A quick refresher course for those who have had statistical training in the past or a fast-paced introduction to basic statistics for beginners. Statistical measures such as percentages, averages, frequency and standard error are used widely. But how are they calculated, and exactly what do they tell us? This one day workshop will help participants develop an appreciation of the potential of statistics and a critical eye of when and how they should or shouldn't be used.

Dataset

This dataset was created by Saleh Zeer

Contents

These are the materials developed for the Mo(Wa)²TER Data Science workshop, which is designed for upper level and graduate students in environmental engineering or industry professionals in the water and wastewater treatment (W/WWT) fields. Working through this material will improve a learner’s data analysis and programming skills with the free R language and will focus exclusively on problems arising in W/WWT. Training in basic R coding, data cleaning, visualization, data analysis, statistical modeling, and machine learning are provided. Real W/WWT examples and exercises are given with each topic to strengthen and deepen comprehension. These materials aim to equip students with the skills to handle data science challenges in their future careers. Materials were developed over three offerings of this workshop in 2021, 2022, and 2023. At the time of publication, all code runs, but we provide no guarantees on future versions of R or packages used in this workshop.

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.

Numerous studies demonstrating that statistical errors are common in basic science publications have led to calls to improve statistical training for basic scientists. In this article, we sought to evaluate statistical requirements for PhD training and to identify opportunities for improving biostatistics education in the basic sciences. We provide recommendations for improving statistics training for basic biomedical scientists, including: 1. Encouraging departments to require statistics training, 2. Tailoring coursework to the students’ fields of research, and 3. Developing tools and strategies to promote education and dissemination of statistical knowledge. We also provide a list of statistical considerations that should be addressed in statistics education for basic scientists.

The Data Visualization Workshop II: Data Wrangling was a web-based event held on October 18, 2017. This workshop report summarizes the individual perspectives of a group of visualization experts from the public, private, and academic sectors who met online to discuss how to improve the creation and use of high-quality visualizations. The specific focus of this workshop was on the complexities of "data wrangling". Data wrangling includes finding the appropriate data sources that are both accessible and usable and then shaping and combining that data to facilitate the most accurate and meaningful analysis possible. The workshop was organized as a 3-hour web event and moderated by the members of the Human Computer Interaction and Information Management Task Force of the Networking and Information Technology Research and Development Program's Big Data Interagency Working Group. Report prepared by the Human Computer Interaction And Information Management Task Force, Big Data Interagency Working Group, Networking & Information Technology Research & Development Subcommittee, Committee On Technology Of The National Science & Technology Council...

The Series of the Statistics of Non-University Teachings aims to show the evolution of the basic variables and statistical indicators of these teachings. The data provided are the result of a thorough review, carried out in 2006 in order to further homogenise the concepts and coverage for the different courses to which the information relates. This revision may imply slight differences for some variables with respect to the data that appear in the Detailed Results of the corresponding course.

Dataset compiles basic statistics (capital, land area, population) for all 12 Dutch provinces, intended solely for the workshop. Mention that data were extracted from open sources like Wikipedia

Statistics for 2022 training

Training materials for 'Data & services', session 7 of the RDM Support basic training course for information specialists. RDM Support is a basic training course in research data management (support) for information specialists. The training course was developed by Mariëtte van Selm for the information specialists of the Library of the University of Amsterdam (UvA), within the framework of the RDM Support project (2013-2015). The training course was held from January to April 2014.

This section presents statistical information referring to the enrolled student body from the Statistics on Non-university Education carried out by the Sub-Directorate General of Statistics and Studies of the Ministry in cooperation with the statistical services of the Departments of Education of the Autonomous Communities. Information is provided annually on the different characteristics of the student body enrolled in all non-university General Regime, Special Regime and Adult Education courses.

The alternative training class collects statistics on disciplinary cases of male slaves at each level, uses the content of disciplinary cases to strengthen management skills, maintain team discipline, and strengthen publicity and education to avoid the recurrence of similar cases.

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 for data-driven decisio

Training materials for 'Rules concerning research data', session 5 of the RDM Support basic training course for information specialists. RDM Support is a basic training course in research data management (support) for information specialists. The training course was developed by Mariëtte van Selm for the information specialists of the Library of the University of Amsterdam (UvA), within the framework of the RDM Support project (2013-2015). The training course was held from January to April 2014.

This publication presents a synthesis of the different statistics on Vocational Education and Training disseminated in the reference year, taking advantage of the technical notes and the link to the EDUCAbase data tables.

Abstract: The aim of this paper is to assess the levels of statistical literacy and perception of Early Teaching Training (ITE) students and teachers working in the Chilean schooling system. Two measuring tools were applied: (1) statistical literacy and (2) perception, both validated statistically. The results of this analysis show that, in general, ITE students and working teachers present low achievement percentage on decoding textual situations, delivering unlikely arguments with little basis on basic statistical concepts.

Pen-and-paper homework and project-based learning are both commonly used instructional methods in introductory statistics courses. However, there have been few studies comparing these two methods exclusively. In this case study, each was used in two different sections of the same introductory statistics course at a regional state university. Students’ statistical literacy was measured by exam scores across the course, including the final. The comparison of the two instructional methods includes using descriptive statistics and two-sample t-tests, as well authors’ reflections on the instructional methods. Results indicated that there is no statistically discernible difference between the two instructional methods in the introductory statistics course.

Education and further studies: refers to various learning, education and related information collections.

The Big Data Interagency Working Group (BD IWG) held a workshop, Measuring the Impact of Digital Repositories, on February 28 - March 1, 2017 in Arlington, VA. The aim of the workshop was to identify current assessment metrics, tools, and methodologies that are effective in measuring the impact of digital data repositories, and to identify the assessment issues, obstacles, and tools that require additional research and development (R&D). This workshop brought together leaders from academic, journal, government, and international data repository funders, users, and developers to discuss these issues...

RDM Support is a basic training course in research data management (support) for information specialists. The training course was developed by Mariëtte van Selm for the information specialists of the Library of the University of Amsterdam (UvA), within the framework of the RDM Support project (2013-2015). The training course was held from January to April 2014.