Introduction to Data Science in Python

As the world entered the era of big data in recent decades, the demand for more effective and efficient data storage greatly expanded. Businesses that use big data invest a lot of time and energy in creating frameworks that can hold a lot of information. The storage of vast amounts of data was then made possible by the creation of frameworks like Hadoop.

As the storage issue can be resolved by using the frameworks the next issue that comes is to process the data that had already been stored. The solution to processing the data and getting the useful information in a proper manner is given by data science. Data Science has come as the method of getting and handling the data in a proper manner to get useful information. Data science becomes a great tool for the industry that deals with a large amount of data.

Introduction to Data Science using Python

Python is a high-level language that can be used in various domains which include programming and the development of applications. Also, as we have discussed above data science is the field that works with different types of data from the various kind of industries that use the data.

Python has various abilities and becomes a flexible language that is easy to code or program and it can perform various extremely hard mathematical processing that is needed for data science programming. Python programming language has a vast community of users who works on it or with it and it is used for both scientific computing as well as general computing.

Python has shown a great hand in both mentioned fields. In addition, the python programming language consists of various, vast, pre-defined libraries which contain the code to perform nearly every task by just including those libraries in the code.

Benefits of the Python Programming Language

In data science we have to perform various tasks with the data like visualization, cleaning, processing, etc and for each of these tasks we need a programming language or a tool which could be possibly python.

There are other options available to work with data science such as the tool SAS or the programming language R and in this section, we are going to see why python is best and what are the benefits of the python programming language over others.

In recent times python is on top among programming languages and gained a lot of popularity. Data science is not only the field where it is usage of the python is increase, it also covers the area of the Artificial Intelligence, Internet of Things, and the other technologies.

Data science is all about handling the data using the mathematical and statistical concepts to get the useful information from it and in these fields there is no competition of the python programming language. This makes the python is used by data experts around the globe. Over the recent years there is only trend of the python programming language in this field.

Python Libraries for Data Science

Python's libraries are what put it ahead of other programming languages for every task; none of them can compare to the quality of the libraries offered by Python. Libraries feature pre-written code for specific tasks, so users don't have to repeat it while writing a project. Let's have a look at some Python libraries that are useful for data science

NumPy

NumPy is the most powerful when we want to work on n-dimensional arrays. NumPy contains the basic algebra function such as the linear algebra function and it provides advanced random number capabilities. Also, it provides integration with other programming languages or other tools.

Pandas

To perform the structured data manipulations and operations we can use the Pandas library of python. Pandas library is not very old in python and was added very recently and it boots the Python use in data science.

Matplotlib

Matplotlib library is used to plot graphs of various kinds for data science. By using the matplotlib library we can plot any kind of graph.

Scikit-learn

The scikit-learn library of python is a combination of NumPy and matplotlib and is mostly used to plot graphs. In data science many times we need to visualize the data for such operations we need these libraries.

Data Visualization with Python

A lot of data is produced every day, and sometimes it can be challenging to analyze this data for specific trends or patterns if it is in its raw form. Data visualization is used to solve this problem. Data visualization makes it simpler to comprehend, observe, and analyze the data by providing a good, organized pictorial depiction of it. Python offers a variety of libraries with diverse functionalities for displaying data. Each of these libraries has unique features and supports a range of graph types. Here are a few of the libraries

• Matplotlib

• Seaborn

• Bokeh

• Plotly

Data Processing in Python

Data processing, in general, is obtaining and modifying data elements to produce meaningful, potentially valuable information. There are numerous processing formats for various encoding kinds.

You can manage some encoding procedures with Python, and it's more appropriate for data processing than other languages because of its straightforward syntax, scalability, and cleanliness, which enable the solution of various difficult problems in a variety of methods. To make those encoding techniques work, all you'll need are a few libraries or modules, like Pandas.

What makes data processing so important?

Data science requires data processing to be successful. Poor-quality and incorrect data can be detrimental to procedures and analysis. Increased productivity and high-quality information for your decision-making are two benefits of good, clean data.

Is Python Necessary in the data science field?

Either Python or R are suitable for use in a data scientist position. Each language has advantages and disadvantages. Both are frequently employed in the sector. R is more prevalent in some industries, although Python is more often used overall (particularly in academia and research).

You must learn at least one of these two languages if you want to work in the field of data science. Regardless of the language you select, you must also learn a little SQL.

Conclusion

Data Science has come as the method of getting and handling the data in a proper manner to get useful information. Data science becomes a great tool for the industry that deals with a large amount of data. Data science is all about handling the data using the mathematical and statistical concepts to get the useful information from it and in these fields there is no competition of the python programming language. This makes the python is used by data experts around the globe. Over the recent years there is only trend of the python programming language in this field.