Big Data

Big Data

Big data is a term applied to the dataset that is large, fast or complex. These are high in volume, high velocity or high variety. This is the reason, it is also known as the three Vs. Big data is extracted from sensors, devices, video/audio, networks, log files, web, and social media that are generated in real-time and at a very large scale. It is difficult to process these data through the traditional methods, hence the concept of big data gaining attention that uses advanced analytic techniques against very large, diverse data sets. Big data analytics is used by researchers and businesses to make better and faster decisions. Advanced techniques to gain new insights for text analytics, machine learning, predictive analytics, statistics processing.

The benefits of big data include :-

    1. More complete answers because of more information
    2. More complete answers define more confidence in the data

Use of Big data

    1. In oil and gas companies big data is used to identify potential drilling locations and monitor pipeline operations
    2. Risk management and real-time analysis of market data is done using big data systems
    3. Manufacturers and transportation companies use big data in their supply chain management and while optimising delivery routes.s
    4. Emergency response, crime prevention and smart city initiatives are taken by using these big data.

At SPSS tutor, our analytics team harnesses massive amounts of complex data to perform statistical tests and predictive analyses for your firm. Our SPSS experts like the statisticians and econometricians can identify the most appropriate data through Methodologies and sampling and subsampling strategies. Most of the firms use the standard process of R to complete statistical analysis of large-scale projects, our team additionally work with SPSS, SAS, STATA, Python, and MATLAB software also, depending on your firm’s preferences and dataset.

The professionals in our team can help you in performing a subsampling-based method analysis for any of the following methods :-

    1. Bootstrapping
    2. Leveraging
    3. Regression tree
    4. LASSO
    5. Mean-log likelihood
    6. Markov chain Monte Carlo (MCMC) and parallel MCMC
    7. Aggregated estimating equations
    8. Majority voting
    9. Random forest
    10. Ensemble
    11. Screening for ultrahigh dimensions
    12. Online updating for data streams

From the initial level of methodology and survey development to designing and implementing the analysis plan to the final publication-ready write-up of your results, we stay connected to you with our subject experts. Contact us by filling the request form.

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