Python’s Applications in the Profession of Data Science


Year : 2025 | Volume : 03 | Issue : 01 | Page : 23-30
    By

    V Hans,

  1. Research Professor, Departments of Commerce and Management, and Social Sciences and Humanities, Srinivas University, Mangaluru, Karnataka, India

Abstract

document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_abs_177479’);});Edit Abstract & Keyword

Because of its ease of use, adaptability, and huge ecosystem of libraries, Python has become one of the most influential programming languages in the field of data science. Python is highly valued for its straightforward and versatile nature. This study delves into its various uses in data science, including tasks like data preprocessing, exploratory data analysis (EDA), statistical modeling, machine learning, and creating visualizations. Libraries like Pandas and NumPy make tasks like data preparation and manipulation more efficient and straightforward. Matplotlib and Seaborn are two examples of libraries that make it easier to create visualizations that are insightful. In addition, its machine learning frameworks, such as Scikit-learn, TensorFlow, and PyTorch, give data scientists the ability to construct predictive models and make use of the promise of artificial intelligence. The interoperability of Python with big data tools, such as Apache Spark, considerably expands the scope of Python’s capabilities for managing large-scale datasets. In order to demonstrate Python’s vital role in translating raw data into meaningful insights across a variety of industries, including healthcare, finance, marketing, and technology, this study focuses on real-world applications and case studies. Python is highlighted as a fundamental component of contemporary data science methods in this study by highlighting its adaptability and the support it receives from the community.

Keywords: Python, exploratory data analysis, data professionals, data-driven companies, big data, and deep learning

[This article belongs to International Journal of Data Structure Studies ]

How to cite this article:
V Hans. Python’s Applications in the Profession of Data Science. International Journal of Data Structure Studies. 2025; 03(01):23-30.
How to cite this URL:
V Hans. Python’s Applications in the Profession of Data Science. International Journal of Data Structure Studies. 2025; 03(01):23-30. Available from: https://journals.stmjournals.com/ijdss/article=2025/view=0


document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_ref_177479’);});Edit

References

  1. Raschka S, Patterson J, Nolet C. Machine learning in python: Main developments and technology trends in data science, machine learning, and artificial intelligence. Information. 2020 Apr; 11(4): 193.
  2. Sial AH, Rashdi SY, Khan AH. Comparative analysis of data visualization libraries Matplotlib and Seaborn in Python. International Journal of Advanced Trends in Computer Science and Engineering (IJATCSE). 2021 Jan; 10(1): 2770–81.
  3. Gandhi P, Pruthi J. Data visualization techniques: traditional data to big data. Data Visualization: Trends and Challenges Toward Multidisciplinary Perception. Singapore: Springer; 2020; 53–74.
  4. Igual L, Seguí S. Introduction to data science. In: Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications. Cham: Springer International Publishing; 2024 Apr 13; 1–4.
  5. Hill C. Learning scientific programming with Python. Cambridge University Press: Cambridge University Press; 2020 Oct 22.
  6. Sundnes J. Introduction to scientific programming with Python. Cham: Springer Nature; 2020.
  7. Jalolov TS. Exploring the mathematical libraries of python: a comprehensive guide. World of science. 2024; 7(5): 121–7.
  8. Bruce P, Bruce A, Gedeck P. Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python. Sebastopol (CA): O’Reilly Media; 2020.
  9. Berman F, Rutenbar R, Hailpern B, Christensen H, Davidson S, Estrin D, Franklin M, Martonosi M, Raghavan P, Stodden V, Szalay AS. Realizing the potential of data science. Commun ACM. 2018 Mar 26; 61(4): 67–72.
  10. Cao L. Data science: challenges and directions. Commun ACM. 2017 Jul 24; 60(8): 59–68.
  11. Severance C. Guido van rossum: The early years of python. Computer. 2015 Feb 16; 48(2): 7–9.
Regular Issue Subscription Review Article
Volume 03
Issue 01
Received 20/12/2024
Accepted 07/02/2025
Published 10/03/2025
Publication Time 80 Days
99

async function fetchCitationCount(doi) {
let apiUrl = `https://api.crossref.org/works/${doi}`;
try {
let response = await fetch(apiUrl);
let data = await response.json();
let citationCount = data.message[“is-referenced-by-count”];
document.getElementById(“citation-count”).innerText = `Citations: ${citationCount}`;
} catch (error) {
console.error(“Error fetching citation count:”, error);
document.getElementById(“citation-count”).innerText = “Citations: Data unavailable”;
}
}
fetchCitationCount(“10.37591/IJDSS.v03i01.0”);

Loading citations…