MATLAB led the global advanced analytics and data science software industry in 2025 with a market share of ***** percent. First launched in 1984, MATLAB is developed by the U.S. firm MathWorks.

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for statistical data science in the U.S.

Across industries, organizations are increasing their hiring efforts to build larger data science arsenals: from 2020 to 2021, the percentage of surveyed organizations that employed ** data scientists or more increased from ** percent to almost ** percent. On average, the number of data scientists employed in a organization grew from ** to **.

In 2024, data scientists worldwide demonstrated varying levels of proficiency across different skills according to DevSkiller assessments. CSV handling emerged as the most proficient skill, reaching an advanced-level score of **. This high proficiency in CSV manipulation highlights the continued importance of working with structured data in various formats. Data analysis and data structures followed closely behind, with scores of ** and **, respectively, indicating strong foundational skills among data scientists. Nonetheless, several skills fell just above the intermediate threshold, including data selection, ETL fundamentals, and classification algorithms.

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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

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?

The statistic displays the most wanted data science skills in the United States as of **********. As of the measured period, ***** percent of data scientist job openings on LinkedIn required a knowledge of the programming language Python.

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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?

Get Key Insights on Market Forecast (PDF) Request Free Sample

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.

Request Free Sample

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

In recent years, data science agents powered by Large Language Models (LLMs), known as “data agents,” have shown significant potential to transform the traditional data analysis paradigm. This survey provides an overview of the evolution, capabilities, and applications of LLM-based data agents, highlighting their role in simplifying complex data tasks and lowering the entry barrier for users without related expertise. We explore current trends in the design of LLM-based frameworks, detailing essential features such as planning, reasoning, reflection, multi-agent collaboration, user interface, knowledge integration, and system design, which enable agents to address data-centric problems with minimal human intervention. Furthermore, we analyze several case studies to demonstrate the practical applications of various data agents in real-world scenarios. Finally, we identify key challenges and propose future research directions to advance the development of data agents into intelligent statistical analysis software.

Context

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1842206%2Fa1cea229c659d168f5780e83e6fcf08d%2Flecturer.png?generation=1706763786158636&alt=media" alt="">

I've collected information on the published videos, along with the threads and comments of well-known Datascience, Python, Statistics & Knowledge YouTube Channels.

https://www.youtube.com/watch?v=z3ZnOW-S550" alt="">

Time Series Forecasting with XGBoost - Advanced Methods One of Rob Mulla's published videos

Channels

• 365 Data Science
• 3Blue1Brown
• Abhishek Thakur
• Alex The Analyst
• Analytics India Magazine
• Analytics Vidhya
• ArjanCodes
• CS Dojo
• ChrisMcCormickAI
• Corey Schafer
• DATAtab
• Data Professor
• Data Science Dojo
• DataCamp
• Greg Hogg
• Healthy Software Developer
• IBM Technology
• Joma Tech
• Ken Jee
• Krish Naik
• NeuralNine
• Nicholas Renotte
• Rob Mulla
• StatQuest with Josh Starmer
• Tech With Tim
• Tina Huang
• Two Minute Papers
• codebasics
• freeCodeCamp.org
• sentdex

Important Note

There may be some missing videos esp if the channel has more than 600+ videos, this is because the API itself doesn't return all the videos as explained in this Stackoverlow post.

Over the last 20 years, statistics preparation has become vital for a broad range of scientific fields, and statistics coursework has been readily incorporated into undergraduate and graduate programs. However, a gap remains between the computational skills taught in statistics service courses and those required for the use of statistics in scientific research. Ten years after the publication of "Computing in the Statistics Curriculum,'' the nature of statistics continues to change, and computing skills are more necessary than ever for modern scientific researchers. In this paper, we describe research on the design and implementation of a suite of data science workshops for environmental science graduate students, providing students with the skills necessary to retrieve, view, wrangle, visualize, and analyze their data using reproducible tools. These workshops help to bridge the gap between the computing skills necessary for scientific research and the computing skills with which students leave their statistics service courses. Moreover, though targeted to environmental science graduate students, these workshops are open to the larger academic community. As such, they promote the continued learning of the computational tools necessary for working with data, and provide resources for incorporating data science into the classroom.

Methods Surveys from Carpentries style workshops the results of which are presented in the accompanying manuscript.

Pre- and post-workshop surveys for each workshop (Introduction to R, Intermediate R, Data Wrangling in R, Data Visualization in R) were collected via Google Form.

The surveys administered for the fall 2018, spring 2019 academic year are included as pre_workshop_survey and post_workshop_assessment PDF files. 
The raw versions of these data are included in the Excel files ending in survey_raw or assessment_raw.

  The data files whose name includes survey contain raw data from pre-workshop surveys and the data files whose name includes assessment contain raw data from the post-workshop assessment survey.


The annotated RMarkdown files used to clean the pre-workshop surveys and post-workshop assessments are included as workshop_survey_cleaning and workshop_assessment_cleaning, respectively. 
The cleaned pre- and post-workshop survey data are included in the Excel files ending in clean. 
The summaries and visualizations presented in the manuscript are included in the analysis annotated RMarkdown file.

365 Data Science Database

365 Data Science is a website that provides online courses and resources for learning data science, machine learning, and data analysis.

It is common for websites that offer online courses to have **databases **to store information about their courses, students, and progress. It is also possible that they use databases for storing and organizing the data used in their courses and examples.

If you're looking for specific information about the database used by 365 Data Science, I recommend reaching out to them directly through their Website or support channels.

A tech stack represents a combination of technologies a company uses in order to build and run an application or project. The most popular technology skill in the data science tech stack in 2024 was Python 3.x, chosen by **** percent of respondents. ETL ranked second, being used by *** percent of respondents. This comes as no surprise due to Python's importance in building artificial intelligence (AI) solutions and machine learning products.

The Data Science Services market has emerged as a pivotal force, reshaping industries through the power of data-driven decision-making. As organizations increasingly recognize the value of harnessing vast amounts of data, the demand for data science services is experiencing significant growth. In 2023, the global ma

International Journal of Data Science and Analytics - ResearchHelpDesk - International Journal of Data Science and Analytics - Data Science has been established as an important emergent scientific field and paradigm driving research evolution in such disciplines as statistics, computing science and intelligence science, and practical transformation in such domains as science, engineering, the public sector, business, social science, and lifestyle. The field encompasses the larger areas of artificial intelligence, data analytics, machine learning, pattern recognition, natural language understanding, and big data manipulation. It also tackles related new scientific challenges, ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and visualization, to integrative analysis across heterogeneous and interdependent complex resources for better decision-making, collaboration, and, ultimately, value creation. The International Journal of Data Science and Analytics (JDSA) brings together thought leaders, researchers, industry practitioners, and potential users of data science and analytics, to develop the field, discuss new trends and opportunities, exchange ideas and practices, and promote transdisciplinary and cross-domain collaborations.

A study on the book "Practical Statistics for Data Scientists - 50 essential concepts" - O'Reilly (CHAPTER 1)

The Data Science Platform market is witnessing remarkable growth, emerging as a crucial component for businesses aiming to harness the power of data-driven decision-making. These platforms offer advanced tools and frameworks that facilitate data extraction, transformation, analysis, and visualization, enabling organ

The Data Science Collaboration Platform market is rapidly evolving, becoming an essential hub for organizations seeking to leverage data-driven insights for strategic decision-making. With the increasing importance of data across industries, these platforms facilitate collaboration among data scientists, analysts, a

This paper reviews some ingredients of the current “Data Science moment”, including recent commentary about data science in the popular media, and about how/whether Data Science is really different from Statistics.

Crime isn't a topic most people want to use mental energy to think about. We want to avoid harm, protect our loved ones, and hold on to what we claim is ours. So how do we remain vigilant without digging too deep into the filth that is crime? Data, of course. The focus of our study is to explore possible trends between crime and communities in the city of Calgary. Our purpose is visualize Calgary criminal behaviour in order to help increase awareness for both citizens and law enforcement. Through the use of our visuals, individuals can make more informed decisions to improve the overall safety of their lives. Some of the main concerns of the study include: how crime rates increase with population, which areas in Calgary have the most crime, and if crime adheres to time-sensative patterns.