Snapshot img

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

The aim of this survey was to collect feedback about existing training programmes in statistical analysis for postgraduate researchers at the University of Edinburgh, as well as respondents' preferred methods for training, and their requirements for new courses. The survey was circulated via e-mail to research staff and postgraduate researchers across three colleges of the University of Edinburgh: the College of Arts, Humanities and Social Sciences; the College of Science and Engineering; and the College of Medicine and Veterinary Medicine. The survey was conducted on-line using the Bristol Online Survey tool, March through July 2017. 90 responses were received. The Scoping Statistical Analysis Support project, funded by Information Services Innovation Fund, aims to increase visibility and raise the profile of the Research Data Service by: understanding how statistical analysis support is conducted across University of Edinburgh Schools; scoping existing support mechanisms and models for students, researchers and teachers; identifying services and support that would satisfy existing or future demand.

This course is an introduction to the key ideas and principles of the collection, display, and analysis of data to guide you in making valid and appropriate conclusions about the world. We live in a world where data are increasingly available, in ever larger quantities, and are increasingly expected to form the basis for decisions by governments, businesses, and other organizations, as well as by individuals in their daily lives. To cope effectively, every informed citizen must be statistically literate. This course will provide an intuitive introduction to applied statistical reasoning, introducing fundamental statistical skills and acquainting students with the full process of inquiry and evaluation used in investigations in a wide range of fields.

This dataset is about books. It has 2 rows and is filtered where the book is Data analysis with SPSS : a first course in applied statistics. It features 7 columns including author, publication date, language, and book publisher.

The open science movement produces vast quantities of openly published data connected to journal articles, creating an enormous resource for educators to engage students in current topics and analyses. However, educators face challenges using these materials to meet course objectives. I present a case study using open science (published articles and their corresponding datasets) and open educational practices in a capstone course. While engaging in current topics of conservation, students trace connections in the research process, learn statistical analyses, and recreate analyses using the programming language R. I assessed the presence of best practices in open articles and datasets, examined student selection in the open grading policy, surveyed students on their perceived learning gains, and conducted a thematic analysis on student reflections. First, articles and datasets met just over half of the assessed fairness practices, but this increased with the publication date. There was a..., Article and dataset fairness To assess the utility of open articles and their datasets as an educational tool in an undergraduate academic setting, I measured the congruence of each pair to a set of best practices and guiding principles. I assessed ten guiding principles and best practices (Table 1), where each category was scored ‘1’ or ‘0’ based on whether it met that criteria, with a total possible score of ten. Open grading policies Students were allowed to specify the percentage weight for each assessment category in the course, including 1) six coding exercises (Exercises), 2) one lead exercise (Lead Exercise), 3) fourteen annotation assignments of readings (Annotations), 4) one final project (Final Project), 5) five discussion board posts and a statement of learning reflection (Discussion), and 6) attendance and participation (Participation). I examined if assessment categories (independent variable) were weighted (dependent variable) differently by students using an analysis of ..., , # Data for: Integrating open education practices with data analysis of open science in an undergraduate course

Author: Marja H Bakermans Affiliation: Worcester Polytechnic Institute, 100 Institute Rd, Worcester, MA 01609 USA ORCID: https://orcid.org/0000-0002-4879-7771 Institutional IRB approval: IRB-24–0314

Data and file overview

The full dataset file called OEPandOSdata (.xlsx extension) contains 8 files. Below are descriptions of the name and contents of each file. NA = not applicable or no data available

1. BestPracticesData.csv
   • Description: Data to assess the adherence of articles and datasets to open science best practices.
   • Column headers and descriptions:
     • Article: articles used in the study, numbered randomly
     • F1: Findable, Data are assigned a unique and persistent doi
     • F2: Findable, Metadata includes an identifier of data
     • F3: Findable, Data are registered in a searchable database
     • A1: ...

This dataset is about book series. It has 1 row and is filtered where the books is Data analysis and regression : a second course in statistics. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Teaching undergraduate political methodology courses is a challenging task, yet has garnered little pedagogical discussion within the discipline. With the growing use of technology in the classroom, as well as the growing demand for data science and data literacy in our society, better understanding how we use statistical software in these courses is warranted. In this short paper, we shed light on current practices in teaching political methodology courses, with a particular emphasis on the use of statistical software. Combining an analysis of 93 course syllabi with a quantitative survey of research method instructors, we provide key information on the structure of these courses and how they incorporate statistical software. Our results reflect the growing importance of data literacy within the discipline, and suggest that more intentional discussions of research method pedagogy are needed in the future.

Vision and Change in Undergraduate Biology Education encouraged faculty to focus on core concepts and competencies in undergraduate curriculum. We created a sophomore-level course, Biologists' Toolkit, to focus on the competencies of quantitative reasoning and scientific communication. We introduce students to the statistical analysis of data using the open source statistical language and environment, R and R Studio, in the first two-thirds of the course. During this time the students learn to write basic computer commands to input data and conduct common statistical analysis. The students also learn to graphically represent their data using R. In a final project, we assign students unique data sets that require them to develop a hypothesis that can be explored with the data, analyze and graph the data, search literature related to their data set, and write a report that emulates a scientific paper. The final report includes publication quality graphs and proper reporting of data and statistical results. At the end of the course students reported greater confidence in their ability to read and make graphs, analyze data, and develop hypotheses. Although programming in R has a steep learning curve, we found that students who learned programming in R developed a robust strategy for data analyses and they retained and successfully applied those skills in other courses during their junior and senior years.

[Coursera] Computational Methods for Data Analysis (University of Washington) (compmethods)

Analysis of ‘Coursera Course Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/siddharthm1698/coursera-course-dataset on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

This is a dataset i generated during a hackathon for project purpose. Here i have scrapped data from Coursera official web site. Our project aims to help any new learner get the right course to learn by just answering a few questions. It is an intelligent course recommendation system. Hence we had to scrap data from few educational websites. This is data scrapped from Coursera website. For the project visit: https://github.com/Siddharth1698/Coursu . Please do show your support by following us. I have just started to learn on data science and hope this dataset will be helpful to someone for his/her personal purposes. The scrapping code is here : https://github.com/Siddharth1698/Coursera-Course-Dataset Article about the dataset generation : https://medium.com/analytics-vidhya/web-scraping-and-coursera-8db6af45d83f

Content

This dataset contains mainly 6 columns and 890 course data. The detailed description: 1. course_title : Contains the course title. 2. course_organization : It tells which organization is conducting the courses. 3. course_Certificate_type : It has details about what are the different certifications available in courses. 4. course_rating : It has the ratings associated with each course. 5. course_difficulty : It tells about how difficult or what is the level of the course. 6. course_students_enrolled : It has the number of students that are enrolled in the course.

Inspiration

This is just one of my first scrapped dataset. Follow my GitHub for more: https://github.com/Siddharth1698

--- Original source retains full ownership of the source dataset ---

We include the course syllabus used to teach quantitative research design and analysis methods to graduate Linguistics students using a blended teaching and learning approach. The blended course took place over two weeks and builds on a face to face course presented over two days in 2019. Students worked through the topics in preparation for a live interactive video session each Friday to go through the activities. Additional communication took place on Slack for two hours each week. A survey was conducted at the start and end of the course to ascertain participants' perceptions of the usefulness of the course. The links to online elements and the evaluations have been removed from the uploaded course guide.Participants who complete this workshop will be able to:- outline the steps and decisions involved in quantitative data analysis of linguistic data- explain common statistical terminology (sample, mean, standard deviation, correlation, nominal, ordinal and scale data)- perform common statistical tests using jamovi (e.g. t-test, correlation, anova, regression)- interpret and report common statistical tests- describe and choose from the various graphing options used to display data- use jamovi to perform common statistical tests and graph resultsEvaluationParticipants who complete the course will use these skills and knowledge to complete the following activities for evaluation:- analyse the data for a project and/or assignment (in part or in whole)- plan the results section of an Honours research project (where applicable)Feedback and suggestions can be directed to M Schaefer schaemn@unisa.ac.za

Snapshot img

Data Science Platform Market Size 2025-2029

The data science platform market size is forecast to increase by USD 763.9 million, at a CAGR of 40.2% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies. This fusion enables organizations to derive deeper insights from their data, fueling business innovation and decision-making. Another trend shaping the market is the emergence of containerization and microservices in data science platforms. This approach offers enhanced flexibility, scalability, and efficiency, making it an attractive choice for businesses seeking to streamline their data science operations. However, the market also faces challenges. Data privacy and security remain critical concerns, with the increasing volume and complexity of data posing significant risks. Ensuring robust data security and privacy measures is essential for companies to maintain customer trust and comply with regulatory requirements. Additionally, managing the complexity of data science platforms and ensuring seamless integration with existing systems can be a daunting task, requiring significant investment in resources and expertise. Companies must navigate these challenges effectively to capitalize on the market's opportunities and stay competitive in the rapidly evolving data landscape.

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

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe market continues to evolve, driven by the increasing demand for advanced analytics and artificial intelligence solutions across various sectors. Real-time analytics and classification models are at the forefront of this evolution, with APIs integrations enabling seamless implementation. Deep learning and model deployment are crucial components, powering applications such as fraud detection and customer segmentation. Data science platforms provide essential tools for data cleaning and data transformation, ensuring data integrity for big data analytics. Feature engineering and data visualization facilitate model training and evaluation, while data security and data governance ensure data privacy and compliance. Machine learning algorithms, including regression models and clustering models, are integral to predictive modeling and anomaly detection. Statistical analysis and time series analysis provide valuable insights, while ETL processes streamline data integration. Cloud computing enables scalability and cost savings, while risk management and algorithm selection optimize model performance. Natural language processing and sentiment analysis offer new opportunities for data storytelling and computer vision. Supply chain optimization and recommendation engines are among the latest applications of data science platforms, demonstrating their versatility and continuous value proposition. Data mining and data warehousing provide the foundation for these advanced analytics capabilities.

How is this Data Science Platform Industry segmented?

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. DeploymentOn-premisesCloudComponentPlatformServicesEnd-userBFSIRetail and e-commerceManufacturingMedia and entertainmentOthersSectorLarge enterprisesSMEsApplicationData PreparationData VisualizationMachine LearningPredictive AnalyticsData GovernanceOthersGeographyNorth AmericaUSCanadaEuropeFranceGermanyUKMiddle East and AfricaUAEAPACChinaIndiaJapanSouth AmericaBrazilRest of World (ROW)

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.In the dynamic the market, businesses increasingly adopt solutions to gain real-time insights from their data, enabling them to make informed decisions. Classification models and deep learning algorithms are integral parts of these platforms, providing capabilities for fraud detection, customer segmentation, and predictive modeling. API integrations facilitate seamless data exchange between systems, while data security measures ensure the protection of valuable business information. Big data analytics and feature engineering are essential for deriving meaningful insights from vast datasets. Data transformation, data mining, and statistical analysis are crucial processes in data preparation and discovery. Machine learning models, including regression and clustering, are employed for model training and evaluation. Time series analysis and natural language processing are valuable tools for understanding trends and customer sen

This paper explores a unique dataset of all the SET ratings provided by students of one university in Poland at the end of the winter semester of the 2020/2021 academic year. The SET questionnaire used by this university is presented in Appendix 1. The dataset is unique for several reasons. It covers all SET surveys filled by students in all fields and levels of study offered by the university. In the period analysed, the university was entirely in the online regime amid the Covid-19 pandemic. While the expected learning outcomes formally have not been changed, the online mode of study could have affected the grading policy and could have implications for some of the studied SET biases. This Covid-19 effect is captured by econometric models and discussed in the paper. The average SET scores were matched with the characteristics of the teacher for degree, seniority, gender, and SET scores in the past six semesters; the course characteristics for time of day, day of the week, course type, course breadth, class duration, and class size; the attributes of the SET survey responses as the percentage of students providing SET feedback; and the grades of the course for the mean, standard deviation, and percentage failed. Data on course grades are also available for the previous six semesters. This rich dataset allows many of the biases reported in the literature to be tested for and new hypotheses to be formulated, as presented in the introduction section. The unit of observation or the single row in the data set is identified by three parameters: teacher unique id (j), course unique id (k) and the question number in the SET questionnaire (n ϵ {1, 2, 3, 4, 5, 6, 7, 8, 9} ). It means that for each pair (j,k), we have nine rows, one for each SET survey question, or sometimes less when students did not answer one of the SET questions at all. For example, the dependent variable SET_score_avg(j,k,n) for the triplet (j=Calculus, k=John Smith, n=2) is calculated as the average of all Likert-scale answers to question nr 2 in the SET survey distributed to all students that took the Calculus course taught by John Smith. The data set has 8,015 such observations or rows. The full list of variables or columns in the data set included in the analysis is presented in the attached filesection. Their description refers to the triplet (teacher id = j, course id = k, question number = n). When the last value of the triplet (n) is dropped, it means that the variable takes the same values for all n ϵ {1, 2, 3, 4, 5, 6, 7, 8, 9}.Two attachments:- word file with variables description- Rdata file with the data set (for R language).Appendix 1. Appendix 1. The SET questionnaire was used for this paper. Evaluation survey of the teaching staff of [university name] Please, complete the following evaluation form, which aims to assess the lecturer’s performance. Only one answer should be indicated for each question. The answers are coded in the following way: 5- I strongly agree; 4- I agree; 3- Neutral; 2- I don’t agree; 1- I strongly don’t agree. Questions 1 2 3 4 5 I learnt a lot during the course. ○ ○ ○ ○ ○ I think that the knowledge acquired during the course is very useful. ○ ○ ○ ○ ○ The professor used activities to make the class more engaging. ○ ○ ○ ○ ○ If it was possible, I would enroll for the course conducted by this lecturer again. ○ ○ ○ ○ ○ The classes started on time. ○ ○ ○ ○ ○ The lecturer always used time efficiently. ○ ○ ○ ○ ○ The lecturer delivered the class content in an understandable and efficient way. ○ ○ ○ ○ ○ The lecturer was available when we had doubts. ○ ○ ○ ○ ○ The lecturer treated all students equally regardless of their race, background and ethnicity. ○ ○

The global data analytics training market is expected to grow in the forecast period of 2025-2034 at a CAGR of 23.00%.

Higher degree research students in education are largely underprepared for understanding or employing statistical data analysis methods. This is despite their need to read literature in their field which will indubitably include such research. This weakness may result in students choosing to use qualitative or interpretivist methodologies, even though education data are highly complex requiring sophisticated analysis techniques to properly evaluate the impact of nested data, multi-collinear factors, missing data, and changes over time. This paper describes a research methods course at a research-intensive university designed for students in a thesis-only degree program. The course emphasizes the logic and conceptual function of statistical methods and exposes students to hands-on tutorials in which students are required to conduct analyses with open-access data. The first half of the 12-week course focuses on core knowledge, normally taught in first-year probability and statistics courses. The second half focuses on introducing and modeling advanced statistical methods needed to handle complex problems and data. The course outline is provided along with descriptions of teaching and assessments. This exemplar functions as a potential model of how relative novices in statistical methods can be introduced to a conceptual use of statistical methods to raise the credibility of research.

This study aimed to explore whether the framing effect could be reduced in medical students and residents by teaching them the statistical concepts of effect size, probability, and sampling for use in the medical decision-making process. Ninety-five second-year medical students and 100 second-year medical residents of Austral University and Buenos Aires University, Argentina were invited to participate in the study between March and June 2017. A questionnaire was developed to assess the different types of framing effects in medical situations. After an initial administration of the survey, students and residents were taught statistical concepts including effect size, probability, and sampling during 2 individual independent official biostatistics courses. After these interventions, the same questionnaire was randomly administered again, and pre- and post-intervention outcomes were compared among students and residents.

Statistical applications are increasingly inducing ethical considerations, which are often not able to be resolved via statistics alone. In this article, we present a proposed course that combines applied statistics and moral philosophy. The instructional methods included are designed with implementation at a large research institution in mind but are fully intended to be transferable to any setting adopting such an interdisciplinary course into its curriculum. The aforementioned methods will foreground case-studies as tangible examples in a recurrent workflow involving identification of a dilemma, statistical analysis, philosophical defense, and application to the particular case study. Formative and summative assessment mechanisms will be presented alongside future directions and potential pitfalls of such a course. Motivating the proposed course is a desire to fill the comparative void in moral reasoning for statistics and data science curricula.

The Massive Open Online Courses (MOOC) market has transformed the landscape of education, revolutionizing how learners access knowledge across the globe. As an innovative approach to education, MOOCs offer free or low-cost online courses delivered by universities, educational institutions, and industry leaders, maki

Replication Data for “Student Self-Assessment of the Graduate Course: a multidimensional model proposal” Description This dataset is derived from a study that develop a self-assessment model by students and alumni of Graduate Courses. A survey was carried out with students and alumni at a higher education institution in southern Brazil and a valid sample of 2037 respondents was obtained. The research was approved by the Research Ethics Committee (CAAE: 00982218.0.0000.5346). Steps to reproduce The data analysis and statistical procedures are described in the article " Student Self-Assessment of the Graduate Course: a multidimensional model proposal” Who is willing to reproduce this research may read the method section of that article and follow the steps we perfomed. This data set also allows researchers to make other and fhurteher statistical analysis.

The uploaded files serve as a concise but meaningful training data set in the Galaxy training network (https://galaxyproject.github.io/training-material/).

HEK and E.coli cell pellets were lysed with 5 % SDS, 50 mM triethylammonium bicarbonate (TEAB), pH 7.55. The obtained protein extracts were reduced by adding f.c. 5 mM TCEP and alkylated by the addition of f.c. 10 mM iodacetamide. Protein digestion and purification was performed on S-Trap columns. To ensure protein binding to the S-Trap columns, samples were acidified to a final concentration of 1.2 % phosphoric acid (~ pH 2). Six times the sample volume S-Trap buffer (90% aqueous methanol containing a final concentration of 100 mM TEAB, pH 7.1) was added to the samples which were then loaded on the columns and washed with S-Trap buffer. Protein digestion was performed with trypsin and LysC for one hour at 47 °C. Peptides were eluted in three steps with (1) 50 mM TEAB, (2) 0.2 % aqueous formic acid and (3) 50 % acetonitrile containing 0.2 % formic acid. Eluted peptides of HEK and E.coli were mixed in two different ratios and four replicates of each Spike/in ratio were measured and analysed using OpenSwathWorkflow in Galaxy. Results were exported using PyProphet and can be used for the statistical analysis and detection of the two different Spike-in Ratios. The Spike-in ratios were the following:

Sample HEK E.coli
Spike_in_1 2.5 0.15 Spike_in_2 2.5 0.80

Besides the two PyProphet export files, we uploaded a sample annotation file as well as a comparison matrix file. Additionally, we uploaded the Galaxy MSstats training result files: MSstats_ComparisonResult_export_tabular and MSstats_ComparisonResult_msstats_input.