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Data Science Platform Market Size 2025-2029

The data science platform market size is valued to increase USD 763.9 million, at a CAGR of 40.2% from 2024 to 2029. Integration of AI and ML technologies with data science platforms will drive the data science platform market.

Major Market Trends & Insights

North America dominated the market and accounted for a 48% growth during the forecast period.
By Deployment - On-premises segment was valued at USD 38.70 million in 2023
By Component - Platform segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 1.00 million
Market Future Opportunities: USD 763.90 million
CAGR : 40.2%
North America: Largest market in 2023

Market Summary

The market represents a dynamic and continually evolving landscape, underpinned by advancements in core technologies and applications. Key technologies, such as machine learning and artificial intelligence, are increasingly integrated into data science platforms to enhance predictive analytics and automate data processing. Additionally, the emergence of containerization and microservices in data science platforms enables greater flexibility and scalability. However, the market also faces challenges, including data privacy and security risks, which necessitate robust compliance with regulations.
According to recent estimates, the market is expected to account for over 30% of the overall big data analytics market by 2025, underscoring its growing importance in the data-driven business landscape.

What will be the Size of the Data Science Platform Market during the forecast period?

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How is the Data Science Platform Market Segmented and what are the key trends of market segmentation?

The data science platform industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Deployment

  On-premises
  Cloud


Component

  Platform
  Services


End-user

  BFSI
  Retail and e-commerce
  Manufacturing
  Media and entertainment
  Others


Sector

  Large enterprises
  SMEs


Application

  Data Preparation
  Data Visualization
  Machine Learning
  Predictive Analytics
  Data Governance
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  Middle East and Africa

    UAE


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.

In the dynamic and evolving the market, big data processing is a key focus, enabling advanced model accuracy metrics through various data mining methods. Distributed computing and algorithm optimization are integral components, ensuring efficient handling of large datasets. Data governance policies are crucial for managing data security protocols and ensuring data lineage tracking. Software development kits, model versioning, and anomaly detection systems facilitate seamless development, deployment, and monitoring of predictive modeling techniques, including machine learning algorithms, regression analysis, and statistical modeling. Real-time data streaming and parallelized algorithms enable real-time insights, while predictive modeling techniques and machine learning algorithms drive business intelligence and decision-making.

Cloud computing infrastructure, data visualization tools, high-performance computing, and database management systems support scalable data solutions and efficient data warehousing. ETL processes and data integration pipelines ensure data quality assessment and feature engineering techniques. Clustering techniques and natural language processing are essential for advanced data analysis. The market is witnessing significant growth, with adoption increasing by 18.7% in the past year, and industry experts anticipate a further expansion of 21.6% in the upcoming period. Companies across various sectors are recognizing the potential of data science platforms, leading to a surge in demand for scalable, secure, and efficient solutions.

API integration services and deep learning frameworks are gaining traction, offering advanced capabilities and seamless integration with existing systems. Data security protocols and model explainability methods are becoming increasingly important, ensuring transparency and trust in data-driven decision-making. The market is expected to continue unfolding, with ongoing advancements in technology and evolving business needs shaping its future trajectory.

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The On-premises segment was valued at USD 38.70 million in 2019 and showed

Travel regions are not necessarily defined by political or administrative boundaries. For example, in the Schengen region of Europe, tourists can travel freely across borders irrespective of national borders. Identifying transboundary travel regions is an interesting problem which we aim to solve using mobility analysis of Twitter users. Our proposed solution comprises collecting geotagged tweets, combining them into trajectories and, thus, mining thousands of trips undertaken by twitter users. After aggregating these trips into a mobility graph, we apply a community detection algorithm to find coherent regions throughout the world. The discovered regions provide insights into international travel and can reveal both domestic and transnational travel regions.

-> If you use Turkish_Ecommerce_Products_by_Gozukara_and_Ozel_2016 dataset, please cite: https://academic.oup.com/comjnl/advance-article-abstract/doi/10.1093/comjnl/bxab179/6425234

@article{10.1093/comjnl/bxab179, author = {Gözükara, Furkan and Özel, Selma Ayşe}, title = "{An Incremental Hierarchical Clustering Based System For Record Linkage In E-Commerce Domain}", journal = {The Computer Journal}, year = {2021}, month = {11}, abstract = "{In this study, a novel record linkage system for E-commerce products is presented. Our system aims to cluster the same products that are crawled from different E-commerce websites into the same cluster. The proposed system achieves a very high success rate by combining both semi-supervised and unsupervised approaches. Unlike the previously proposed systems in the literature, neither a training set nor structured corpora are necessary. The core of the system is based on Hierarchical Agglomerative Clustering (HAC); however, the HAC algorithm is modified to be dynamic such that it can efficiently cluster a stream of incoming new data. Since the proposed system does not depend on any prior data, it can cluster new products. The system uses bag-of-words representation of the product titles, employs a single distance metric, exploits multiple domain-based attributes and does not depend on the characteristics of the natural language used in the product records. To our knowledge, there is no commonly used tool or technique to measure the quality of a clustering task. Therefore in this study, we use ELKI (Environment for Developing KDD-Applications Supported by Index-Structures), an open-source data mining software, for performance measurement of the clustering methods; and show how to use ELKI for this purpose. To evaluate our system, we collect our own dataset and make it publicly available to researchers who study E-commerce product clustering. Our proposed system achieves 96.25\% F-Measure according to our experimental analysis. The other state-of-the-art clustering systems obtain the best 89.12\% F-Measure.}", issn = {0010-4620}, doi = {10.1093/comjnl/bxab179}, url = {https://doi.org/10.1093/comjnl/bxab179}, note = {bxab179}, eprint = {https://academic.oup.com/comjnl/advance-article-pdf/doi/10.1093/comjnl/bxab179/41133297/bxab179.pdf}, }

-> elki-bundle-0.7.2-SNAPSHOT.jar Is the ELKI bundle that we have compiled from the github source code of ELKI. The date of the source code is 6 June 2016. The compile command is as below: ->-> mvn -DskipTests -Dmaven.javadoc.skip=true -P svg,bundle package ->-> Github repository of ELKI: https://github.com/elki-project/elki ->-> This bundle file is used for all of the experiments that are presented in the article

-> Turkish_Ecommerce_Products_by_Gozukara_and_Ozel_2016 dataset is composed as below: ->-> Top 50 E-commerce websites that operate in Turkey are crawled, and their attributes are extracted. ->-> The crawling is made between 2015-01-13 15:12:46 ---- 2015-01-17 19:07:53 dates. ->-> Then 250 product offers from Vatanbilgisayar are randomly selected. ->-> Then the entire dataset is manually scanned to find which other products that are sold in different E-commerce websites are same as the selected ones. ->-> Then each product is classified respectively. ->-> This dataset contains these products along with their price (if available), title, categories (if available), free text description (if available), wrapped features (if available), crawled URL (the URL might have expired) attributes

-> The dataset files are provided as used in the study. -> ARFF files are generated with Raw Frequency of terms rather than used Weighting Schemes for All_Products and Only_Price_Having_Products. The reason is, we have tested these datasets with only our system and since our system does incremental clustering, even if provide TF-IDF weightings, they wouldn't be same as used in the article. More information provided in the article. ->-> For Macro_Average_Datasets we provide both Raw frequency and TF-IDF scheme weightings as used in the experiments

-> There are 3 main folders -> All_Products: This folder contains 1800 products. ->-> This is the entire collection that is manually labeled. ->-> They are from 250 different classes. -> Only_Price_Having_Products: This folder contains all of the products that have the price feature set. ->-> The collection has 1721 products from 250 classes. ->-> This is the dataset that we have experimented. -> Macro_Average_Datasets: This folder contains 100 datasets that we have used to conduct more reliable experiments. ->-> Each dataset is composed of selecting 1000 different products from the price having products dataset and then randomly ordering them...

Accurate monitoring and assessment of the environmental state, as a prerequisite for improved action, is valuable and necessary because of the growing number of environmental problems that have harmful effects on natural systems and human society. This study developed an integrated novel framework containing three modules remote sensing technology (RST), cruise monitoring technology (CMT), and deep learning to achieve a robust performance for environmental monitoring and the subsequent assessment. The deep neural network (DNN), a type of deep learning, can adapt and take advantage of the big data platform effectively provided by RST and CMT to obtain more accurate and improved monitoring results. It was proved by our case study in the Qingcaosha Reservoir (QCSR) that DNN showed a more robust performance (R2 = 0.89 for pH, R2 = 0.77 for DO, R2 = 0.86 for conductivity, and R2 = 0.95 for backscattered particles) compared to the traditional machine learning, including multiple linear regression, support vector regression, and random forest regression. Based on the monitoring results, the water quality assessment of QCSR was achieved by applying a deep learning algorithm called improved deep embedding clustering. Deep clustering analysis enables the scientific delineation of joint control regions and determines the characteristic factors of each area. This study presents the high value of the framework with a core of big data mining for environmental monitoring and follow-up assessment in a manner of high frequency, multidimensionality, and deep hierarchy.

Overview

This dataset contains a collection of over 2,000 company documents, categorized into four main types: invoices, inventory reports, purchase orders, and shipping orders. Each document is provided in PDF format, accompanied by a CSV file that includes the text extracted from these documents, their respective labels, and the word count of each document. This dataset is ideal for various natural language processing (NLP) tasks, including text classification, information extraction, and document clustering.

Dataset Content

PDF Documents: The dataset includes 2,677 PDF files, each representing a unique company document. These documents are derived from the Northwind dataset, which is commonly used for demonstrating database functionalities.

The document types are:

• Invoices: Detailed records of transactions between a buyer and a seller.
• Inventory Reports: Records of inventory levels, including items in stock and units sold.
• Purchase Orders: Requests made by a buyer to a seller to purchase products or services.
• Shipping Orders: Instructions for the delivery of goods to specified recipients.

Example Entries

Here are a few example entries from the CSV file:

Shipping Order:

• Order ID: 10718
• Shipping Details: "Ship Name: Königlich Essen, Ship Address: Maubelstr. 90, Ship City: ..."
• Word Count: 120

Invoice:

• Order ID: 10707
• Customer Details: "Customer ID: Arout, Order Date: 2017-10-16, Contact Name: Th..."
• Word Count: 66

Purchase Order:

• Order ID: 10892
• Order Details: "Order Date: 2018-02-17, Customer Name: Catherine Dewey, Products: Product ..."
• Word Count: 26

Applications

This dataset can be used for:

• Text Classification: Train models to classify documents into their respective categories.
• Information Extraction: Extract specific fields and details from the documents.
• Document Clustering: Group similar documents together based on their content.
• OCR and Text Mining: Improve OCR (Optical Character Recognition) models and text mining techniques using real-world data.

Microseismicity is expected in potash mining due to the associated rock-mass response. This phenomenon is known, but not fully understood. To assess the safety and efficiency of mining operations, producers must quantitatively discern between normal and abnormal seismic activity. In this work, statistical aspects and clustering of microseismicity from a Saskatchewan, Canada, potash mine are analyzed and quantified. Specifically, the frequency-magnitude statistics display a rich behavior that deviates from the standard Gutenberg-Richter scaling for small magnitudes. To model the magnitude distribution, we consider two additional models, i.e., the tapered Pareto distribution and a mixture of the tapered Pareto and Pareto distributions to fit the bi-modal catalog data. To study the clustering aspects of the observed microseismicity, the nearest-neighbor distance (NND) method is applied. This allowed the identification of potential cluster characteristics in time, space, and magnitude domains. The implemented modeling approaches and obtained results will be used to further advance strategies and protocols for the safe and efficient operation of potash mines.

IntroductionType 2 diabetes mellitus (T2DM) is a complex, chronic disease affecting multiple organs with varying symptoms and comorbidities. Profiling patients helps identify those with unfavorable disease progression, allowing for tailored therapy and addressing special needs. This study aims to uncover different T2DM profiles based on medication intake records and laboratory measurements, with a focus on how individuals with diabetes move through disease phases.MethodsWe use medical records from databases of the last 20 years from the Department of Endocrinology and Diabetology of the University Medical Center in Maribor. Using the standard ATC medication classification system, we created a patient-specific drug profile, created using advanced natural language processing methods combined with data mining and hierarchical clustering.ResultsOur results show a well-structured profile distribution characterizing different age groups of individuals with diabetes. Interestingly, only two main profiles characterize the early 40–50 age group, and the same is true for the last 80+ age group. One of these profiles includes individuals with diabetes with very low use of various medications, while the other profile includes individuals with diabetes with much higher use. The number in both groups is reciprocal. Conversely, the middle-aged groups are characterized by several distinct profiles with a wide range of medications that are associated with the distinct concomitant complications of T2DM. It is intuitive that the number of profiles increases in the later age groups, but it is not obvious why it is reduced later in the 80+ age group. In this context, further studies are needed to evaluate the contributions of a range of factors, such as drug development, drug adoption, and the impact of mortality associated with all T2DM-related diseases, which characterize these middle-aged groups, particularly those aged 55–75.ConclusionOur approach aligns with existing studies and can be widely implemented without complex or expensive analyses. Treatment and drug use data are readily available in healthcare facilities worldwide, allowing for profiling insights into individuals with diabetes. Integrating data from other departments, such as cardiology and renal disease, may provide a more sophisticated understanding of T2DM patient profiles.

This is the data used in the experiments of our paper:N. Arinik, R. Figueiredo, V. Labatut (2020), Multiplicity and Diversity: Analyzing the Optimal Solution Space of the Correlation Clustering Problem on Complete Signed Graphs, Journal of Complex Networks, DOI: 10.1093/comnet/cnaa025. The code source is accessible here: https://github.com/CompNet/SosoccThis dataset contains:* Plot files used in the article* Input signed networks* All optimal solutions (i.e. optimal solution space) of the corresponding networks* Evaluation files# PLOT FILES* Figure1.zip: Figures showing that there might be many distinct optimal solutions of a small-sized network.* Figure2.zip: Figures showing that distinct optimal solutions of a given network might be partition-wise very similar or different.* Figure4: All Results.zip: Figure 4 in the article contains only a few plots regarding the results for space considerations. This zip file contains all plots, and it is organized by the values of l0. In each l0 folder, the results are shown in three different perspectives: --- Detected Imbalance Percentage vs Graph Order (i.e. number of vertices) --- Prop mispl vs Graph order --- Graph order vs Prop mispl* workflow.pdf: The workflow of the methodology used in the article.* Syrian network With All Solutions.pdf: Syrian network (on top) with core part information through node colors, and its optimal solutions in which node colors represent partition information (on bottom).#NETWORKSAll networks are in Input Signed Networks.tar.gz.Networks are generated through a simple random model (available in https://github.com/CompNet/SignedBenchmark) designed to produce complete (or uncomplete) unweighted networks with built-in modular structure. There are 3 parameters used for the generation:- number of nodes (n)- initial number of modules (l0)- proportion of misplaced links, i.e. proportion of frustrated links, (qm)Inside Input Signed Networks.tar.gz:NETWORKS|_n=NB-NODE_l0=INIT_NB_MODULE_dens=1.0000....|_propMispl=PROP_MISPL ........|_propNeg=PROP_NEG ............|_network=NETWORK_NO- The first hierarchy => the folders are named as follows: n=NB-NODE_l0=INIT-NB-MODULE_dens=1.0000 The number of nodes, the initial number of modules and the network density are given. The network density is always 1, since we treat only complete signed networks.- The second hierarchy => the folders are named as follows: propMispl=PROP_MISPL Proportion of misplaced links is given.- The third hierarchy => the folders are named as follows: propNeg=PROP_NEG Proportion of negative links (qn) is specified. qn changes depending on n and l0. Since only complete signed networks are studied, this parameter is automatically computed from the other input parameters.- The fourth hierarchy => the folders are named as follows: network=NETWORK_NO Network numbers are shown.In the end, thre are three file formats describing the same network content: GraphML (.graphml), Pajek NET (.net) or .G format.# PARTITIONSAll partition results are in Partition Results.tar.gz. Note that all optimal partitions of a signed network are obtained through an exact partitioning method. The code source is accessible here: https://github.com/arinik9/ExCCInside Partition Results.tar.gz:PARTITIONS|_n=NB-NODE_l0=INIT_NB_MODULE_dens=1.0000 ....|_propMispl=PROP_MISPL ........|_propNeg=PROP_NEG ............|_network=NETWORK_NO ................|_"ExCC-all" ....................|_"signed-unweighted"- The first hierarchy => the folders are named as follows: n=NB-NODE_l0=INIT-NB-MODULE_dens=1.0000- The second hierarchy => the folders are named as follows: propMispl=PROP_MISPL- The third hierarchy => the folders are named as follows: propNeg=PROP_NEG- The fourth hierarchy => the folders are named as follows: network=NETWORK_NO- The fifth hierarchy => the folders are named as follows: "ExCC-all" The name of the partitioning method are shown. Since an exact partitioning method is used to obtain all distinct optimal solutions, it is named as "ExCC-all".- The sixth hierarchy => the folders are named as follows: "signed-unweighted" The type of signed networks are shown: signed and unweightedIn the end, the partition results are located, and the file names are named as follows: membership.txt. Note that the first partition result number starts from zero.# EVALUATIONSEvaluation results related to our plots are in Evaluation Results.tar.gz. Note that the hierarchy of this folder is the same as that of 'Partitions'. InsideEvaluation Results.tar.gz:-Best-k-for-kmedoids.csv: It contains three columns. 1) the number of solution classes via kmedoids, 2) the best Silhouette score, 3) the best clustering in terms of Silhouette score, which represents solution classes.-class-core-part-size-tresh=1.00.csv. It indicates the proportion of core part size for each solution class.-exec-time.csv: It indicates the execution time in seconds.-imbalance.csv: It contains the information of imbalance as 1) count and 2) percentage -nb-solution.csv`: It indicates the total number of solutions