INTRODUCTION TO PYTHON IN DATA SCIENCE

What is Python? What is data science? Let me do you better, why is Python in data science? Seems like my infinity war reference may have not referenced, but let's not diverge from our focus, these are the pertinent issues I am to assist you to cover as you go through the article.
Data science is the domain of study that deals with vast volumes of data using modern tools and algorithms to find unseen patterns, derive meaningful information, and make business decisions. According to IBM, Data science uses a combination of maths and statistics, specialized programming, advanced analytics, Artificial intelligence(AI) and machine learning to cover with specific subject matter expertise to uncover actionable insights hidden in organization's data.
In simple terms, data science refers to the use of statistical, mathematical and computer science to extract insights from data that will assist in decision-making.

Applications of data science:
We encounter data science every day in ways we may have not realised:

• Have you ever wondered how youtube gets to know your taste since youtube recommends channels or even music that you'd prefer to watch and listen to, youtube has complex machine learning algorithms that can analyze your preferences and gives you a recommendation as per the results of the algorithm.

• The advertisement that you come across on youtube are at times personalized this is through the help of data science.

Pthon

Python is a high-level programming language, which means its a language that is easily understandable by users. It is widely used in the field of data science due to its simplicity, flexibility, and powerful libraries.

Advantages of using Python for data science include:

1. Simple and User-Friendly Syntax: Python has a simple and easy-to-learn syntax that makes it accessible to everyone, even those who are new to programming. The code is easy to read and understand, which makes it easier to write and debug.

2. Large Community: Python is an open-source language, therefore it has a large and active community of developers who constantly work on improving the language and developing new libraries. This community provides a wealth of resources, including tutorials, documentation, and forums, which makes it easier for beginners to learn the language.

3. Powerful Libraries: Python has a vast number of libraries that make it a powerful tool for data science. These libraries provide various tools for data manipulation, data analysis, machine learning and visualization, making it easier for data scientists to perform complex tasks.

4. Versatility: Python is a versatile language that can be used for a wide range of applications, including web development, scientific computing, machine learning, and data analysis. This versatility makes it a popular choice for data scient
   ists who want to work on different projects.
   Python also contains powerful libraries that make it a popular choice for data scientists. These libraries make python a powerful tool for data science and contain various tools for data manipulation, data analysis, machine learning, and visualization these libraries may include:
   NB: These libraries must first be imported to be used in your code

1.NumPy: NumPy is a Python library/module that provides support for large, multi-dimensional arrays and matrices and provides various mathematical functions that make it a powerful tool for scientific computing. import numpy as np.

1. Pandas-provides data manipulation tools for tabular data and provides support for reading and writing data from various file formats. import pandas as pd.

2. Matplotlib-a python library that provides tools for visualization and provides support for various types of plots. import matplotlib. pyplot as plt

3. Scikit-learn-python library that provides tools for machine learning.

Python plays a crucial role in the data science workflow, which involves the following steps:

-Data Collection: Data scientists collect data from various sources, including web scraping, APIs, and databases. Python provides various libraries, for example, Beautiful Soup, that make it easier to collect data from websites.

• Data Cleaning and Preprocessing: Data needs to be cleaned and preprocessed to remove any errors or inconsistencies. Python libraries, including Pandas and NumPy, provide various tools for data cleaning and preprocessing.

• Data Analysis: Data scientists analyze the data to extract insights and patterns. Python provides various libraries, including Pandas and Matplotlib, that make it easier to analyze and visualize the data.

  • Machine Learning: Data scientists use machine learning algorithms to build predictive models.

Install python from Here.

Install an IDE-integrated development environment- this eases your coding experience. There are multiple IDEs in the market but I would recommend Visual Studio Code as it is lighter and more efficient. You would be required to install the python extension from visual studio. Here are some guidelines. Click for guidance

I would also recommend trying out Anaconda, it comes with Python preinstalled and JUpyter anaconda is a powerful tool for creating machine learning algorithms and conducting EDAs. Try it out.
To start your journey to understanding python. I recommend the following websites for their awesome tutorialsw3 schools

Conclusion

I tried to give you some insights on the fundamentals. It may not be enough but I hope it gives you enough footing to start this long and exciting journey. There are many exciting resources all over the internet that you could use and they are completely free of change. You know what they say, "the best things are free", I don't know who exactly said this but this works in this case. I wish you all the best as you start the journey.