Existing scientific visualization tools have specific limitations for large scale scientific data sets. Of these four limitations can be seen as paramount: (i) memory management, (ii) remote visualization, (iii) interactivity, and (iv) specificity. In Phase I, we proposed and successfully developed a prototype of a collection of computer tools and libraries called SciViz that overcome these limitations and enable researchers to visualize large scale data sets (greater than 200 gigabytes) on HPC resources remotely from their workstations at interactive rates. A key element of our technology is the stack oriented rather than a framework driven approach which allows it to interoperate with common existing scientific visualization software thereby eliminating the need for the user to switch and learn new software. The result is a versatile 3D visualization capability that will significantly decrease the time to knowledge discovery from large, complex data sets.

Typical visualization activity can be organized into a simple stack of steps that leads to the visualization result. These steps can broadly be classified into data retrieval, data analysis, visual representation, and rendering. Our approach will be to continue with the technique selected in Phase I of utilizing existing visualization tools at each point in the visualization stack and to develop specific tools that address the core limitations identified and seamlessly integrate them into the visualization stack. Specifically, we intend to complete technical objectives in four areas that will complete the development of visualization tools for interactive visualization of very large data sets in each layer of the visualization stack. These four areas are: Feature Objectives, C++ Conversion and Optimization, Testing Objectives, and Domain Specifics and Integration. The technology will be developed and tested at NASA and the San Diego Supercomputer Center.

This dataset compiles the top 2500 datasets from Kaggle, encompassing a diverse range of topics and contributors. It provides insights into dataset creation, usability, popularity, and more, offering valuable information for researchers, analysts, and data enthusiasts.

Research Analysis: Researchers can utilize this dataset to analyze trends in dataset creation, popularity, and usability scores across various categories.

Contributor Insights: Kaggle contributors can explore the dataset to gain insights into factors influencing the success and engagement of their datasets, aiding in optimizing future submissions.

Machine Learning Training: Data scientists and machine learning enthusiasts can use this dataset to train models for predicting dataset popularity or usability based on features such as creator, category, and file types.

Market Analysis: Analysts can leverage the dataset to conduct market analysis, identifying emerging trends and popular topics within the data science community on Kaggle.

Educational Purposes: Educators and students can use this dataset to teach and learn about data analysis, visualization, and interpretation within the context of real-world datasets and community-driven platforms like Kaggle.

Column Definitions:

Dataset Name: Name of the dataset. Created By: Creator(s) of the dataset. Last Updated in number of days: Time elapsed since last update. Usability Score: Score indicating the ease of use. Number of File: Quantity of files included. Type of file: Format of files (e.g., CSV, JSON). Size: Size of the dataset. Total Votes: Number of votes received. Category: Categorization of the dataset's subject matter.

As high-throughput methods become more common, training undergraduates to analyze data must include having them generate informative summaries of large datasets. This flexible case study provides an opportunity for undergraduate students to become familiar with the capabilities of R programming in the context of high-throughput evolutionary data collected using macroarrays. The story line introduces a recent graduate hired at a biotech firm and tasked with analysis and visualization of changes in gene expression from 20,000 generations of the Lenski Lab’s Long-Term Evolution Experiment (LTEE). Our main character is not familiar with R and is guided by a coworker to learn about this platform. Initially this involves a step-by-step analysis of the small Iris dataset built into R which includes sepal and petal length of three species of irises. Practice calculating summary statistics and correlations, and making histograms and scatter plots, prepares the protagonist to perform similar analyses with the LTEE dataset. In the LTEE module, students analyze gene expression data from the long-term evolutionary experiments, developing their skills in manipulating and interpreting large scientific datasets through visualizations and statistical analysis. Prerequisite knowledge is basic statistics, the Central Dogma, and basic evolutionary principles. The Iris module provides hands-on experience using R programming to explore and visualize a simple dataset; it can be used independently as an introduction to R for biological data or skipped if students already have some experience with R. Both modules emphasize understanding the utility of R, rather than creation of original code. Pilot testing showed the case study was well-received by students and faculty, who described it as a clear introduction to R and appreciated the value of R for visualizing and analyzing large datasets.

Displaying the component-wise between-group differences high-dimensional datasets is problematic because widely used plots such as Bland–Altman and Volcano plots do not show what they are colloquially believed to show. Thus, it is difficult for the experimentalist to grasp why the between-group difference of one component is “significant” while that of another component is not. Here, we propose a type of “Effect Plot” that displays between-group differences in relation to respective underlying variability for every component of a high-dimensional dataset. We use synthetic data to show that such a plot captures the essence of what determines “significance” for between-group differences in each component, and provide guidance in the interpretation of the plot. Supplementary online materials contain the code and data for this article and include simple R functions to produce an effect plot from suitable datasets.

Description: - This dataset includes all 22 built-in datasets from the Seaborn library, a widely used Python data visualization tool. Seaborn's built-in datasets are essential resources for anyone interested in practicing data analysis, visualization, and machine learning. They span a wide range of topics, from classic datasets like the Iris flower classification to real-world data such as Titanic survival records and diamond characteristics.

• Included Datasets:
  • Anagrams: Analysis of word anagram patterns.
  • Anscombe: Anscombe's quartet demonstrating the importance of data visualization.
  • Attention: Data on attention span variations in different scenarios.
  • Brain Networks: Connectivity data within brain networks.
  • Car Crashes: US car crash statistics.
  • Diamonds: Data on diamond properties including price, cut, and clarity.
  • Dots: Randomly generated data for scatter plot visualization.
  • Dow Jones: Historical records of the Dow Jones Industrial Average.
  • Exercise: The relationship between exercise and health metrics.
  • Flights: Monthly passenger numbers on flights.
  • FMRI: Functional MRI data capturing brain activity.
  • Geyser: Eruption times of the Old Faithful geyser.
  • Glue: Strength of glue under different conditions.
  • Health Expenditure: Health expenditure statistics across countries.
  • Iris: Famous dataset for classifying Iris species.
  • MPG: Miles per gallon for various vehicles.
  • Penguins: Data on penguin species and their features.
  • Planets: Characteristics of discovered exoplanets.
  • Sea Ice: Measurements of sea ice extent.
  • Taxis: Taxi trips data in a city.
  • Tips: Tipping data collected from a restaurant.
  • Titanic: Survival data from the Titanic disaster.

This complete collection serves as an excellent starting point for anyone looking to improve their data science skills, offering a wide array of datasets suitable for both beginners and advanced users.

Data Visualization Software Market Outlook

According to our latest research, the global Data Visualization Software market size reached USD 8.2 billion in 2024, reflecting the sectorÂ’s rapid adoption across industries. With a robust CAGR of 10.8% projected from 2025 to 2033, the market is expected to grow significantly, attaining a value of USD 20.3 billion by 2033. This dynamic expansion is primarily driven by the increasing demand for actionable business insights, the proliferation of big data analytics, and the growing need for real-time decision-making tools across enterprises worldwide.

One of the most powerful growth factors for the Data Visualization Software market is the surge in big data generation and the corresponding need for advanced analytics solutions. Organizations are increasingly dealing with massive and complex datasets that traditional reporting tools cannot handle efficiently. Modern data visualization software enables users to interpret these vast datasets quickly, presenting trends, patterns, and anomalies in intuitive graphical formats. This empowers organizations to make informed decisions faster, boosting overall operational efficiency and competitive advantage. Furthermore, the integration of artificial intelligence and machine learning capabilities into data visualization platforms is enhancing their analytical power, allowing for predictive and prescriptive insights that were previously unattainable.

Another significant driver of the Data Visualization Software market is the widespread digital transformation initiatives across various sectors. Enterprises are investing heavily in digital technologies to streamline operations, improve customer experiences, and unlock new revenue streams. Data visualization tools have become integral to these transformations, serving as a bridge between raw data and strategic business outcomes. By offering interactive dashboards, real-time reporting, and customizable analytics, these solutions enable users at all organizational levels to engage with data meaningfully. The democratization of data access facilitated by user-friendly visualization software is fostering a data-driven culture, encouraging innovation and agility across industries such as BFSI, healthcare, retail, and manufacturing.

The increasing adoption of cloud-based data visualization solutions is also fueling market growth. Cloud deployment offers scalability, flexibility, and cost-effectiveness, making advanced analytics accessible to organizations of all sizes, including small and medium enterprises (SMEs). Cloud-based platforms support seamless integration with other business applications, facilitate remote collaboration, and provide robust security features. As businesses continue to embrace remote and hybrid work models, the demand for cloud-based data visualization tools is expected to rise, further accelerating market expansion. Vendors are responding with enhanced offerings, including AI-driven analytics, embedded BI, and self-service visualization capabilities, catering to the evolving needs of modern enterprises.

In the realm of warehouse management systems (WMS), the integration of WMS Data Visualization Tools is becoming increasingly vital. These tools offer a comprehensive view of warehouse operations, enabling managers to visualize data related to inventory levels, order processing, and shipment tracking in real-time. By leveraging advanced visualization techniques, WMS data visualization tools help in identifying bottlenecks, optimizing resource allocation, and improving overall efficiency. The ability to transform complex data sets into intuitive visual formats empowers warehouse managers to make informed decisions swiftly, thereby enhancing productivity and reducing operational costs. As the demand for streamlined logistics and supply chain management continues to grow, the adoption of WMS data visualization tools is expected to rise, driving further innovation in the sector.

Regionally, North America continues to dominate the Data Visualization Software market due to early technology adoption, a strong presence of leading vendors, and a mature analytics landscape. However, the Asia Pacific region is witnessing the fastest growth, driven by rapid digitalization, increasing IT investments, and the emergence of data-centric business models in countries like China, India

Set Visualization Tools Market Outlook

According to our latest research, the global set visualization tools market size reached USD 3.6 billion in 2024, with a robust year-over-year growth driven by the surging demand for advanced data analysis and visualization solutions across industries. The market is projected to expand at a CAGR of 11.7% from 2025 to 2033, reaching a forecasted value of USD 10.1 billion by 2033. This remarkable growth trajectory is primarily attributed to the increasing adoption of big data analytics, artificial intelligence, and digital transformation initiatives among enterprises, government bodies, and academic institutions worldwide.

One of the primary growth factors for the set visualization tools market is the escalating volume, velocity, and variety of data generated across sectors such as business intelligence, scientific research, and education. Organizations are increasingly recognizing the value of transforming complex, multidimensional datasets into intuitive, interactive visual representations to facilitate better decision-making, uncover hidden insights, and drive operational efficiency. The proliferation of IoT devices, cloud computing, and advanced analytics platforms has further amplified the need for sophisticated set visualization tools that can seamlessly integrate with existing data ecosystems, enabling users to analyze relationships, intersections, and trends within large, heterogeneous datasets.

Another significant driver propelling the market growth is the rapid digitalization of enterprises and the growing emphasis on data-driven strategies. Businesses are leveraging set visualization tools to enhance their business intelligence capabilities, monitor key performance indicators, and gain a competitive edge in an increasingly data-centric landscape. These tools empower organizations to visualize overlaps, gaps, and anomalies in data sets, supporting functions such as market segmentation, customer profiling, and risk management. As companies continue to invest in advanced analytics and visualization solutions, the demand for customizable, scalable, and user-friendly set visualization platforms is poised to witness sustained growth throughout the forecast period.

Furthermore, the integration of artificial intelligence and machine learning algorithms into set visualization tools is revolutionizing the market, enabling automated pattern recognition, predictive analytics, and real-time data exploration. This technological evolution is not only enhancing the accuracy and efficiency of data analysis but also democratizing access to complex analytical capabilities for non-technical users. The growing focus on enhancing user experience, interoperability, and cross-platform compatibility is fostering innovation and differentiation among solution providers, further accelerating market expansion. Additionally, the increasing adoption of remote and hybrid work models is driving demand for cloud-based visualization tools that offer flexibility, scalability, and collaborative features.

From a regional perspective, North America currently dominates the set visualization tools market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The strong presence of leading technology vendors, high digital adoption rates, and significant investments in data analytics infrastructure are key factors underpinning North America's leadership. Meanwhile, Asia Pacific is emerging as the fastest-growing region, fueled by rapid digital transformation, expanding enterprise IT budgets, and a burgeoning ecosystem of startups and academic institutions. As organizations across all regions continue to prioritize data-driven decision-making, the global set visualization tools market is expected to maintain its upward momentum over the coming years.

Component Analysis

The set visualization tools market by component is primarily segmented into software and services, each playing a pivotal role in the overall ecosystem. Software solutions dominate the market, driven by the continuous evolution of visualization platforms that offer advanced features such as dynamic dashboards, drag-and-drop interfaces, and integration with diverse data sources. Vendors are focusing on enhancing the scalability, security, and customization capabilities of their software offerings to cater to the unique requirements of various industries. The growing trend of self-service analytics is further boo

Prelude

This dataset is a cleaned version of the Chicago Crime Dataset, which can be found here. All rights for the dataset go to the original owners. The purpose of this dataset is to display my skills in visualizations and creating dashboards. To be specific, I will attempt to create a dashboard that will allow users to see metrics for a specific crime within a given year using filters and metrics. Due to this, there will not be much of a focus on the analysis of the data, but there will be portions discussing the validity of the dataset, the steps I took to clean the data, and how I organized it. The cleaned datasets can be found below, the Query (which utilized BigQuery) can be found here and the Tableau dashboard can be found here.

About the Dataset

Important Facts

The dataset comes directly from the City of Chicago's website under the page "City Data Catalog." The data is gathered directly from the Chicago Police's CLEAR (Citizen Law Enforcement Analysis and Reporting) and is updated daily to present the information accurately. This means that a crime on a specific date may be changed to better display the case. The dataset represents crimes starting all the way from 2001 to seven days prior to today's date.

Reliability

Using the ROCCC method, we can see that: * The data has high reliability: The data covers the entirety of Chicago from a little over 2 decades. It covers all the wards within Chicago and even gives the street names. While we may not have an idea for how big the sample size is, I do believe that the dataset has high reliability since it geographically covers the entirety of Chicago. * The data has high originality: The dataset was gained directly from the Chicago Police Dept. using their database, so we can say this dataset is original. * The data is somewhat comprehensive: While we do have important information such as the types of crimes committed and their geographic location, I do not think this gives us proper insights as to why these crimes take place. We can pinpoint the location of the crime, but we are limited by the information we have. How hot was the day of the crime? Did the crime take place in a neighborhood with low-income? I believe that these key factors prevent us from getting proper insights as to why these crimes take place, so I would say that this dataset is subpar with how comprehensive it is. * The data is current: The dataset is updated frequently to display crimes that took place seven days prior to today's date and may even update past crimes as more information comes to light. Due to the frequent updates, I do believe the data is current. * The data is cited: As mentioned prior, the data is collected directly from the polices CLEAR system, so we can say that the data is cited.

Processing the Data

Cleaning the Dataset

The purpose of this step is to clean the dataset such that there are no outliers in the dashboard. To do this, we are going to do the following: * Check for any null values and determine whether we should remove them. * Update any values where there may be typos. * Check for outliers and determine if we should remove them.

The following steps will be explained in the code segments below. (I used BigQuery for this so the coding will follow BigQuery's syntax) ```

Examining the dataset

There are over 7.5 million rows of data

Putting a limit so it does not take a long time to run

SELECT * FROM portfolioproject-350601.ChicagoCrime.Crime LIMIT 1000;

Seeing which points are null

There are 85,000 null points so we can exclude them as it's not a significant amount since it is only ~1.3% of the dataset

Most of the null points are in the lat and long, which we will need later

Because we don't have the full address, we can't estimate the lat and long in SQL so we will have to delete the rows with Null Data

SELECT * FROM portfolioproject-350601.ChicagoCrime.Crime WHERE unique_key IS NULL OR case_number IS NULL OR date IS NULL OR primary_type IS NULL OR location_description IS NULL OR arrest IS NULL OR longitude IS NULL OR latitude IS NULL;

Deleting all null rows

DELETE FROM portfolioproject-350601.ChicagoCrime.Crime WHERE
unique_key IS NULL OR case_number IS NULL OR date IS NULL OR primary_type IS NULL OR location_description IS NULL OR arrest IS NULL OR longitude IS NULL OR latitude IS NULL;

Checking for any duplicates in the unique keys

None to be found

SELECT unique_key, COUNT(unique_key) FROM `portfolioproject-350601.ChicagoCrime....

PARAMOUNT: parallel modal analysis of large datasets

PARAMOUNT is a python package developed at University of Twente to perform modal analysis of large numerical and experimental datasets. Brief video introduction into the theory and methodology is presented _here.

Features

- Distributed processing of data on local machines or clusters using Dask Distributed
- Reading CSV files in glob format from specified folders
- Extracting relevant columns from CSV files and writing Parquet database for each specified variable
- Distributed computation of Proper Orthogonal Decomposition (POD)
- Writing U, S and V matrices into Parquet database for further analysis
- Visualizing POD modes and coefficients using pyplot

Using PARAMOUNT

Make sure to install the dependencies by running `pip install -r requirements.txt`

Refer to csv_example to see how to use PARAMOUNT to read CSV files, write the variables of interest into Parquet datasets and inspect the final datasets.

Refer to svd_example to see how to read Parquet datasets, compute the Singular Value Decomposition, and store the results in Parquet format.

To visualize the results you can simply read the U, S and V parquet files and your plotting tool of choice. Examples are provided in viz_example.

Author and Acknowledgements

This package is developed by Alireza Ghasemi (alireza.ghasemi@utwente.nl) at University of Twente under the MAGISTER (https://www.magister-itn.eu/) project. This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 766264.

Big Data in Healthcare Market Outlook

The global market size for Big Data in Healthcare was valued at approximately USD 32.9 billion in 2023 and is projected to reach around USD 105.7 billion by 2032, growing at a compound annual growth rate (CAGR) of 14.1% from 2024 to 2032. This rapid expansion is driven by several growth factors including the increasing adoption of data-driven decision-making processes, the integration of advanced technologies such as AI and machine learning, and the rising demand for personalized medicine and advanced patient care.

One of the key growth factors driving the Big Data in Healthcare market is the increasing need for cost-effective treatment options and improved patient outcomes. Healthcare providers are increasingly turning to Big Data analytics to optimize their clinical workflows, reduce operational costs, and enhance the quality of care. By leveraging large datasets, healthcare professionals can identify patterns and trends that inform more accurate diagnoses, personalized treatment plans, and better patient management strategies. This transformation in healthcare delivery is expected to contribute significantly to the market's growth over the forecast period.

Another significant growth factor is the rising prevalence of chronic diseases and the need for effective disease management. Chronic conditions such as diabetes, cardiovascular diseases, and cancer require ongoing monitoring and management, which generates vast amounts of data. Big Data analytics enables the analysis of these datasets to predict disease outbreaks, monitor patient adherence to treatment plans, and improve overall disease management. The growing emphasis on preventative healthcare and early diagnosis is further propelling the demand for Big Data analytics solutions in the healthcare sector.

Moreover, technological advancements and the increasing integration of Artificial Intelligence (AI) and machine learning (ML) into Big Data analytics are fostering market growth. AI and ML algorithms can analyze massive datasets at high speeds, uncovering insights that would be impossible to detect manually. These technologies enhance predictive analytics, clinical decision support systems, and personalized medicine, thereby driving the adoption of Big Data solutions in healthcare. The continuous development of these technologies and their application in healthcare analytics are expected to significantly boost market growth.

The regional outlook for Big Data in Healthcare indicates substantial growth across various regions, with North America leading the market due to its advanced healthcare infrastructure and high adoption rate of innovative technologies. Europe follows closely, driven by government initiatives to promote digital health and data analytics. The Asia Pacific region is expected to witness the highest growth rate, attributed to the increasing investments in healthcare infrastructure, the rising prevalence of chronic diseases, and the growing adoption of digital health solutions. Latin America and the Middle East & Africa are also expected to experience significant growth, albeit at a slower pace, due to improving healthcare systems and increasing awareness of Big Data benefits.

Component Analysis

The Big Data in Healthcare market is segmented by component into Software, Hardware, and Services. The software segment holds the largest market share, driven by the increasing demand for advanced analytics tools and platforms that facilitate data-driven decision-making in healthcare. Software solutions enable healthcare providers to collect, analyze, and visualize large datasets, improving clinical outcomes and operational efficiency. The continuous development of sophisticated analytics software and the integration of AI and ML capabilities are expected to further boost the growth of this segment.

The hardware segment, while smaller in comparison to software, plays a crucial role in the Big Data in Healthcare market. Hardware components such as servers, storage devices, and networking equipment are essential for the collection, storage, and processing of vast amounts of healthcare data. With the increasing volume of data generated by healthcare applications, there is a growing need for high-performance hardware solutions that can handle large-scale data analytics tasks. The development of advanced hardware technologies and the increasing adoption of edge computing in healthcare are expected to drive

You have 5 '.xls' by the name savedrecs The files contain articles related to the chemistry with focus in ML and AI topics. Besides it, you have 2 extra files for your interpretation. One of the significances of this dataset is to teach all different methodologies for various kinds of data in one dataset. Another importance is to deal with novel data. Therefore this dataset presents a progression in your career steps. Below are the steps you should be able to take on the provided datasets 1. apply the appropriate concatenation method for joining the given files. 2. transform the categorical data into numerical ones with a suitable strategy. 3. decide which features are significant for the aim of the described scenario. 4. select the required features of the dataset. 5. investigate the correct strategy for filling nan values of the dataset. 6. demonstrate an understandable visualization for the time series. 7. develop a new column using the existing columns according to the purpose of the scenario. 8. interpret and appraise the dataset. 9. apply the methodology for handling the textual data. 10. convert the textual data to numerical data form. 11. present what (s)he did throughout his study

Hi Folks,

Let's understand the importance of Data Visualization.

Here below, we have four different data sets and they are paired in the sense of x and y.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12425689%2F4f6c696e3ad5e2c887b01a0bdd14b355%2Fdata_set.png?generation=1685190700223447&alt=media" alt="">

Next let's calculate some descriptive statistics such as mean, standard deviation and correlation of each variables.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12425689%2F14765ba12bdc18b8ff67cb6a9f2d7c7a%2Fstatistics.png?generation=1685192394142325&alt=media" alt="">

After examining the descriptive statistics the above four data sets have nearly identical or similar simple descriptive statistics.

However, when we graphically plot the datasets on scatter plot, we can see the difference that these 4 datasets looks very different.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12425689%2Fdbccf9dc638d3de28930b9f660e5f5a4%2Fgarph.png?generation=1685191588780934&alt=media" alt="">

Data 1 has a clear linear relationship, Data 2 has a curved relationship that is not linear, Data 3 has a tight linear relationship with one outlier and Data 4 has a linear relationship with one large outlier.

Such datasets are known as Anscombe's Quartet

Anscombe's quartet is a classic example of the importance of data visualization.

Anscombe's quartet is a set of four datasets that have nearly identical simple descriptive statistics, yet have very different distributions and appear very different when graphically represented. Each dataset consists of eleven (x,y) points.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12425689%2F2b964d437afe17db949c57988b5fba05%2Fanscombes_quartet.png?generation=1685192626504792&alt=media" alt="">

Anscombe's quartet illustrates the importance of plotting data before we analyze it. Descriptive statistics can be misleading, and they can't tell us everything we need to know about a dataset. Plotting the data on charts can help us to understand the shape of the distribution and to identify any outliers.

Big Data Management Market Outlook

The global big data management market size was valued at approximately USD 45 billion in 2023 and is projected to reach around USD 150 billion by 2032, growing at a compound annual growth rate (CAGR) of 14.5% over the forecast period. The primary growth factor driving this market is the exponential increase in data generation across various industries, coupled with the rising need for data-driven decision-making processes.

The growth of the big data management market is significantly influenced by the surge in digital transformation initiatives across diverse industry verticals. Organizations are increasingly adopting advanced analytics and big data technologies to enhance operational efficiency, improve customer experience, and gain competitive advantages. This digital transformation is leading to massive data generation, necessitating robust big data management solutions to manage, store, and analyze this data effectively. Furthermore, the advent of technologies such as the Internet of Things (IoT), artificial intelligence (AI), and machine learning (ML) has further fueled the demand for big data management solutions.

Another critical growth factor for the big data management market is the increasing adoption of cloud-based solutions. Cloud computing offers scalable and flexible infrastructure, enabling organizations to handle large volumes of data without significant capital investment in physical hardware. The migration of data and applications to the cloud has made it easier for businesses to implement big data analytics, thereby driving market growth. Additionally, the growing trend of hybrid cloud adoption is providing organizations with the flexibility to manage their data across on-premises and cloud environments, further boosting the demand for big data management solutions.

The rising demand for real-time data analytics is also a significant driver for the big data management market. Organizations are increasingly recognizing the importance of real-time insights to make informed decisions, optimize operations, and enhance customer experiences. Real-time data analytics enables businesses to analyze data as it is generated, allowing for quicker response times and improved agility. This demand for real-time analytics is pushing organizations to invest in sophisticated big data management tools and technologies that can efficiently handle and process large datasets in real-time.

From a regional perspective, North America holds a significant share of the big data management market, primarily due to the early adoption of advanced technologies and the presence of major market players in the region. The Asia Pacific region is expected to witness substantial growth during the forecast period, driven by the increasing digitalization initiatives, rapid economic development, and the growing adoption of big data analytics across various industries. Europe is also a significant market for big data management, with strong adoption across sectors such as BFSI, healthcare, and manufacturing.

Component Analysis

The big data management market is segmented into software and services based on components. The software segment holds a significant share and includes various tools and platforms designed to manage, store, analyze, and visualize large datasets. This segment is driven by the continuous advancements in big data technologies, such as data lakes, data warehouses, and data analytics platforms. These software solutions help organizations derive actionable insights from their data, leading to better decision-making and improved operational efficiency. The increasing demand for advanced analytics and data visualization tools is further propelling the growth of the software segment.

The services segment encompasses a wide range of offerings, including consulting, implementation, training, and support services. As organizations increasingly adopt big data solutions, there is a growing need for expert guidance to effectively implement and manage these technologies. Consulting services help businesses develop robust data strategies, while implementation services ensure seamless integration of big data solutions into existing IT infrastructure. Additionally, training and support services are crucial for empowering employees with the necessary skills to leverage big data tools effectively. The services segment is expected to witness robust growth due to the increasing demand for professional services that facilit

This page links to the data associated with the publication "ToxicoDB: an integrated database to mine and visualize large-scale toxicogenomic datasets ". The data have been curated and analyzed using our open-source R package, ToxicoGx (https://github.com/bhklab/ToxicoGx) and are available publicly in the ToxicoDB web application (www.toxicodb.ca). Please see the included DOIs below, or download the .csv file which contains the names, dates and DOIs of all datasets listed here.

The TGGATES data was generated by Igarashi Y, Nakatsu N, Yamashita T, Ono A, Ohno Y, Urushidani T, Yamada H. Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids Res [Internet]. 2015 Jan;43(Database issue):D921–7. Available from: http://dx.doi.org/10.1093/nar/gku955 PMCID: PMC4384023.

Data:

TGGATEs humanldh (https://doi.org/10.5281/zenodo.3762812)

TGGATEs humandna (https://doi.org/10.5281/zenodo.4024859)

TGGATEs ratldh (https://doi.org/10.5281/zenodo.3762817)

TGGATEs ratdna (https://doi.org/10.5281/zenodo.4024918)

This Drug Matrix data was generated by Ganter B, Snyder RD, Halbert DN, Lee MD. Toxicogenomics in drug discovery and development: mechanistic analysis of compound/class-dependent effects using the DrugMatrix database. Pharmacogenomics [Internet]. 2006 Oct;7(7):1025–1044. Available from: http://dx.doi.org/10.2217/14622416.7.7.1025 PMID: 17054413.

Data:

Drug Matrix (https://doi.org/10.5281/zenodo.3766569)

AI in Data Visualization Market Outlook

According to our latest research, the global AI in Data Visualization market size reached $3.8 billion in 2024, demonstrating robust growth as organizations increasingly leverage artificial intelligence to enhance data-driven decision-making. The market is forecasted to expand at a CAGR of 21.1% from 2025 to 2033, reaching an estimated $26.6 billion by 2033. This exceptional growth is fueled by the rising demand for actionable insights, the proliferation of big data, and the integration of AI technologies to automate and enrich data visualization processes across industries.

A primary growth factor in the AI in Data Visualization market is the exponential increase in data generation from various sources, including IoT devices, social media platforms, and enterprise systems. Organizations face significant challenges in interpreting complex datasets, and AI-powered visualization tools offer a solution by transforming raw data into intuitive, interactive visual formats. These solutions enable businesses to quickly identify trends, patterns, and anomalies, thereby improving operational efficiency and strategic planning. The integration of AI capabilities such as natural language processing, machine learning, and automated analytics further enhances the value proposition, allowing users to generate dynamic visualizations with minimal technical expertise.

Another significant driver is the growing adoption of business intelligence and analytics platforms across diverse sectors such as BFSI, healthcare, retail, and manufacturing. As competition intensifies and consumer expectations evolve, enterprises are prioritizing data-driven decision-making to gain a competitive edge. AI in data visualization solutions empower users at all organizational levels to interact with data in real-time, uncover hidden insights, and make informed decisions rapidly. The shift towards self-service analytics, where non-technical users can generate their own reports and dashboards, is accelerating the uptake of AI-driven visualization tools. This democratization of data access is expected to continue propelling the market forward.

The rapid advancements in cloud computing and the increasing adoption of cloud-based analytics platforms are also contributing to the growth of the AI in Data Visualization market. Cloud deployment offers scalability, flexibility, and cost-effectiveness, enabling organizations to process and visualize vast volumes of data without substantial infrastructure investments. Additionally, cloud-based solutions facilitate seamless integration with other enterprise applications and data sources, supporting real-time analytics and collaboration across geographically dispersed teams. As more organizations transition to hybrid and multi-cloud environments, the demand for AI-powered visualization tools that can operate efficiently in these settings is poised to surge.

From a regional perspective, North America currently dominates the AI in Data Visualization market due to the presence of leading technology providers, high digital adoption rates, and significant investments in AI and analytics. However, the Asia Pacific region is anticipated to witness the fastest growth over the forecast period, driven by rapid digitalization, expanding IT infrastructure, and increasing awareness of the benefits of AI-driven data visualization. Europe is also expected to see substantial adoption, particularly in industries such as finance, healthcare, and manufacturing, where regulatory compliance and data-driven strategies are critical. Meanwhile, emerging markets in Latin America and the Middle East & Africa are gradually embracing these technologies as digital transformation initiatives gain momentum.

Component Analysis

The Component segment of the AI in Data Visualization market is bifurcated into Software and Services, each playing a pivotal role in shaping the industry landscape. Software solutions encompass a wide array of platforms and tools that leverage AI algorithms to automate, enhance, and personalize data visualization. These solutions are designed to cater to varying business needs, from simple dashboard creation to advanced predictive analytics and real-time data exploration. The software segment is witnessing rapid innovation, with vendors continuously integrating new AI capabilities such as natural language queries, automated anomaly detection, and adaptive visualization techniques. This has significantly reduced the learning

This data was generated by Igarashi Y, Nakatsu N, Yamashita T, Ono A, Ohno Y, Urushidani T, Yamada H. Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids Res [Internet]. 2015 Jan;43(Database issue):D921–7. Available from: http://dx.doi.org/10.1093/nar/gku955 PMCID: PMC4384023. The data have been curated and analyzed using our open-source R package, ToxicoGx (https://bioconductor.org/packages/devel/bioc/html/ToxicoGx.html), and are available publicly in the ToxicoDB web application (www.toxicodb.ca).

Background In microarray data analysis, the comparison of gene-expression profiles with respect to different conditions and the selection of biologically interesting genes are crucial tasks. Multivariate statistical methods have been applied to analyze these large datasets. Less work has been published concerning the assessment of the reliability of gene-selection procedures. Here we describe a method to assess reliability in multivariate microarray data analysis using permutation-validated principal components analysis (PCA). The approach is designed for microarray data with a group structure. Results We used PCA to detect the major sources of variance underlying the hybridization conditions followed by gene selection based on PCA-derived and permutation-based test statistics. We validated our method by applying it to well characterized yeast cell-cycle data and to two datasets from our laboratory. We could describe the major sources of variance, select informative genes and visualize the relationship of genes and arrays. We observed differences in the level of the explained variance and the interpretability of the selected genes. Conclusions Combining data visualization and permutation-based gene selection, permutation-validated PCA enables one to illustrate gene-expression variance between several conditions and to select genes by taking into account the relationship of between-group to within-group variance of genes. The method can be used to extract the leading sources of variance from microarray data, to visualize relationships between genes and hybridizations and to select informative genes in a statistically reliable manner. This selection accounts for the level of reproducibility of replicates or group structure as well as gene-specific scatter. Visualization of the data can support a straightforward biological interpretation.

Ferret is an interactive computer visualization and analysis environment designed to meet the needs of oceanographers and meteorologists analyzing large and complex gridded data sets. It runs on recent Unix and Mac systems, using X windows for display. PyFerret, introduced in 2012, is a Python module wrapping Ferret. The pyferret module provides Python functions so Python users can easily take advantage of Ferret's abilities to retrieve, manipulate, visualize, and save data.

Ferret and PyFerret can transparently access extensive remote Internet data sources using OPeNDAP; see http://opendap.org and http://www.unidata.ucar.edu/publications/directorspage/UnidataOverview.html

Ferret was developed by the Thermal Modeling and Analysis Project (TMAP) at PMEL in Seattle to analyze the outputs of its numerical ocean models and compare them with gridded, observational data. The model data sets are generally multi-gigabyte in size with mixed multi-dimensional variables defined on staggered grids. Ferret offers a Mathematica-like approach to analysis; new variables may be defined interactively as mathematical expressions involving data set variables. Calculations may be applied over arbitrarily shaped regions. Fully documented graphics are produced with a single command.

Many excellent software packages have been developed recently for scientific visualization. The features that make Ferret distinctive among these packages are Mathematica-like flexibility, geophysical formatting, "intelligent" connection to its data base, memory management for very large calculations, and symmetrical processing in 6 dimensions.

Ferret is widely used in the oceanographic community to analyze data and create publication quality graphics. We have compiled an (incomplete) list of publications where the authors felt that the contribution of Ferret was sufficient to warrant an acknowledgment. We appreciate your acknowledgment of Ferret in your publications. Here is a suggested acknowledgment that you may use.

Graph Data Science Platform Market Outlook

The global Graph Data Science Platform market size reached USD 2.9 billion in 2024, with a robust year-on-year growth reflecting the increasing adoption of advanced analytics across industries. According to our latest research, the market is projected to expand at a CAGR of 32.1% from 2025 to 2033, reaching an estimated USD 32.8 billion by 2033. This remarkable growth trajectory is primarily driven by the rising need for sophisticated data analysis tools capable of uncovering complex relationships in large datasets, coupled with the proliferation of big data and artificial intelligence technologies.

Several key factors are fueling the rapid expansion of the Graph Data Science Platform market. Firstly, organizations across sectors such as BFSI, healthcare, and retail are increasingly leveraging graph data science to derive actionable insights from interconnected data. The ability of these platforms to efficiently model, analyze, and visualize relationships among data points is revolutionizing fraud detection, recommendation systems, and customer analytics. As digital transformation accelerates, enterprises are seeking more advanced solutions to manage and extract value from their growing volumes of structured and unstructured data. This shift is propelling the demand for graph-based analytics, which offer significant advantages over traditional relational databases in terms of flexibility and scalability.

Another significant growth driver for the Graph Data Science Platform market is the integration of artificial intelligence and machine learning capabilities within graph analytics solutions. The convergence of AI with graph data science is enabling businesses to automate complex analytical tasks, enhance predictive accuracy, and identify hidden patterns within massive datasets. This trend is particularly evident in applications such as fraud detection, risk management, and supply chain optimization, where real-time analysis of data relationships is critical. Moreover, the increasing availability of cloud-based graph data science platforms is lowering the barriers to adoption, providing organizations of all sizes with scalable, cost-effective access to advanced analytics tools.

The evolution of regulatory frameworks and heightened focus on data privacy and compliance are also shaping the Graph Data Science Platform market. As organizations face stricter regulations concerning data governance and security, graph data science platforms are being enhanced with robust compliance features, including data lineage tracking, access controls, and audit trails. This is particularly relevant for industries such as BFSI and healthcare, where regulatory compliance is paramount. The ability of graph platforms to provide transparent, auditable insights into data relationships is emerging as a key differentiator, further driving market adoption.

Regionally, North America continues to dominate the Graph Data Science Platform market, accounting for the largest share in 2024, driven by the presence of leading technology providers, high digital maturity, and early adoption of advanced analytics. However, Asia Pacific is emerging as the fastest-growing region, fueled by rapid digitalization, expanding IT infrastructure, and increasing investments in artificial intelligence and big data analytics. Europe also holds a significant market share, supported by strong demand from industries such as financial services, manufacturing, and healthcare. The Middle East & Africa and Latin America are witnessing steady growth, underpinned by rising awareness of the benefits of graph data science and increasing adoption among government and enterprise sectors.

Component Analysis

The Component segment of the Graph Data Science Platform market is bifurcated into software and services, each playing a pivotal role in the market's overall growth and adoption. Software solutions form the backbone of the industry, providing the core capabilities for graph data modeling, visualization, analytics, and integration with existing enterprise systems. These platforms are continuously evolving, incorporating advanced features such as natural language processing, machine learning, and real-time analytics, which are crucial for handling complex and dynamic datasets. The software segment's growth is further accelerated by the increasing demand for user-friendly interfaces and seamless integration with cl

Netflix is a popular streaming service that offers a vast catalog of movies, TV shows, and original contents. This dataset is a cleaned version of the original version which can be found here. The data consist of contents added to Netflix from 2008 to 2021. The oldest content is as old as 1925 and the newest as 2021. This dataset will be cleaned with PostgreSQL and visualized with Tableau. The purpose of this dataset is to test my data cleaning and visualization skills. The cleaned data can be found below and the Tableau dashboard can be found here .

Data Cleaning

We are going to: 1. Treat the Nulls 2. Treat the duplicates 3. Populate missing rows 4. Drop unneeded columns 5. Split columns Extra steps and more explanation on the process will be explained through the code comments

--View dataset

SELECT * 
FROM netflix;

--The show_id column is the unique id for the dataset, therefore we are going to check for duplicates
                                  
SELECT show_id, COUNT(*)                                                                                      
FROM netflix 
GROUP BY show_id                                                                                              
ORDER BY show_id DESC;

--No duplicates

--Check null values across columns

SELECT COUNT(*) FILTER (WHERE show_id IS NULL) AS showid_nulls,
    COUNT(*) FILTER (WHERE type IS NULL) AS type_nulls,
    COUNT(*) FILTER (WHERE title IS NULL) AS title_nulls,
    COUNT(*) FILTER (WHERE director IS NULL) AS director_nulls,
    COUNT(*) FILTER (WHERE movie_cast IS NULL) AS movie_cast_nulls,
    COUNT(*) FILTER (WHERE country IS NULL) AS country_nulls,
    COUNT(*) FILTER (WHERE date_added IS NULL) AS date_addes_nulls,
    COUNT(*) FILTER (WHERE release_year IS NULL) AS release_year_nulls,
    COUNT(*) FILTER (WHERE rating IS NULL) AS rating_nulls,
    COUNT(*) FILTER (WHERE duration IS NULL) AS duration_nulls,
    COUNT(*) FILTER (WHERE listed_in IS NULL) AS listed_in_nulls,
    COUNT(*) FILTER (WHERE description IS NULL) AS description_nulls
FROM netflix;

We can see that there are NULLS. 
director_nulls = 2634
movie_cast_nulls = 825
country_nulls = 831
date_added_nulls = 10
rating_nulls = 4
duration_nulls = 3 

The director column nulls is about 30% of the whole column, therefore I will not delete them. I will rather find another column to populate it. To populate the director column, we want to find out if there is relationship between movie_cast column and director column

-- Below, we find out if some directors are likely to work with particular cast

WITH cte AS
(
SELECT title, CONCAT(director, '---', movie_cast) AS director_cast 
FROM netflix
)

SELECT director_cast, COUNT(*) AS count
FROM cte
GROUP BY director_cast
HAVING COUNT(*) > 1
ORDER BY COUNT(*) DESC;

With this, we can now populate NULL rows in directors 
using their record with movie_cast 

UPDATE netflix 
SET director = 'Alastair Fothergill'
WHERE movie_cast = 'David Attenborough'
AND director IS NULL ;

--Repeat this step to populate the rest of the director nulls
--Populate the rest of the NULL in director as "Not Given"

UPDATE netflix 
SET director = 'Not Given'
WHERE director IS NULL;

--When I was doing this, I found a less complex and faster way to populate a column which I will use next

Just like the director column, I will not delete the nulls in country. Since the country column is related to director and movie, we are going to populate the country column with the director column

--Populate the country using the director column

SELECT COALESCE(nt.country,nt2.country) 
FROM netflix AS nt
JOIN netflix AS nt2 
ON nt.director = nt2.director 
AND nt.show_id <> nt2.show_id
WHERE nt.country IS NULL;
UPDATE netflix
SET country = nt2.country
FROM netflix AS nt2
WHERE netflix.director = nt2.director and netflix.show_id <> nt2.show_id 
AND netflix.country IS NULL;


--To confirm if there are still directors linked to country that refuse to update

SELECT director, country, date_added
FROM netflix
WHERE country IS NULL;

--Populate the rest of the NULL in director as "Not Given"

UPDATE netflix 
SET country = 'Not Given'
WHERE country IS NULL;

The date_added rows nulls is just 10 out of over 8000 rows, deleting them cannot affect our analysis or visualization

--Show date_added nulls

SELECT show_id, date_added
FROM netflix_clean
WHERE date_added IS NULL;

--DELETE nulls

DELETE F...