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  1. 1 vote
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    khushnuma commented  · 

    Data science is an interdisciplinary field that uses statistical methods, algorithms, and data analysis to extract insights and knowledge from structured and unstructured data. It combines skills from mathematics, computer science, and domain expertise to interpret complex data sets. Data scientists clean, analyze, and visualize data to inform decision-making and solve problems. Techniques such as machine learning and predictive modeling are commonly employed to uncover patterns and trends. Ultimately, data science aims to convert raw data into actionable insights, driving improvements in various industries like healthcare, finance, marketing, and technology.

    If you have any dought you can visit here: https://uncodemy.com/course/data-science-training-course-in-delhi

  2. 1 vote
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    khushnuma commented  · 

    Java offers numerous benefits, making it a popular choice for developers. Its platform independence, thanks to the Java Virtual Machine (JVM), allows code to run on any device with a JVM installed. Java's strong type checking enhances code reliability, while its rich standard library provides pre-built functions for common tasks. Additionally, Java's object-oriented design promotes modularity and code reuse. The language has robust community support, extensive documentation, and a wealth of frameworks, like Spring and Hibernate, that streamline development. Lastly, Java’s performance has improved over the years, making it suitable for a wide range of applications, from web to mobile.

  3. 1 vote
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    khushnuma commented  · 

    An online data analytics certificate typically teaches skills such as data collection and cleaning, statistical analysis, and data visualization. You'll learn to use tools like Excel, SQL, and data visualization software (e.g., Tableau or Power BI) to interpret and present data effectively. Courses often cover basic and advanced analytics techniques, including hypothesis testing, regression analysis, and predictive modeling. You'll also develop the ability to translate data insights into actionable business strategies. Additionally, the program usually emphasizes data ethics and effective communication of findings to stakeholders.

    for more information visit here: https://uncodemy.com/course/data-analytics-training-course-in-nagpur

  4. 1 vote
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    khushnuma commented  · 

    A data analyst is a professional who collects, processes, and analyzes data to help organizations make informed decisions. They use statistical tools and software to interpret complex datasets, identify trends, and generate actionable insights. Their work often involves cleaning and organizing data, creating reports, and visualizing information to communicate findings effectively. Data analysts play a crucial role in various industries by supporting strategic planning, optimizing operations, and solving business problems through data-driven insights. Their skills bridge the gap between raw data and decision-making, making them essential in today’s data-driven world.

  5. 1 vote
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    khushnuma commented  · 

    As of 2024, the average salary for web developers in the United States typically ranges between $60,000 and $85,000 per year. Factors such as experience, location, and specialization can significantly influence earnings. For instance, developers in high-cost living areas like San Francisco or New York often earn more, while those in smaller markets might earn less. Additionally, web developers with expertise in emerging technologies or advanced skills can command higher salaries. Junior developers usually start at the lower end of the spectrum, while senior developers or those in managerial roles can earn significantly more.

  6. 1 vote
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    khushnuma shared this idea  · 
  7. 1 vote
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    khushnuma shared this idea  · 
  8. 1 vote
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  9. 4 votes
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    khushnuma commented  · 

    very well explained

    If you have any doubts, you can visit here: https://uncodemy.com/course/software-testing-course-in-nagpur

  10. 1 vote
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    khushnuma commented  · 

    You’ve summarized it perfectly: front-end, back-end, databases, version control, and deployment—everything needed for a full-stack web developer. If you have any doubts, you can visit here. https://uncodemy.com/course/full-stack-development-training-course-in-nagpur

  11. 1 vote
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    khushnuma commented  · 

    Business intelligence involves analyzing data to make informed decisions, identify trends, and improve business performance and strategy.

  12. 2 votes
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    khushnuma commented  · 

    Key skills in embedded programming include proficiency in C and C++, as these languages are commonly used for low-level hardware interactions. Understanding microcontroller architecture, such as ARM or AVR, is crucial. Familiarity with real-time operating systems (RTOS) and interrupt handling is essential for managing time-sensitive tasks. Knowledge of hardware interfaces like I2C, SPI, and UART enables communication between devices. Debugging skills with tools like JTAG and logic analyzers are vital for troubleshooting. Additionally, power management, memory optimization, and understanding of embedded protocols (e.g., CAN, Modbus) are important for creating efficient and reliable embedded systems.

  13. 1 vote
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    khushnuma commented  · 

    In IoT analytics, data science is crucial for processing and interpreting vast amounts of data from connected devices. It involves collecting data from sensors and IoT devices, cleaning and transforming this data, and applying statistical and machine learning techniques to extract meaningful insights. Data science enables predictive maintenance by identifying patterns that signal equipment failure, optimizing operations through real-time analytics, and enhancing decision-making with actionable insights. It also supports anomaly detection to identify unusual behaviors and improve security. Overall, data science transforms raw IoT data into valuable information for better efficiency, safety, and innovation.

  14. 1 vote
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    khushnuma commented  · 

    Overfitting in machine learning occurs when a model learns the training data too well, capturing noise and outliers instead of the underlying pattern. This results in high accuracy on the training set but poor performance on unseen test data because the model fails to generalize. Overfitting happens when the model is too complex relative to the amount of data, such as having too many parameters or layers. Techniques to combat overfitting include simplifying the model, using regularization methods, employing cross-validation, and augmenting the training data.

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