After you’ve installed the library, activate it by clicking the dropdown next to its entry in the Anaconda Navigator and selecting the “Install ‘ML Tools’ for current environment” menu option. After you’ve installed these libraries, you’re ready to start doing data science! You can create scripts and automate tasks, use useful libraries for data manipulation and analysis, and learn more about different types of data science by reading blogs and online forums.