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]

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How Useful is Data Science and Machine Learning Platforms

One of the primary reasons why data science and machine learning platforms are so useful is their ability to analyze large volumes of data and extract meaningful patterns and trends. In the past, dealing with huge amounts of data was a daunting task that required significant manual effort and time. However, with the advent of data science and machine learning platforms, this process has been streamlined and simplified. These tools can automatically process and analyze massive datasets, uncovering insights that would have been difficult, if not impossible, for humans to discern on their own.

Moreover, data science and machine learning platforms are incredibly versatile and can be applied to a wide range of scenarios. Whether it’s predicting customer behavior, optimizing manufacturing processes, or detecting anomalies and fraud, these tools can be tailored to meet the specific needs and challenges of any given situation. The flexibility and adaptability of data science and machine learning platforms make them invaluable assets for organizations looking to gain a competitive edge in today’s data-driven world.

Another key benefit of data science and machine learning platforms is their ability to continuously learn and improve over time. These tools use sophisticated algorithms that can analyze data, learn from patterns, and make predictions based on past data. As more data is fed into the system, the algorithms are refined and become more accurate in their predictions. This iterative process of learning and improvement is what sets data science and machine learning platforms apart from traditional analytics methods, which are typically static and do not evolve over time.

Furthermore, data science and machine learning platforms enable organizations to make data-driven decisions with confidence. By analyzing historical data and predicting future outcomes, these tools provide valuable insights that can guide strategic decision-making and drive business growth. Whether it’s identifying new market opportunities, optimizing resource allocation, or improving customer satisfaction, data science and machine learning platforms help organizations make informed choices that are backed by sound analysis and evidence.

In conclusion, the usefulness of data science and machine learning platforms cannot be overstated. These tools have revolutionized the way we leverage data and have empowered organizations to unlock new insights, drive innovation, and stay ahead of the competition. With their ability to analyze large volumes of data, adapt to changing circumstances, and make accurate predictions, data science and machine learning platforms are indispensable assets for modern businesses looking to thrive in the digital age. By harnessing the power of these tools, organizations can unlock new opportunities, solve complex problems, and achieve sustainable growth in an increasingly data-centric world.

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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