What Tools Do Data Scientists Use for Visualization and Analysis?

Visualization and analysis are foundational elements of a data scientist's work. Analysis involves hands-on data processing to extract valuable insights, often related to a business problem.¹ The process typically involves visualization, which represents data in easily digestible graphic formats that showcase patterns.²

Specialized programming languages and tools help data scientists perform these tasks. Some are used for data modeling, which defines the structure of a data set and supports analysis.³ Other tools make communication and decision-making simpler by providing a collaboration platform.

This post will explain what tools data scientists use for analysis and visualization, and how you can expect to use them to enhance your data science skills.

Programming Languages for Analysis

Most data science tools and techniques require some programming knowledge. The three most widely used programming languages in data science are:⁴

• Python: A general-purpose language known for its versatility, straightforward syntax, and extensive libraries.⁵ A Python library is a collection of pre-written modules of code that you can drop into a program⁶
• R: A statistical computing and graphics production language, commonly used in academia. Known for its clear, publication-ready plot graphs⁷
• SQL: A specialized language that lets users manage and extract information from databases. Its relatively simple command style makes it an approachable language for beginning coders⁸

Python Libraries for Visualization and Analysis

Python libraries contain commonly used functions for specific tasks. They provide standardization and reduce the potential for human error in data science techniques, while making the coding process more efficient and accessible. Some libraries are:

• Pandas: A toolkit for data manipulation and cleaning. User-friendly data structures make it easier to handle missing data and prepare messy data for analysis⁹
• NumPy: Short for Numerical Python, a library often used in engineering and scientific analysis. It contains a variety of data structures and functions that do calculations within those structures¹⁰
• Matplotlib: A library of foundational plotting functions that helps users create static or dynamic visualizations, from basic plots to animated charts¹¹
• Seaborn: Builds on Matplotlib and integrates with Pandas to create statistical visualizations. It's valuable for finding trends in large datasets¹²
• Plotly: A graphics library for making interactive, publication-ready charts. It includes modules for all fundamental visualization types, including scatterplots, bubble charts, and histograms¹³
• Scikit-learn: Performs machine learning and modeling tasks, including predictive regressions and classification.¹⁴ It reduces potential error in hand-coding machine learning algorithms and is accessible to users without advanced math knowledge¹⁵

R Packages for Visualization

An R package is a core unit of shareable code, similar to a Python library.¹⁶ Common packages used in data science include:

• ggplot2: Builds statistical visualizations using the grammar of graphics, a framework for constructing complex data representations.¹⁷ The ggplot2 package uses up to seven components, from component data to visual theming¹⁸
• Shiny: Creates interactive web apps for data visualization.¹⁹ App users can manipulate graphs with no coding knowledge
• dplyr & tidyr: Provide pre-written functions that let you work directly with data for easier preprocessing²⁰

Business Intelligence (BI) Tools

Business intelligence is a data-driven discipline that helps organizations make better decisions. It's one of the many career paths that intersect with data science, particularly in the use of visualizations to generate business insights.²¹ Common BI tools for data scientists include:

• Tableau: An advanced data visualization platform that generates powerful interactive dashboards. Teams use Tableau to uncover and share meaningful insights that power operations²²
• Power BI: A Microsoft-powered tool for generating actionable business insights. Organizations may use it alone or as part of the Microsoft Fabric analytics platform, which integrates data manipulation²³
• Looker: A comprehensive BI solution from Google Cloud, offering data modeling and dashboard creation²⁴ 

Big Data and Cloud Analytics Tools

Large-scale data processing requires more computing power. Here are a few tools data scientists use to handle these higher-volume tasks:

• Apache Spark: A large-scale data processing engine that lets you batch and analyze information at scale. This reduces the need to use smaller samples, which can affect results²⁵
• Databricks: A collaborative analytics platform for large organizations. The platform powers everything related to data, including the development of AI applications²⁶
• Google BigQuery / AWS Redshift: Comparable tools for warehousing data in the cloud. AWS Redshift uses a simpler structure and is ideal for stable data sets, while BigQuery provides more automation for flexible data modeling²⁷

Notebook and Workflow Tools

Notebooks are interactive platforms for collaborative analysis. They provide a place to organize and document the process, so team members can optimize their code.²⁸ Tools that support this data science technique include:

• Jupyter Notebooks: An open-source, web-based notebook environment that supports more than 40 languages, including R and Python. They maintain full records of user sessions and support shareable outputs²⁹
• Google Colab: A Google-run service that hosts Jupyter Notebooks with no setup required. It's free and geared towards machine learning and data science students³⁰
• VS Code: A free and lightweight online coding environment with data plugins³¹

Visualization Tools for Machine Learning

Machine learning empowers today's data scientists to get the most out of large data sets. Using advanced data science visualization tools, they leverage frameworks such as deep learning and neural networks to generate valuable predictions and insights. Key tools include:

• Model evaluation plots: Visualizations that help to evaluate the effectiveness of machine learning models. Commonly used evaluation plots include receiver operating characteristic (ROC) curves, confusion matrices, and feature importance analyses³²
• MLflow dashboards: End-to-end experiment tracking tools for machine learning teams³³
• TensorBoard: A toolkit that visualizes the process of training a deep learning algorithm, used to help teams streamline algorithm development³⁴

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