A Review of Statistical Methods and Visualization Techniques in Data Science

A Review of Statistical Methods and Visualization Techniques in Data Science

1. Sharwari Kshirsagar ¹  AI & DS (Computer Engineering)

VP’s Kamalnayan Bajaj Institute of Engineering and Technology, Baramati.

2. G.J. Chhajed ²HOD AI &DS (Computer Engineering)

VP’s Kamalnayan Bajaj Institute of Engineering and Technology, Baramati.

3. Monali Rahul Bhosale³ Assistant Professor AI &DS (Computer Engineering)

VP’s Kamalnayan Bajaj Institute of Engineering and Technology, Baramati.

4. Sanika Gonjari ⁴ AI & DS (Computer Engineering)

VP’s Kamalnayan Bajaj Institute of Engineering and Technology, Baramati.

5. Priyanka Pawar⁵ AI & DS (Computer Engineering)

VP’s Kamalnayan Bajaj Institute of Engineering and Technology, Baramati.

Abstract - Data science is the study of analyzing, visualizing, and interpreting data to make informed decisions. This discipline uses statistical methods, machine learning algorithms, and visualization tools to extract meaningful patterns from raw data. By cleaning, organizing, and analyzing datasets, data science helps solve complex problems across various fields, including healthcare, finance, and business. This paper reviews three studies focusing on statistical techniques, visualization methods, and their integration in data workflows. The importance of combining qualitative and quantitative approaches is discussed, alongside visualization tools like scatter plots, heatmaps, and line charts, which simplify complex data. This synthesis highlights the role of data science in identifying trends, improving decision-making, and addressing challenges in modern datasets.

Key Words:  Data Science, Statistical Methods, Data Visualization, Machine Learning, Quantitative Analysis, Qualitative Analysis