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.

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.

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.

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?

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.

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.

Python is one of the most popular programming languages among data scientists, partly due to its varied packages and capabilities. In 2021, Numpy and Pandas were the most used Python frameworks for data science, with a ** percent and ** percent share respectively.

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.

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

Students in statistics, data science, analytics, and related fields study the theory and methodology of data-related topics. Some, but not all, are exposed to experiential learning courses that cover essential parts of the life cycle of practical problem-solving. Experiential learning enables students to convert real-world issues into solvable technical questions and effectively communicate their findings to clients. We describe several experiential learning course designs in statistics, data science, and analytics curricula. We present findings from interviews with faculty from the U.S., Europe, and the Middle East and surveys of former students. We observe that courses featuring live projects and coaching by experienced faculty have a high career impact, as reported by former participants. However, such courses are labor-intensive for both instructors and students. We give estimates of the required effort to deliver courses with live projects and the perceived benefits and tradeoffs of such courses. Overall, we conclude that courses offering live-project experiences, despite being more time-consuming than traditional courses, offer significant benefits for students regarding career impact and skill development, making them worthwhile investments. Supplementary materials for this article are available online.

This dataset covers more than 1000 common data science concepts. It covers several topics related to statistics, machine learning, and artificial intelligence. It has two columns, one of which is questions or instructions, the other is responses to these instructions. The dataset can be used in Q&A and text generation.

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.

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

Anonymized dataset for MD Anderson Summer Student Stats Course.

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 **.

Overview of descriptive statistics.

Historical Dataset of Newark Sch Of Data Science And Information Technology is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2022-2023),Total Classroom Teachers Trends Over Years (2022-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2022-2023),Asian Student Percentage Comparison Over Years (2022-2023),Hispanic Student Percentage Comparison Over Years (2022-2023),Black Student Percentage Comparison Over Years (2022-2023),White Student Percentage Comparison Over Years (2022-2023),Diversity Score Comparison Over Years (2022-2023),Free Lunch Eligibility Comparison Over Years (2022-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2022-2023),Math Proficiency Comparison Over Years (2022-2023),Overall School Rank Trends Over Years (2022-2023)

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.

Poster on the a data science course presented at the 2019 BioQUEST & QUBES Summer Workshop

A majority of businesses responding to a 2023 survey said that investment in data and analytics was a top priority. However, only ** percent said that their efforts to improve data quality had been successful, highlighting an ongoing challenge faced by organizations across industry sectors.