Data Science
Data science is a trendy topic these days and the field is expanding quickly, but many people are unsure of what the term actually means. In this post, we'll try to clarify what data science is and how to utilise it in business analytics.
Data
First of all, what exactly is data? Data is omnipresent, and people are terrified of it being stolen. Data, however, is something that can teach us a significant amount about a person, a company, and international businesses.
Using data effectively in data science means developing analytical models from the data and making decisions on them.
Data Science
Three words—analysis, statistics, and machine learning—combine to form the term data science.

• Analysis is performed to extract the data's practical insights.
• For identifying and interpreting data patterns, statistics is used.
• Machine learning is utilized to forecast data. Approaching the literal definition: Data science is the application of data to enhance decision-making to accomplish three objectives,

1. Analysis
2. Statistics
3. Machine Learning

You now understand what data Science and its uses , moving toward Which prerequisites must be satisfied before you may begin with data science.

Tools for Data Science

- Python
Other programming languages, such R, are also utilised in data science. But we'll be talking about which one is easiest to put into practice.
Python is currently gaining popularity because of how simple the syntax is while writing code in it. It can also run on a variety of devices such as Windows and Mac

- Anaconda
It's convenient because most of the data science packages we need are already there and are free, so we don't have to install additional programmes.

- Jupyter Notebook
It is a web-based Python interface that makes learning Python very simple, You can use to generate and distribute documents with text, mathematics, and live code.

- Numpy
It is scientific computing toolkit in Python that we use whenever we need to perform calculations.

- Pandas
For me, it combines Excel and SQL. its for data manipulation and analysis tool

For Machine Learning portion and the model validation:

- Scikit-learn
It is Python's most practical and reliable machine learning library. It offers a variety of effective methods for statistical modelling and machine learning, including  dimensionality reduction, clustering, and regression, all through a Python interface.

- Matplotlib
A cross-platform library for Python's numerical extension NumPy and data visualisation and graphical charting

- Seaborn
Built upon Matplotlib, Seaborn uses single lines to create stunning data visualisations of statistical data.

These all are open source, free tools are a cornerstone of data science,
I hope you find this blog fascinating; I hope to see you again soon.