Dr. V. N. Rajavarman, Mrs. R. Gayathri, Mrs. R. Hemavathi

Publication Date:

DECEMBER 1, 2022

No.of Pages: 214
Price: 350/-



Magestic Technology Solutions (P) Ltd.
Place of Publication:
Chennai, India.


The field of study known as “data science,” the goal is to glean useful information from massive volumes of data by using a wide variety of scientific approaches, algorithmic procedures, and other procedures. Discovering hidden patterns in the raw data is much easier with its assistance. As a result of developments in mathematical statistics, data analysis, and big data, a new field known as “data science” has come into existence. The discipline of Data Science is an interdisciplinary one that enables one to derive information from either organised or unstructured data. The field of data science gives you the ability to turn an issue with your company into a research project and then turn that project into a solution for real-world problems. When it comes to the field of data science, we need some kind of programming language or instrument, such as Python. In spite of the fact that there are more tools for data science, such as R and SAS, the primary emphasis of this post will be on Python and how it may be advantageous for data science. In recent years, Python has emerged as a dominant force in the world of programming languages. Its incorporation into data science, the internet of things, artificial intelligence, and other technological fields has contributed to the rise in its popularity. Python is a popular choice for usage as a programming language for data science due to the fact that it provides access to a variety of powerful mathematical and statistical tools. Python is used by data scientists all around the globe, and this is a big reason why Python is used. If you’ve been paying attention to industry developments over the last few years, you’ve probably seen that Python has emerged as the dominant programming language, especially in the data science sector. This book has five units that cover the whole of the university curriculum for Data Science. Beginning with an introduction, the courses address describing data, using Python for data handling, describing relationships, and using Python for data visualisations respectively. Students of Computer Science, Engineering and Technology, and Computer Applications from all of India’s universities are the intended audience for this book, which was developed just for them.



Bisong, E. (2019). Building machine learning and deep learning models on Google cloud platform: A comprehensive guide for beginners. Apress.

Blum, A., Hopcroft, J., & Kannan, R. (2018). Foundations of Data Science (2018). URL: https://www. cs. cornell. edu/jeh/book. pdf.

Cao, L. (2017). Data science: a comprehensive overview. ACM Computing Surveys (CSUR)50(3), 1-42.

De Brouwer, P. J. (2020). The Big R-Book: From Data Science to Learning Machines and Big Data. John Wiley & Sons.

Efron, B., & Hastie, T. (2021). Computer Age Statistical Inference, Student Edition: Algorithms, Evidence, and Data Science (Vol. 6). Cambridge University Press.

Erl, T., Khattak, W., & Buhler, P. (2016). Big data fundamentals: concepts, drivers & techniques. Prentice Hall Press.

Maier-Hein, L., Eisenmann, M., Sarikaya, D., März, K., Collins, T., Malpani, A., … & Speidel, S. (2022). Surgical data science–from concepts toward clinical translation. Medical image analysis76, 102306.

National Academies of Sciences, Engineering, and Medicine. (2018). Data science for undergraduates: Opportunities and options. National Academies Press.

Pierson, L. (2021). Data science for dummies. John Wiley & Sons.

Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big data1(1), 51-59.

Saura, J. R. (2021). Using data sciences in digital marketing: Framework, methods, and performance metrics. Journal of Innovation & Knowledge6(2), 92-102.

Sharda, R., Delen, D., & Turban, E. (2021). Analytics, data science, & artificial intelligence: Systems for decision support. Pearson Education Limited.

Selvan, C., & Balasundaram, S. R. (2021). Data Analysis in Context-Based Statistical Modeling in Predictive Analytics. In Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics (pp. 96-114). IGI Global.

Shwartz-Ziv, R., & Armon, A. (2022). Tabular data: Deep learning is not all you need. Information Fusion81, 84-90.

Singleton, A., & ArribasBel, D. (2021). Geographic data science. Geographical Analysis53(1), 61-75.

Szajowski, K. J. (2017). On a book Algorithms for data science by Brian Steele, John Chandler and Swarn Reddy. Mathematica Applicanda45(2).

Van Der Aalst, W. (2016). Process mining: data science in action (Vol. 2). Heidelberg: Springer.

Van Der Aalst, W. (2016). Process mining: data science in action (Vol. 2). Heidelberg: Springer.



 ConductScience. (2020, January 20). Why do we need data science. Conduct Science.

Kumar, R. (2022, August 31). What is data science & advantages and disadvantages of data science what is data science.

Saradalakshmi8074. (2022, October 3). Data journalism. Medium.

Data science process: A beginner’s guide in plain English. (2022, August 10). Springboard Blog.

Software, E. (2022, September 6). The importance of defining a research goal in a data science project. Expeed Software | Trustworthy Software Solutions.

Chapter 2. The data science process · Introducing data science: Big data, machine learning, and more, using Python tools. (n.d.). liveBook · Manning.

Zubair, M. (2022, October 16). To increase data analysing power you must know frequency distribution (stat-04). Medium.

Raj, A. (2022, August 18). Outlier detection and treatment in data science. CloudyML.

Vijayamohan, P. (2022, September 19). Nominal data 101 – Definition, examples, analysis. SurveySparrow.

Book Citation

Rajavarman.V.N,Gayathri.R, Hemavathi.R.,(2022). Data Science (1st ed., Vol. 1). Magestic Technology Solutions (P) Ltd. ISBN: 978-93-92090-09-7. DOI:

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