Data Science



Why is Python Such a Popular Choice Among Data Scientists?

With the growing role of data in our day-to-day lives, the demand for data scientists has never been higher. As a result, many people are looking to learn the skills necessary to become data scientists.

Data science is the art of extracting insights from data. It involves using techniques from various fields, including mathematics, statistics, computer science, and machine learning.

One of the most important skills for a data scientist is programming. Unsurprisingly, for data science projects in 2022, Python is the de facto programming language that data science students go with.

There are many reasons why Python is gaining popularity among data scientists. One reason is that Python is a versatile language that can be used for a variety of tasks, including data wrangling, data visualization, machine learning, and deep learning. 

Python is also relatively easy to learn, making it a good choice for people who are new to data science. Plus, it is increasingly being used in production environments at companies with some of the largest databases in the world, including Google, Facebook, and Netflix.

But what makes Python so lucrative when you choose the field of data science? Why are so many companies choosing to go with Python? Why are many professionals pursuing a Python with data science course?

In this article, we will explore some of the major reasons why Python for data scientists is a great choice.

Overview of the Python programming language

Python is an extremely versatile programming language that was first released in 1991. Created by Guido van Rossum, it was designed with an emphasis on code readability. Its syntax allows programmers to express concepts in fewer lines of code than would be possible in languages such as C++ or Java.

In the years since its release, Python has become one of the most popular programming languages in the world and is used in a wide variety of fields, from web development and scientific computing to artificial intelligence and machine learning.

Python’s popularity can be attributed to its versatility and ease of use. It is a great language for beginners as it is very readable and has a relatively simple syntax. However, it is also powerful enough to be used in complex applications such as machine learning and artificial intelligence.

Due to its power and flexibility, Python is often used in building web applications as well. Django, an open-source framework written in Python, simplifies the process of building database-driven web applications.

A Python web app is easy to maintain and extend. Since Python is interpreted and not compiled, programmers can revisit an application’s source code and make modifications without a time-consuming compilation process.

You can get started with a comprehensive Python data science course that will help you learn to use the Python programming language for complex Data Science projects. 

Key elements of Python that make it a good choice for Data Science

In this section, we will discuss the key elements of Python for data science:

1. Data structures

Python has a wide range of data structures available which can be used for efficiently storing data. These include lists, tuples, dictionaries, and sets.

Python also provides a wide range of tools for manipulating data, which make it easy to perform operations such as slicing, concatenation, and sorting.

2. Extensive data libraries

Python has several libraries which are designed for scientific computing. These include NumPy, SciPy, and matplotlib.

These libraries provide powerful tools for performing numerical computations and data visualization.

3. Accessible machine learning

Python has a number of libraries that can be used for implementing machine learning algorithms. These include scikit-learn, TensorFlow, and Keras.

These libraries provide tools for data preprocessing, model training, and model evaluation.

4. Web development frameworks

Python can be used for developing web applications using frameworks such as Django and Flask. These frameworks provide tools for handling requests, routing, database access, and template rendering.

5. Scripting

Python makes it extremely easy for a new programmer to begin automating repeating tasks. Since the language is easy, you can learn as you automate your work, one step at a time.

Due to Python’s extensive libraries, the many tasks that have already been handled before can be reused for your purpose.

Why should you learn Python for data science?

Python is a high-level programming language that aims to make coding easy. Its object-oriented programming methodology is straightforward but efficient, and it includes good high-level data structures.

Tools like Jupyter Notebook, NumPy, Pandas, Matplotlib, SciPy, and Scikit-learn are part of its data science stack. These technologies enable data scientists to work with enormous datasets effectively.

Here are some of the reasons why Python for data scientists is a preferred programming language:

1. Python is versatile

Python can be used for a wide variety of tasks such as web development, GUI development, and scientific computing.

When it comes to data science, Python can be used:

a. Data wrangling

Data wrangling is the act of getting data into a format that machine learning algorithms can use.

Almost any programming language can be used for data wrangling, but Python is a popular choice for this task because it is relatively easy to learn.

Languages that are harder to learn, such as C/C++, can be made to work with data wrangling, but Python’s syntax is simpler, and it is easier to write and debug Python code.

b. Data visualization

Python is a good choice for data visualization because its many libraries make it easy to create data visualizations.

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These include libraries like matplotlib, seaborn, and ggplot2.

  • matplotlib is a Python library for creating 2D plots. Matplotlib can be used to make bar graphs, pie charts, line charts, scatter plots, etc.
  • Seaborn is a library for creating statistical data visualizations. Seaborn can be used to make box plots, violin plots, and heatmaps.
  • ggplot2 is a Python library for creating data visualizations. ggplot2 can be used to make bar charts, histograms, line charts, scatter plots, etc.

3. Python is free and open source

The Python programming language is free and open source, so anyone can use it without having to pay anything. Data scientists frequently use Python for this reason.

Python is also growing in popularity as more data scientists are realizing the benefits of using Python.

4. Python is easy to learn

Python is a great language for data science because it is easy to learn. Its syntax is simple and consistent, and there is a wide range of libraries and tools available that can be used for data science projects.

Python has an expressive syntax that makes it easy to write code. It also has functions that help you complete complex functionality with less code compared to traditional languages.

5. Python has great community support

Python has a very large and active community of developers who are always creating new modules and libraries that can be used for data science projects.

This is extremely valuable for data scientists because it means that new functionality is always being added to the language.

Additionally, if you have any questions or need help with your code, there is a good chance that someone in the community will be able to help you.

Applications of Python in Data Science projects

Being a versatile language, Python adapts to the data scientist’s needs. Here are five applications of Python in data science:

1. Data cleaning and preparation

Python is widely used for data cleaning and preparation due to its ease of use and rich set of libraries.

Python’s Pandas library is particularly useful for data preparation, providing functions for reading and writing data, handling missing and null values, and performing data transformations.

Data cleaning often requires repetitive or tedious tasks, such as renaming variables or calculating summary statistics. Python’s syntax is designed to be readable and concise, making it an ideal language for writing scripts to automate data preparation tasks.

2. Data analysis and exploration

In addition to its usefulness for data preparation, we can use Python for data analytics too. Python’s SciPy library provides various numerical and statistical functions, while the NumPy library supports working with high-dimensional arrays.

Not only can we use Python for data analytics, but it’s also an excellent tool for exploring data, owing to its flexibility and ease of use. Its libraries for data analysis and visualization, such as matplotlib and seaborn, allow data scientists to create informative visualizations quickly.

3. Data visualization

Data visualization is an important part of data science, and Python is a powerful tool for creating visualizations. Python’s matplotlib and seaborn libraries are widely used for creating static and interactive visualizations, respectively.

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Python’s Bokeh library is also becoming increasingly popular for creating interactive visualizations due to its ability to create sophisticated visualizations with minimal code.

4. Machine learning

Machine learning is a growing area of data science, and Python is a popular language for developing machine learning models.

Python’s Scikit-learn library provides a wide range of tools for building machine learning models, including functions for preprocessing data, training models, and evaluating results.

Hero Vired Python Data Science course is a comprehensive program that dives into the depths of artificial intelligence and how to leverage it to generate powerful insights from the data at hand. 

5. Deep learning

Deep learning is a subset of machine learning that is concerned with building models that can learn from data with a high level of abstraction. Python is a popular language for deep learning due to its ease of use and rich set of libraries.

Python’s TensorFlow and Keras libraries are widely used for deep learning, providing functions for building and training models, as well as a wide range of pre-trained models.

Python vs. R for Data Science: Which is better?

Python R
Python is a general-purpose programming language that is used for project deployment and development. It includes all of the tools needed to put a project into production. R is a statistical language used for data processing and visualization.
Better suited for machine learning, deep learning, and big-scale web applications requiring massive datasets. Suitable for statistical learning having powerful libraries for data experiment and exploration.
Not limited to data science projects as Python can be used to develop advanced applications that support real-world use-cases. Developed with the intent to solve data analysis problems and does it well. However, learning R limits you to data science.
Its extensive selection of libraries and reusable code helps speed up development. It has lesser development compared to Python; however, the use-case for data exploration and analysis is satisfied.
It is a free-flowing language that resembles English, making it very simple to learn and understand The syntax can be intimidating for beginners considering it is developed with data scientists in mind. However, we would still consider R fairly easy compared to other low-level languages.
Python supports both object-oriented and functional programming paradigms. R was built to be a functional language.
More memory efficient, letting you work with large datasets with ease Can be memory intensive as the iterations start rising above 1000.

When it comes to data science, both Python and R are equally capable languages. However, Python may have a slight edge due to its versatility and ease of use. Now is the best time for getting started with Python for data science.

Power your data exploration with Python

In this article, we explored some of the compelling reasons that make Python a good choice as a scripting language for analytics. As open source software, it is free and has a huge developer community.

It has many great built-in tools and is easy to integrate with other languages such as R, C, and Matlab. It also works well with all operating systems. Plus, it is widely used for analytical purposes, especially in finance, marketing, and consumer goods industries. 

Python can be extremely powerful when venturing into the world of data science; however, it can also be intimidating.

The  Hero Vired Data Science, Machine Learning, and Artificial Intelligence program is a great Python data science course that equips you with all the knowledge you need to analyze complex data, derive objective insights and conclusions, and solve valuable business problems.

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