Context

Riga Data Science Club is a non-profit organisation to share ideas, experience and build machine learning projects together. Data Science community should known own data, so this is a dataset about ourselves: our website analytics, social media activity, slack statistics and even meetup transcriptions!

Content

Dataset is split up in several folders by the context: * linkedin - company page visitor, follower and post stats * slack - messaging and member activity * typeform - new member responses * website - website visitors by country, language, device, operating system, screen resolution * youtube - meetup transcriptions

Inspiration

Let's make Riga Data Science Club better! We expect this data to bring lots of insights on how to improve.

"Know your c̶u̶s̶t̶o̶m̶e̶r̶ member" - Explore member interests by analysing sign-up survey (typeform) responses - Explore messaging patterns in Slack to understand how members are retained and when they are lost

Social media intelligence * Define LinkedIn posting strategy based on historical engagement data * Define target user profile based on LinkedIn page attendance data

Website * Define website localisation strategy based on data about visitor countries and languages * Define website responsive design strategy based on data about visitor devices, operating systems and screen resolutions

Have some fun * NLP analysis of meetup transcriptions: word frequencies, question answering, something else?

Stack Exchange is a network of question-and-answer websites on topics in diverse fields, each site covering a specific topic, where questions, answers, and users are subject to a reputation award process. The reputation system allows the sites to be self-moderating.

The dataset here is specific to one such network site of Stack Exchange named Data Science Stack Exchange. The dataset is distributed over multiple files. It contains information on various Posts on data science that can be used for language processing, it has data on which posts are being liked by users more, etc. A lot of analysis can be done on this dataset.

The city of Austin has administered a community survey for the 2015, 2016, 2017, 2018 and 2019 years (https://data.austintexas.gov/City-Government/Community-Survey/s2py-ceb7), to “assess satisfaction with the delivery of the major City Services and to help determine priorities for the community as part of the City’s ongoing planning process.” To directly access this dataset from the city of Austin’s website, you can follow this link https://cutt.ly/VNqq5Kd. Although we downloaded the dataset analyzed in this study from the former link, given that the city of Austin is interested in continuing administering this survey, there is a chance that the data we used for this analysis and the data hosted in the city of Austin’s website may differ in the following years. Accordingly, to ensure the replication of our findings, we recommend researchers to download and analyze the dataset we employed in our analyses, which can be accessed at the following link https://github.com/democratizing-data-science/MDCOR/blob/main/Community_Survey.csv. Replication Features or Variables The community survey data has 10,684 rows and 251 columns. Of these columns, our analyses will rely on the following three indicators that are taken verbatim from the survey: “ID”, “Q25 - If there was one thing you could share with the Mayor regarding the City of Austin (any comment, suggestion, etc.), what would it be?", and “Do you own or rent your home?”

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Online Data Science Training Programs Market Size 2025-2029

The online data science training programs market size is forecast to increase by USD 8.67 billion, at a CAGR of 35.8% between 2024 and 2029.

The market is experiencing significant growth due to the increasing demand for data science professionals in various industries. The job market offers lucrative opportunities for individuals with data science skills, making online training programs an attractive option for those seeking to upskill or reskill. Another key driver in the market is the adoption of microlearning and gamification techniques in data science training. These approaches make learning more engaging and accessible, allowing individuals to acquire new skills at their own pace. Furthermore, the availability of open-source learning materials has democratized access to data science education, enabling a larger pool of learners to enter the field. However, the market also faces challenges, including the need for continuous updates to keep up with the rapidly evolving data science landscape and the lack of standardization in online training programs, which can make it difficult for employers to assess the quality of graduates. Companies seeking to capitalize on market opportunities should focus on offering up-to-date, high-quality training programs that incorporate microlearning and gamification techniques, while also addressing the challenges of continuous updates and standardization. By doing so, they can differentiate themselves in a competitive market and meet the evolving needs of learners and employers alike.

What will be the Size of the Online Data Science Training Programs Market during the forecast period?

Request Free SampleThe online data science training market continues to evolve, driven by the increasing demand for data-driven insights and innovations across various sectors. Data science applications, from computer vision and deep learning to natural language processing and predictive analytics, are revolutionizing industries and transforming business operations. Industry case studies showcase the impact of data science in action, with big data and machine learning driving advancements in healthcare, finance, and retail. Virtual labs enable learners to gain hands-on experience, while data scientist salaries remain competitive and attractive. Cloud computing and data science platforms facilitate interactive learning and collaborative research, fostering a vibrant data science community. Data privacy and security concerns are addressed through advanced data governance and ethical frameworks. Data science libraries, such as TensorFlow and Scikit-Learn, streamline the development process, while data storytelling tools help communicate complex insights effectively. Data mining and predictive analytics enable organizations to uncover hidden trends and patterns, driving innovation and growth. The future of data science is bright, with ongoing research and development in areas like data ethics, data governance, and artificial intelligence. Data science conferences and education programs provide opportunities for professionals to expand their knowledge and expertise, ensuring they remain at the forefront of this dynamic field.

How is this Online Data Science Training Programs Industry segmented?

The online data science training programs 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. TypeProfessional degree coursesCertification coursesApplicationStudentsWorking professionalsLanguageR programmingPythonBig MLSASOthersMethodLive streamingRecordedProgram TypeBootcampsCertificatesDegree ProgramsGeographyNorth AmericaUSMexicoEuropeFranceGermanyItalyUKMiddle East and AfricaUAEAPACAustraliaChinaIndiaJapanSouth KoreaSouth AmericaBrazilRest of World (ROW)

By Type Insights

The professional degree courses segment is estimated to witness significant growth during the forecast period.The market encompasses various segments catering to diverse learning needs. The professional degree course segment holds a significant position, offering comprehensive and in-depth training in data science. This segment's curriculum covers essential aspects such as statistical analysis, machine learning, data visualization, and data engineering. Delivered by industry professionals and academic experts, these courses ensure a high-quality education experience. Interactive learning environments, including live lectures, webinars, and group discussions, foster a collaborative and engaging experience. Data science applications, including deep learning, computer vision, and natural language processing, are integral to the market's growth. Data analysis, a crucial application, is gaining traction due to the increasing demand for data-driven decisio

HabibAhmed/Data-Science-Instruct-Dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

The "**Facebook Group Insights Dataset**" on Kaggle is a concise, data-rich resource for analysing the dynamics of a specific Facebook group.

This dataset provides key information on admins, daily metrics, member demographics, geographic distribution, popular activity times, and top-performing posts from the past 28 days. It is an essential tool for researchers, social media analysts, and data enthusiasts looking to gain insights into online community behaviour and engagement strategies. Whether you're a social media manager or a data scientist, this dataset offers precise and valuable insights into the inner workings of Facebook groups.

Please, If you enjoyed this dataset, don't forget to upvote it.

Content

This dataset contains a couple of fields with the information based on Reddit post submission, such:

• title
• id
• redditor
• num_upvotes
• subreddit
• url
• num_comments
• created_on
• body
• upvote_ratio
• over_18
• link_flair_text
• edited

Method

The data was extracted using the PRAW:The Python Reddit API Wrapper.

Credits

Cover Image: Photo by Marius Masalar on Unsplash

Community Analytics Platform Market Outlook

According to our latest research, the global community analytics platform market size reached USD 2.8 billion in 2024, with a robust growth trajectory driven by the rising demand for actionable insights from online communities. The market is expected to expand at a CAGR of 17.2% from 2025 to 2033, reaching an estimated USD 12.9 billion by 2033. This growth is propelled by the increasing integration of artificial intelligence, machine learning, and advanced data analytics in community management tools, which enable organizations to better understand user behavior, enhance engagement, and optimize business strategies.

One of the primary growth factors for the community analytics platform market is the exponential rise in digital communities and social media interactions across industries. As organizations increasingly rely on digital platforms to foster brand loyalty, provide customer support, and build engaged user bases, the need for robust analytics solutions becomes paramount. Community analytics platforms empower businesses to extract valuable insights from user-generated content, sentiment, and engagement patterns, enabling data-driven decision-making. The proliferation of online forums, brand communities, and social networking groups has created a goldmine of data, which, when properly analyzed, can significantly enhance customer engagement and drive business growth.

Another significant driver is the rapid adoption of cloud-based analytics solutions. Cloud deployment offers scalability, flexibility, and cost-effectiveness, making it an attractive choice for organizations of all sizes. The shift towards cloud-based community analytics platforms is further accelerated by the need for real-time data processing and remote accessibility, especially in the post-pandemic era where remote work and virtual communities have become the norm. Cloud solutions also facilitate seamless integration with other business applications, enabling organizations to create a unified data ecosystem that enhances operational efficiency and strategic planning.

Furthermore, advancements in artificial intelligence and machine learning are transforming the landscape of community analytics. AI-powered platforms can automate sentiment analysis, content moderation, and predictive analytics, providing deeper insights into community dynamics and user behavior. These technologies enable organizations to identify emerging trends, detect potential issues, and personalize interactions at scale. As a result, businesses are increasingly investing in AI-driven community analytics solutions to stay ahead of the competition, improve customer satisfaction, and foster long-term loyalty.

From a regional perspective, North America continues to dominate the community analytics platform market, accounting for the largest revenue share in 2024. This dominance is attributed to the high adoption rate of advanced analytics technologies, the presence of major market players, and the strong digital infrastructure in the region. However, Asia Pacific is emerging as the fastest-growing market, fueled by rapid digitalization, increasing internet penetration, and the growing popularity of online communities in countries like China, India, and Japan. Europe also holds a significant market share, driven by the rising focus on customer experience and regulatory requirements for data-driven decision-making.

Component Analysis

The community analytics platform market by component is segmented into software and services, each playing a pivotal role in the ecosystem. The software segment encompasses a wide array of tools such as dashboards, reporting modules, sentiment analysis engines, and integration frameworks designed to extract, process, and visualize data from community interactions. These solutions are continuously evolving, with vendors integrating advanced features like natural language processing, real-time analytics, and automated reporting to provide comprehensive insights. As organizations increasingly seek to levera

The objective of this research is to investigate the factors influencing scientists’ data sharing behaviors in different scientific communities by examining both discipline and individual level predictors together. The target population of this research included faculty members and post-doctoral researchers in U.S. academic institutions who belong to STEM disciplines. The sampling frame of this research was identified from the scholar list in the Community of Science’s (CoS) Scholar Database (http://pivot.cos.com), which provides a researcher profile directory in the world mainly from universities and colleges. The final field survey instrument was distributed to the 16,165 potential survey participants in 56 STEM disciplines. From November 19, 2012 to February 15, 2013, a total of 2,470 valid responses were received for the initial data analysis (15.28% of response rate).

Citation database for the analysis conducted in "Orientations to Mentoring in Academic and Community Data Science."

The popularity of science blogging has increased in recent years, but the number of academic scientists who maintain regular blogs is limited. The role and impact of science communication blogs aimed at general audiences is often discussed, but the value of science community blogs aimed at the academic community has largely been overlooked. Here, we focus on our own experiences as bloggers to argue that science community blogs are valuable to the academic community. We use data from our own blogs (n = 7) to illustrate some of the factors influencing reach and impact of science community blogs. We then discuss the value of blogs as a standalone medium, where rapid communication of scholarly ideas, opinions, and short observational notes can enhance scientific discourse, and discussion of personal experiences can provide indirect mentorship for junior researchers and scientists from underrepresented groups. Finally, we argue that science community blogs can be treated as a primary source and provide some key points to consider when citing blogs in peer-reviewed literature.

The presence of data science has been profound in the scientific community in almost every discipline. An important part of the data science education expansion has been at the undergraduate level. We conducted a systematic literature review to (a) portray current evidence and knowledge gaps in self-proclaimed undergraduate data science education research and (b) inform policymakers and the data science education community about what educators may encounter when searching for literature using the general keyword “data science education.” While open-access publications that target a broader audience of data science educators and include multiple examples of data science programs and courses are a strength, substantial knowledge gaps remain. The undergraduate data science literature that we identified often lacks empirical data, research questions, and reproducibility. Certain disciplines are less visible. We recommend that we should (a) cherish data science as an interdisciplinary field; (b) adopt a consistent set of keywords/terminology to ensure data science education literature is easily identifiable; (c) prioritize investments in empirical studies.

This short activity was an effort to launch a community conversation around the interface of data science principles and practices and undergraduate biology education. A variety of resources, communities, and projects are shared.

Discover the booming Community-Driven Model Service Platform market! This comprehensive analysis reveals a CAGR of 10.1%, driven by AI adoption and open-source innovation. Explore market size, trends, segmentation (cloud, on-premises, adult, children), key players (Kaggle, GitHub, Hugging Face), and regional insights. Learn more about this rapidly expanding sector.

espejelomar/data-science-job-salaries dataset hosted on Hugging Face and contributed by the HF Datasets community

Table of usage statistics (number of views) for datasets within the Halifax Open Data Catalogue.The data was collected to show the usage of data within the Open Data Catalogue. Metadata

Data are the foundation of science, and there is an increasing focus on how data can be reused and enhanced to drive scientific discoveries. However, most seemingly “open data” do not provide legal permissions for reuse and redistribution. The inability to integrate and redistribute our collective data resources blocks innovation and stymies the creation of life-improving diagnostic and drug selection tools. To help the biomedical research and research support communities (e.g. libraries, funders, repositories, etc.) understand and navigate the data licensing landscape, the (Re)usable Data Project (RDP) (http://reusabledata.org) assesses the licensing characteristics of data resources and how licensing behaviors impact reuse. We have created a ruleset to determine the reusability of data resources and have applied it to 56 scientific data resources (e.g. databases) to date. The results show significant reuse and interoperability barriers. Inspired by game-changing projects like Creative Commons, the Wikipedia Foundation, and the Free Software movement, we hope to engage the scientific community in the discussion regarding the legal use and reuse of scientific data, including the balance of openness and how to create sustainable data resources in an increasingly competitive environment.

The Community-Driven Model Service Platform market is experiencing robust growth, projected to reach $35.14 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 10.1% from 2025 to 2033. This expansion is fueled by several key factors. The increasing availability of open-source models and datasets, fostered by platforms like Kaggle, GitHub, and Hugging Face, is democratizing access to advanced machine learning capabilities. This, in turn, accelerates innovation and reduces the barrier to entry for both developers and businesses. Furthermore, the growing demand for specialized AI solutions across diverse sectors—from healthcare and finance to manufacturing and retail—is driving adoption. The cloud-based segment holds a significant market share due to its scalability, accessibility, and cost-effectiveness compared to on-premises solutions. The adult application segment is currently the largest, reflecting the high concentration of skilled professionals and research activities within this group; however, the children's application segment shows significant growth potential given increasing educational initiatives incorporating AI. Geographic distribution shows North America and Europe currently leading market adoption, while Asia-Pacific is expected to witness rapid expansion driven by increasing digitalization and technological advancements. The competitive landscape is characterized by a mix of established technology giants and emerging startups. Platforms like TensorFlow Hub and Model Zoo provide comprehensive model repositories, while companies like DrivenData and Cortex focus on data-centric approaches. This competitive environment encourages continuous improvement and innovation within the platform offerings. Challenges include ensuring data security and privacy, addressing biases in datasets, and maintaining a balance between open collaboration and intellectual property rights. However, the overall trajectory points toward sustained market growth, fueled by ongoing technological advancements, increasing adoption across diverse industries, and the continuous contribution of a vibrant community of developers and researchers. Future growth will hinge on platforms successfully addressing the challenges and further enhancing collaborative features, fostering community engagement, and expanding the available resources.

ABSTRACT The potential impacts of citizen science initiatives are increasing across the globe, albeit in an imbalanced manner. In general, there is a strong element of trial and error in most projects, and the comparison of best practices and project structure between different initiatives remains difficult. In Brazil, the participation of volunteers in environmental research is limited. Identifying the factors related to citizen science projects’ success and longevity within a global perspective can contribute for consolidating such practices in the country. In this study, we explore past and present projects, including a case study in Brazil, to identify the spatial and temporal trends of citizen science programs as well as their best practices and challenges. We performed a bibliographic search using Google Scholar and considered results from 2005-2014. Although these results are subjective due to the Google Scholar’s algorithm and ranking criteria, we highlighted factors to compare projects across geographical and disciplinary areas and identified key matches between project proponents and participants, project goals and local priorities, participant profiles and engagement, scientific methods and funding. This approach is a useful starting point for future citizen science projects, allowing for a systematic analysis of potential inconsistencies and shortcomings in this emerging field.

The high performance computing (HPC) and big data (BD) communities traditionally have pursued independent trajectories in the world of computational science. HPC has been synonymous with modeling and simulation, and BD with ingesting and analyzing data from diverse sources, including from simulations. However, both communities are evolving in response to changing user needs and technological landscapes. Researchers are increasingly using machine learning (ML) not only for data analytics but also for modeling and simulation; science-based simulations are increasingly relying on embedded ML models not only to interpret results from massive data outputs but also to steer computations. Science-based models are being combined with data-driven models to represent complex systems and phenomena. There also is an increasing need for real-time data analytics, which requires large-scale computations to be performed closer to the data and data infrastructures, to adapt to HPC-like modes of operation. These new use cases create a vital need for HPC and BD systems to deal with simulations and data analytics in a more unified fashion. To explore this need, the NITRD Big Data and High-End Computing R&D Interagency Working Groups held a workshop, The Convergence of High-Performance Computing, Big Data, and Machine Learning, on October 29-30, 2018, in Bethesda, Maryland. The purposes of the workshop were to bring together representatives from the public, private, and academic sectors to share their knowledge and insights on integrating HPC, BD, and ML systems and approaches and to identify key research challenges and opportunities. The 58 workshop participants represented a balanced cross-section of stakeholders involved in or impacted by this area of research. Additional workshop information, including a webcast, is available at https://www.nitrd.gov/nitrdgroups/index.php?title=HPC-BD-Convergence.