How is data science used in the oil and Gas Industry?
Data is gathered, a predictive model is established, predictions are produced, and the model is validated or updated when new data becomes available. Predictive models are statistical models used to forecast outcomes. Big data is analyzed and organized using prediction models in data science.
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Understanding the Basics of Data Acquisition and Data Wrangling
Data Acquisition
Paper records were replaced with digital records that were acquired and stored using computer-based data capture. Data acquisition refers to the automatic gathering of information from sensors, equipment, and gadgets in a factory, a lab, or the field. Modern research, development, and production have undergone radical change due to the use of computers in different data collection, control, and analysis processes during the past few years.
Data wrangling
Data wrangling is the act of organizing and cleaning up large and disorganized data sets, so they are simpler to access and analyze.
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How Does Data Science Work in the Human Resource (HR) Domain?
unlike earlier times when it mainly depended on detailed surveys and psychological reviews, the HR industry is now entering an era of scientific analysis based on smart technology. New big data-driven methodologies can provide vital benchmarks and invaluable insights utilizing current data instead of conducting countless surveys or engaging with employees to determine their loyalty or job happiness.
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Tesla Inc., preliminarily known as Tesla Motors, was established on 1st July 2003. Tesla Inc. It is one of the leading electric auto manufacturing companies in the current electric auto race. Tesla does everything from designing, developing, and manufacturing to dealing with its vehicles that run entirely on electric batteries. Besides auto manufacturing, Tesla also has energy storehouse systems. The Company has two different functional corridors: automotive, energy generation, and storehouse.
1. Head First Statistics: A Brain-Friendly Guide
2. Practical Statistics for Data Scientists
3. Introduction to Probability
4. Introduction to Machine Learning with Python: A Guide for Data Scientists
5. Python Machine Learning by Example
6. Pattern recognition and machine learning
