Data Science and Machine Learning Platforms Statistics


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Data Science And Machine Learning Platforms Statistics 2023: Facts about Data Science And Machine Learning Platforms outlines the context of what’s happening in the tech world.

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Top Data Science And Machine Learning Platforms Statistics 2023

☰ Use “CTRL+F” to quickly find statistics. There are total 15 Data Science And Machine Learning Platforms Statistics on this page 🙂

Data Science And Machine Learning Platforms “Latest” Statistics

  • According to the U.S. Bureau of Labor Statistics, employment in the industry will increase by 27.9% through 2026 as a result of the need for data science capabilities.[1]
  • A recent report by IBM and Burning Glass states that there will be 364K new job openings in data-driven professions by 2020 in the US.[1]
  • According to KD Nuggets, there were 48 instruments with 2% or more of the market in 2018, and among them, 14 had a loss in share while 34 saw a gain.[2]
  • The top 11 tools have the same percentage of market share in 2019 as they had in 2018.[2]
  • RapidMiner kept its share at around 51%, which was a reflection of both a large user base and a successful campaign to motivate its users.[2]
  • The tools that were part of the KD Nuggets poll in 2018 that reached at least 25 voters and had a share increase of 20% or more.[2]
  • The 20th annual KD Nuggets Software Poll had over 1,800 participants, and the average voter chose 6.1 different tools, so voters with just one choice stood out.[2]
  • The inaugural “Netflix Prize” competition was sponsored in 2006 by media services company Netflix to identify a software that could more accurately forecast user preferences and raise the accuracy of its current Cinematch movie recommendation algorithm by at least 10%.[3]
  • Some machine learning solutions make use of data and six learning algorithms, which operate under the premise that previous success is a good predictor of future success.[3]
  • The top Data Science courses are all included in Coursera Plus, an annual subscription that provides access to more than 3,000 courses, Specializations, Professional Certificates, and Guided Projects.[4]
  • In addition to the enormous demand, there is a severe lack of data scientists who are qualified, with 39% of the most demanding data science occupations needing a degree greater than a bachelors.[1]
  • In the case of the MicroMasters, passing the classes and earning a certificate will credit toward 30% of the Rochester Institute of Technology (RIT)’s complete Master of Science in Data Science degree.[5]
  • Apache Hadoop was identified as the second most crucial ability for a data scientist, according to a research by Villanova University, by 49% of data scientists.[6]
  • In 2018, Python surpassed R as the most widely used language for data science, with 66% of data scientists reporting using it daily.[6]
  • The Villanova University analysis on the data analytics skill gap states that 56% of data scientist employment include SQL as a necessity.[6]

Also Read

How Useful is Data Science and Machine Learning Platforms

One of the key benefits of data science and machine learning platforms is their ability to significantly enhance the efficiency and effectiveness of decision-making processes. By harnessing the power of advanced analytics, organizations can gain deeper insights into their data, identify patterns and trends, and make data-driven decisions that are informed and strategic. This allows companies to optimize their operations, improve customer satisfaction, and stay ahead of the competition in an increasingly digital landscape.

Moreover, data science and machine learning platforms can help organizations better understand their customers and target market. By analyzing customer data, companies can gain valuable insights into consumer behavior, preferences, and buying patterns, allowing them to tailor their products and services to meet the unique needs of their target audience. This personalized approach can lead to increased customer satisfaction, loyalty, and ultimately, improved business performance.

Furthermore, data science and machine learning platforms enable organizations to unlock the full potential of their data. In today’s data-driven world, businesses are inundated with vast amounts of data from various sources such as social media, sensors, and IoT devices. Data science and machine learning platforms provide the tools and techniques needed to process, analyze, and extract meaningful insights from this data, driving innovation and helping organizations unlock new revenue streams.

In addition, data science and machine learning platforms have the potential to revolutionize healthcare, education, transportation, and many other industries. By leveraging predictive analytics and machine learning algorithms, healthcare providers can improve patient outcomes, reduce costs, and enhance the quality of care. In the education sector, data science can help personalize learning experiences for students, identify at-risk individuals, and optimize teaching methods. In transportation, companies can use data science to improve route planning, optimize fuel efficiency, and enhance safety measures.

However, it is important to note that while data science and machine learning platforms offer tremendous benefits, they also come with certain challenges. One such challenge is the need for organizations to have the necessary tools, resources, and expertise to effectively leverage these platforms. Implementing data science and machine learning solutions requires a diverse set of skills, including expertise in statistics, programming, and data visualization, as well as a deep understanding of the domain in which the solutions are being deployed.

Another challenge is the need to address ethical considerations around data privacy, security, and bias. As organizations collect, analyze, and utilize vast amounts of data, there is a growing need to ensure that data is handled responsibly, ethically, and in compliance with regulations such as GDPR and CCPA. Moreover, biases in data and algorithms can lead to unfair treatment, discrimination, and other negative consequences if not addressed proactively.

In conclusion, data science and machine learning platforms have the potential to transform businesses and industries, driving innovation, improving decision-making, and unlocking new opportunities. However, organizations must be mindful of the challenges and ethical considerations that come with leveraging these platforms, and take proactive steps to ensure responsible and ethical use of data science and machine learning technologies.

Reference

  1. mit – https://micromasters.mit.edu/ds/
  2. kdnuggets – https://www.kdnuggets.com/2019/05/poll-top-data-science-machine-learning-platforms.html
  3. wikipedia – https://en.wikipedia.org/wiki/Machine_learning
  4. medium – https://medium.com/javarevisited/5-best-mathematics-and-statistics-courses-for-data-science-and-machine-learning-programmers-bf4c4f34e288
  5. learndatasci – https://www.learndatasci.com/best-data-science-online-courses/
  6. techtarget – https://www.techtarget.com/searchenterpriseai/tip/11-data-science-skills-for-machine-learning-and-AI

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