In 2017, there were just over ************** overseas students who started an entry level qualification in engineering or an engineering related course in Victoria, Australia. This represents an increase from the previous year.

This dataset is a sample from the TalkingData AdTracking competition. I kept all the positive examples (where is_attributed == 1), while discarding 99% of the negative samples. The sample has roughly 20% positive examples.

For this competition, your objective was to predict whether a user will download an app after clicking a mobile app advertisement.

File descriptions

train_sample.csv - Sampled data

Data fields

Each row of the training data contains a click record, with the following features.

• ip: ip address of click.
• app: app id for marketing.
• device: device type id of user mobile phone (e.g., iphone 6 plus, iphone 7, huawei mate 7, etc.)
• os: os version id of user mobile phone
• channel: channel id of mobile ad publisher
• click_time: timestamp of click (UTC)
• attributed_time: if user download the app for after clicking an ad, this is the time of the app download
• is_attributed: the target that is to be predicted, indicating the app was downloaded

Note that ip, app, device, os, and channel are encoded.

I'm also including Parquet files with various features for use within the course.

Singapore Graduates from Higher Degree Courses: Engineering Sciences data was reported at 2,214.000 Person in 2017. This records an increase from the previous number of 1,933.000 Person for 2016. Singapore Graduates from Higher Degree Courses: Engineering Sciences data is updated yearly, averaging 1,797.000 Person from Dec 1993 (Median) to 2017, with 25 observations. The data reached an all-time high of 2,214.000 Person in 2017 and a record low of 120.000 Person in 1993. Singapore Graduates from Higher Degree Courses: Engineering Sciences data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Singapore – Table SG.G073: Education Statistics: Graduates from Educational Institutions.

In 2017, there were *** male domestic students who started a diploma in engineering or an engineering related course in Australia. In the same year, ** female domestic students commenced a diploma in engineering or an engineering related course.

Requirements engineering (RE) studies concepts, tools, and methods which allow consideration and analysis of a problem. In software engineering, requirements engineering often starts at the beginning of a software project and provides structure to understand who is involved, what the software needs to do, and how well it must perform its functions. This early consideration of the problem is both important and difficult, as it often touches on the bridge between the complex and human world of the problem and the (also complex) world of the system.Often, students find RE courses challenging: there is usually not a single correct answer,i.e., there are many correct ways to write requirements or draw models; and, for those who are accustomed to technical activities like coding and design, a focus on human actions and communication can be surprising. Past work in the Requirements Engineering Education and Training Workshop (REET) have explored some of these issues. These challenges can manifest differently for students depending on their level of education and work experience. Those who lack experience with complex software often do not see the need for RE activities, while those who have detailed technical experience, often find it hard to abstract away from the details.In this work, I describe artifacts aimed to teach RE to first-year software engineering students.I provide my lecture slides, assignments, some assignment cases, and exercises with a description of the context, the artifacts, and a discussion of challenges and possible modifications. My hope is that these artifacts will help others to design and apply effective education in RE topics.

Singapore Enrolment In Higher Degree Courses: Engineering Sciences data was reported at 5,902.000 Number in 2016. This records a decrease from the previous number of 5,967.000 Number for 2015. Singapore Enrolment In Higher Degree Courses: Engineering Sciences data is updated yearly, averaging 5,934.500 Number from Dec 1993 (Median) to 2016, with 24 observations. The data reached an all-time high of 6,492.000 Number in 2009 and a record low of 1,333.000 Number in 1993. Singapore Enrolment In Higher Degree Courses: Engineering Sciences data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Singapore – Table SG.G070: Education Statistics: Enrolment.

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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

Singapore Enrolment in Uni 1st Deg Courses: Females: Engineering Sciences data was reported at 5,508.000 Number in 2017. This records an increase from the previous number of 5,342.000 Number for 2016. Singapore Enrolment in Uni 1st Deg Courses: Females: Engineering Sciences data is updated yearly, averaging 4,968.000 Number from Dec 1991 (Median) to 2017, with 27 observations. The data reached an all-time high of 5,543.000 Number in 2014 and a record low of 975.000 Number in 1992. Singapore Enrolment in Uni 1st Deg Courses: Females: Engineering Sciences data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Singapore – Table SG.G070: Education Statistics: Enrolment.

This data was collected over two academic years, 2018/19 and 2019/20 from students enrolled in two courses at UTA: ART 4365 Technology in Art Education and IE 4340 Engineering Project Management. The data collection instruments were pre- and post-self assessment surveys, distributed at the beginning and end of the semester. The data includes student-self reported competencies for Maker Competencies 9 and 10, "Assembles Effective Teams" and "Collaborates Effectively" on a range of 1 (low) to 5 (high).

In 2017, there were around ***** thousand domestic male students who started an engineering or an engineering related course in Australia. This represents a slight decrease from the previous year, though still significantly more than the domestic female students commencing engineering-related studies in that year.

Abstract The notion of competence is increasingly present in the academic and professional environments. In this context, engineering education in Brazil has established, through curricular guidelines, a roll of competences to be developed during the undergraduate courses. Based on this legislation, a question arises on the adequacy of these definitions regarding what is actually required to professionals. Thus a survey was conducted with engineers in the work market to verify the adequacy of the established legislation to the competences required in practice. It was observed that technical knowledge is fundamental, and well-developed in educational institutions; however, the management area, although addressed to some degree in the guidelines, is not developed during graduation, becoming a deficiency to be corrected by professional engineers.

Context:

You are an Analytics Engineer at an EdTech company focused on improving customer learning experiences. Your team relies on in-depth analysis of user data to enhance the learning journey and inform product feature updates.

Objective:

• Your mission is to transform raw data into structured views that enable data analysts to effectively monitor and analyze user activities, performance patterns, and feedback. These insights are critical for data-informed decision-making within the Customer Success team.

Dataset Overview:

• Your company organizes content in a hierarchical structure, categorized as Track → Course → Topic → Lesson. Each lesson can take various formats, such as videos, practice exercises, exams, etc.
• Any learning activity done by the user on a lesson is stored in logs in the user_lesson_progress_log table. A user can have multiple logs for a lesson in a day.
• You have user registration data that store registration information and demographic information of users.
• A user can give feedback on a lesson multiple times.

DB Diagram: https://dbdiagram.io/d/627100b17f945876b6a93e54 (use the ‘Highlight’ option to understand the relationships)

Table Descriptions

track_table: Contains all tracks

course_table: Contains all courses

topic_table: Contains all topics

lesson_table: Contains all lessons

user_registrations: Contains the registration information of the users. A user has only one entry

user_lesson_progress_log: Any learning activity done by the user on a lesson is stored in logs. A user can have multiple logs for a lesson in a day. Every time a lesson completion percentage of a user is updated, a log is recorded here.

Example: If a user u1 has started the lesson lesson1 and completed 10% of the lesson at May 1st 2022 8:00:00 UTC. And, the user completed 30% of the lesson at May 1st 2022 10:00:00 UTC and 20% of the lesson at May 3rd 2022 10:00:00 UTC, then the logs are recorded as follows:

user_feedback: The table contains the feedback data given by the users. A user can give feedback to a lesson multiple times. Each feedback contains multiple questions. Each question and response is stored in an entry.

Singapore Enrolment in Uni 1st Deg Courses: Males: Engineering Sciences data was reported at 13,314.000 Number in 2016. This records an increase from the previous number of 12,971.000 Number for 2015. Singapore Enrolment in Uni 1st Deg Courses: Males: Engineering Sciences data is updated yearly, averaging 12,404.500 Number from Dec 1991 (Median) to 2016, with 26 observations. The data reached an all-time high of 13,359.000 Number in 2006 and a record low of 5,847.000 Number in 1991. Singapore Enrolment in Uni 1st Deg Courses: Males: Engineering Sciences data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Singapore – Table SG.G061: Education Statistics: Enrolment.

Governmental and educational organizations have pointed out that students coming into the biological sciences require stronger skills in statistics and data modeling that are not usually addressed in typical engineering-based calculus courses. Our work here documents how faculty in biology and mathematics addressed this issue at a large urban university in the southwestern USA. The Calculus for Life Sciences course was redesigned to integrate data analysis and engage students in activities connecting the foundational and practical aspects of statistics-based calculus for professionals in life sciences. The goal was to obtain a better-prepared cohort of students with a positive perception of the use of mathematics and statistics in their careers. Over the course of four semesters, the course doubled the average enrollment of students, the failure rate reduced by more than 50%, and students reported positive attitudinal responses regarding the application of math to biological studies. The perception of mastery reported by students throughout the semester, however, did not correlate with the mastery of skills demonstrated on graded activities. This article provides a case study of the successes and pitfalls that we encountered as we attempted to shift all aspects of the course, including pedagogy, grading, and curriculum.

This dataset contains the records of anonymised user interactions in seven online courses at a Higher Education institution in Brazil. For each course, the dataset covers a period spanning from 2017.1 to 2018.1 equivalent to three Brazilian academic periods. All online courses used the Moodle learning platform.The dataset covers the following courses:F - An introductory course in Philosophy - mandatory for all studentsC - An introductory course in Religion - mandatory for all studentsS - An introductory course in Political Theory - mandatory for students of the School of Humanities and Social SciencesM1 - Differential and Difference Equations course - mandatory for students of the School of Engineering and Exact SciencesM2 - Single Variable Calculus course - mandatory for students of the School of Engineering and Exact SciencesE9 - An introductory course in the Design of Control Systems - mandatory for students of the School of Industrial EngineeringE0 - Foundations of Engineering course - mandatory for all students of the School of EngineeringThe data is compressed in .zip format and can be uncompressed by standard compression utilities. Each course has three separate files grouped by user interactions from different academic periods. For example, the records for the course 'F' are split into F1, F2 and F3. F1 covers the records of the first academic period whereas F2 and F3 contain the records for the second and third academic periods respectively. Note that each instance of a course is independent and that the same student (identified by the same id) may only occur in the same course but in different academic periods iff s/he failed and opted to retake that course in one of the following courses covered by the data available here. The student id is preserved among the courses and academic periods.A description of the log fields contained in this dataset can be found at: https://docs.moodle.org/dev/Event_2#Information_contained_in_events

Dataset about the evaluation of source code quality improvement during a Software Quality course. Content of the dataset:* Task Description;* Questionnaire Templates;* Questionnaire data (initial and final);* Manual evaluation data (quality of source code);* Automated evaluation data (static code analysis with SonarQube);* Anonymized source code of the submissions;More details in the paper: Stanislav Chren, Martin Macak, Bruno Rossi, and Barbora Buhnova (2022). Evaluating Code Improvements in Software Quality Course Projects, in Proceedings of the 25th International Conference on Evaluation and Assessment in Software Engineering (EASE).

Abstract This paper aims to present the results of a first-time experiment conducted using the concept of inverted classroom (or flipped classroom) in the discipline of Differential and Integral Calculus I, in Engineering courses at ITA (Instituto Tecnológico de Aeronáutica). The flipped classroom is characterized, according to Valente (2014), as a form of e-learning, in which the contents and instructions are studied online way before class time class, where practical activities will be carried out, such as problem solving and projects, group discussion, among others. This study highlights the potential, some of the problems faced, and the opinions of students in the methodology. This also shows that, regardless of the difficulties faced during the experience, there is a need for innovative approaches regarding Calculus teaching in higher education.

Over recent decades, literature on assessment in higher education has intensified generating a wealth of frameworks to inform practice. Generic frameworks for assessment practice are sometimes perceived as missing subject-specific considerations. This literature review proposes to (a) map the current landscape of assessment in engineering education and to (b) help drive the field forwards by identifying elements of assessment that require discipline-specific consideration as a foundation to formulate good practice. Sources were identified using a broad set of keywords related to assessment in engineering education. Inclusion criteria considered papers about university-level education and were published, in English, between 2012 and 2018. The review establishes that much literature has focused on design, accreditation and marking with much less literature on key concerns in practice such as workplace assessment, student engagement and programme level design. Based on the results, recommendations are made for research areas where greater focus is needed to advance further engineering specific insights.

The joint UNESCO-OECD-Eurostat (UOE) data collection on formal education systems provides annual data on student participation and completion of educational programmes as well as data on personnel, cost and type of resources devoted to education. The reference period for non-monetary education data is the school year and for monetary data it is the calendar year. The International Statistics of Education and Training Systems ÔÇô UNESCO-UIS/OECD/Eurostat (UOE) Questionnaire aims to provide the data required by international bodies, in addition to offering results at the national level. It is a synthesis and analysis operation that appears in the National Statistical Plan 2021-2024 (Prog. 8677) and is carried out by the S.G. of Statistics and Studies of the Ministry of Education and Vocational Training in collaboration with the Ministry of Universities and the National Institute of Statistics. Its purpose is to integrate the statistical information of the activity of the educational-training system in its different levels of education in order to meet the demands of international statistics, of the same name, requested by Eurostat, OECD and UNESCO-UIS. A selection of tables with data derived from this statistic is provided below, together with a presentation summary note:

Twenty European Universities currently offering specific mining engineering curricula were found: 16 bachelor’s programmes, 18 master’s programmes: in total 34 programmes. 968 courses both from bachelor’s and master’s mining degrees from european universities are listed and categorised using following keywords:

Basic engineering, Economics and Law courses (BEEL) Mathematics, Physics, Chemistry, Basic Computer training, Thermodynamics, Technical drawing, General Science, Orientational Courses, Robotics, Hydraulics, Economy, Accounting, Taxation, Legislation, Licensing and Intellectual Property Mining Engineering specific courses (MESC) Geomatics: Surveying, Geodesy, Deposit Modelling, Data Management; Geomechanics: Rock and Soil Mechanics, Geophysics, Numerical analysis; Geosciences: Geology, Mineralogy, Petrology, Earth science, Deposits, Hydrogeology; Materials: Metals, Ceramics, Building materials, High temperature processes; Operations: Open pit mining, Underground mining, Drilling & Blasting, Ventilation and Water Management, Equipment and Machines, Transport Systems, Historic and Worldwide mining; Processing: Waste treatment, Recycling, Plant operations, Sampling and analysis; Elective Courses specific to mining; Health, Safety, Environment and Sustainability courses (HSES) Occupational Health and Safety, Security and Risk Analysis, Sustainability, Environment, Rehabilitation, Reclamation, Recovery, Post Mining and Remediation; Social Skills, Problem based Learning, Scientific work (SPBLS) Project work, Studies, Seminars, Languages, Sociology, Politics, Organization and Strategy, Management, Internships, Field trips, Excursions, Scientific writing, Bachelors and Master Thesis and preparatory courses therefore; Digitalisation Information technology, Automation, Computer Science, Programming, CAD, Robotics, Algorithms, Data Handling, Data Structure, Simulations, Geo- and engineering statistics, Internet, computer, control engineering and numerical methods; Modelling: this keyword is not included here but courses containing the keyword could be using CAD and or geologic modelling software

List of Abbreviations:

MUL , Montanuniversität Leoben ; ULiège , Université de Liège ; UniZg , University of Zagreb ; VŠB-TUO , Technická univerzita Ostrava ; RWTH Aachen , Rheinisch-Westfälische Technische Hochschule Aachen ; TU Clausthal , Technische Universität Clausthal ; TUBAF , Technische Universität Bergakademie ; NTUA , Freiberg ; Miskolc , National Technical University Of Athens ; Torino , University of Miskolc ; AGH , Politecnico di Torino ; PolSl , Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie ; , Politechnika Śląska ; ULisboa , Universidade de Lisboa ; UPet , University of Petrosani ; TUKE , Technická univerzita v Košiciach ; UniLJ , Univerza v Ljubljani ; ULE , Universidad de León ; UPC , Universitat Politècnica de Catalunya · BarcelonaTech ; UPM , Universidad Politécnica de Madrid ; LTU , Lulea university of technology ; , ; BEEL , Basic engineering, Economics and Law courses ; MESC , Mining Engineering specific courses ; HSES , Health, Safety, Environment, Sustainability courses ; SPBLS , Social Skills, Problem based Learning, Scientific work courses ; ELEC , Elective Courses not specific to mining ;