10 data analysis techniques for big data statistics

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In the Information Age, data has gone from scarce to overwhelming. The key is to examine this overwhelming volume of available data so that companies can correctly interpret its implications.. But working with all this information involves the You need to have tools that allow the use of data analysis techniques. correct without forgetting the relevance of guaranteeing the quality of the information.

técnicas de análisis de datos

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Big data has made analysts produce many tools and data analysis techniques sophisticated that can be used by large institutions. But as these new data analysis techniques proliferate, we must not lose sight of some methods that have existed for a long time and that are still very precise.

If you're just getting started with Big Data analytics, We suggest starting with some basic principles, learn how to avoid your risks and then move towards more sophisticated data analysis techniques.

5 Traditional but accurate data analysis techniques

Before you jump into Application of more complex data analysis techniques., it is important to emphasize investing the time necessary to meet your pioneers. Among them, we can point out five:

  1. Arithmetic average. Is the sum of a list of numbers divided by the number of items in that list and is used to set the overall trend of a dataset. Finding the average also helps you get a quick snapshot of the information., since it is easy to calculate. Despite this, keep in mind that it can be a dangerous tool. In some data sets, the arithmetic mean is closely related to fashion and the median, Y in samples with a large number of outliers or a biased distribution, the average would simply never provide the accuracy that is needed to make a coherent choice.
  2. Standard deviation. this calculation is useful for quickly establishing the distribution of data points. A high standard deviation means that data is distributed more widely from the mean, while a low indicates that there is more data that aligns with the average.. The problem with using these types of data analysis techniques is that, in the same way as with the average, standard deviation can be misleading. As an example, if your data has a very strange pattern, such as a non-normal curve or a large number of outliers, the standard deviation will not illustrate the reality, since you can't provide all the information you need.
  3. Determination of sample size. Sometimes it is not necessary to collect information from each member of a population and a sample is sufficient. When measuring a large dataset or population, this is usually the case. But nevertheless, the key is to determine the correct size so that the sample taken is accurate. Using standard deviation and proportion methods, it is feasible to refine this measure so that data collection is statistically significant. The disadvantage of this technique has to do with the fact that when studying a new variable, it is feasible that the ratio equations should be based on assumptions that may be inaccurate. If so,, this error would eventually be transferred to the determination of the sample size, to end up affecting the result of the analysis.
  4. Regression. Used to set trends over time, since it models the relationships between dependent and explanatory variables, that are generally plotted in a scatter plot, indicating whether the links that exist are strong or weak. In the same way as with the previous data analysis techniques, regression is related to risks. The fact is that, sometimes, outliers on a scatter plot are important, but the analyst will never discover them, since this method tends to ignore them.
  5. Hypothesis testing. This technique allows you to examine whether a premise is truly true for your dataset or population.. Eliminate the opportunity for something to be accidental. Despite this, to be rigorous in its application, hypothesis testing should beware of common mistakes, such as the Hawthorne effect or the placebo effect.

Today, technology at the service of institutions makes it possible apply advanced solutions that automate analysis, leaving behind the manual calculation, thus reducing human intervention and minimizing risk.

5 more sophisticated data analysis techniques

Between the data analysis techniques that best contribute to broadening the business vision, by granting quality knowledge to the business are:

  1. Machine learning. This subspecialty of computer science It is included in the field of artificial intelligence and is also known as machine learning.. It is related to the design and development of algorithms that allow computers to promote actions based on empirical data. Your goal is to learn how to automatically recognize complex patterns and make intelligent, data-driven decisions.. Natural language processing is an example of machine learning.
  2. Neural networks. This type of data analysis techniques consists of computational models, inspired by the structure and functioning of biological neural networks. In the same way that cells and connections would function and be established within the brain, these networks allow us to find patterns in the data. Nonlinear patterns are their specialty and are highly recommended in both applications that involve supervised learning and those that involve unsupervised learning.. An example of this type of technique would be the identification of customers at risk of abandonment..
  3. Association Rules Learning. It is a set of data analysis techniques used to discover interesting relationships between variables in large databases. The generation and testing of possible rules is the result of the application of algorithms and, in practice, one of its most common uses is the analysis of the shopping cart, enabling retailers to determine which products are increasingly being purchased. lower frequency for Optimize your planning and sourcing decisions.
  4. Genetic algorithms. Again we meet a kind of nature-inspired data analysis techniques. In this circumstance, has a Darwinian side, since it is based on natural evolution, the survival of the fittest. When applying this technique, potential solutions are encoded to combine with each other and even undergo mutations, as it could be done in a chromosome lab. Therefore, after being studied, individual chromosomes are selected to survive within a modeled environment that determines the fitness or performance of each compared to the rest of the population. Genetic algorithms are often used for purposes as multiple as make the most of the performance of an investment portfolio or to boost the scheduling of jobs in manufacturing processes.
  5. Time series analysis. In this circumstance, data point sequences are analyzed, representing values in successive times, to extract the most significant features of the information. You could say that is the use of a model to predict future values of a time series based on known past values of the same or another series. The forecast of sales figures would be one of its applications in the company.

What data analysis techniques can add more value to your business? Does your organization have the right level of information quality to ensure the reliability of the results??

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