In the dynamic landscape of data-driven decision-making, quantitative data analytics and visualization have emerged as indispensable tools for researchers, analysts, and businesses seeking meaningful insights from vast datasets.
Quantitative data analytics systematically explores numerical data to identify patterns, trends, and relationships. Software is pivotal in this process, enabling researchers to process large datasets efficiently and apply statistical techniques for rigorous analysis.
One such software that excels in quantitative analytics is R, a programming language and environment specifically designed for statistical computing and graphics. With its extensive library of packages, R empowers users to conduct many statistical analyses, from basic descriptive statistics to advanced machine learning algorithms.
However, the true power of quantitative data analytics comes to life through visualization. Visualization tools like Tableau, Power BI, and Python’s Matplotlib provide a visual canvas for data. They transform raw numbers into compelling charts, graphs, and interactive dashboards, making complex patterns accessible to a broader audience. These tools enhance data comprehension and aid in storytelling, allowing analysts to convey their findings in a compelling and easily digestible format.
Moreover, integrating machine learning algorithms with visualization tools amplifies the predictive capabilities of quantitative analytics. Python libraries such as sci-kit-learn and TensorFlow enable users to build predictive models, while tools like Plotly and Seaborn turn these models into visually engaging representations.
List of recommended resources #
For a broad overview #
Data Analytics and Visualization Software: Home
These resource overviews by Duke University Medical Center Library & Archives lists various softwares according to their functions and gives hyperlinks for each software for more detailed understanding.
Quantitative Analysis Guide: Which Statistical Software to Use?
This guide by NYU Libraries gives a comparative overview of various quantitative data analysis softwares like SPSS, SAS, Stata and R among others.
For in depth understanding #
Introduction to Quantitative Research Methods
Mark Balnaves & Peter Caputi incorporate a detective theme throughout their book to show how quantitative methods have been used to solve real-life problems making the book not only very reader-friendly but also very engaging for beginners. The multimedia courseware accompanying the book includes brief video lessons in SPSS using real data sets. The book provides an integrative typology of the different ways of conducting quantitative research.
R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
This book by Hadley Wickham, Mine Cetinkaya-Rundel & Garrett Grolemund gives a practicum of skills for data science with the quantitative data analysis software R and RStudio. It teaches the grammar of graphics, literate programming, and reproducible research to save time.
Case study #
Basic Data Visualization in R and Python
Case Study: New York Taxi Cabs – This case study uses ggplot2 library of R programming language to plot data containing information on every single trip taken with a yellow New York City taxi cab in the month of June, 2015.
Covid-19 Mapping with Microsoft Power BI
This paper by Steven M. Graves and Lauren He uses the data visualization tool of Microsoft Power BI to create a map and dashboard of the data collected on Covid-19.
References #
Technology for Research: Computation and Data Visualization Resources
