Data analysis is important for organisations. Not because it is the technology of the 21st century but because data analytics helps in making informed business decisions. The technologies and techniques provide a means of analysing the data sets and extract conclusions from them. Big data analytics involves complex applications. It uses tools as predictive models, statistical algorithms and what-if analysis. Don’t worry, I will tell you what these all are.
To make sure that the data that is analysed in the processes further down the line, we make use of several interpretation techniques. These interpretation techniques are –
In a perfect world, the data is also perfect without any errors and flaws. But in the real world in which we are living, the data has errors and flaws. Thus, data assessment is an effort to make sure that the data is complete, accurate, pertinent, and unambiguous. The data should feel like Tom Cruise is feeling.
Nobody wants to see a dirty data. Ahem, not that kind of dirty. Data cleansing is a needed process which is used to make sure that all the records in the data are correct. The techniques are used to detect and correct inaccurate records and remove the corrupted ones. I wish you also enjoyed data cleansing like Mrs Doubtfire enjoys sweeping.
Your data might not be regular, agreed? So, what do you do to make sure that due to some missing information, your analysis is not hampered? You cannot always track back the source to collect the missing information. You cannot also always get all the information in your dataset. But you need to do something because the analysis is not going to be accurate unless you have it ready. That is when imputation steps in. Imputation is when you replace the missing data with some substitute values. When you are substituting a data point, it is called a unit imputation. When you are substituting the component of the data point, it is called item imputation. Not all substitutes are this cool, by the way. I cannot only tell you this much now. Want to know more? I am only one call away *singing in Charlie Puth’s voice* and you can get in touch with me for a personalised mentoring session here.
A descriptive analysis works on the graphical representation of the data. When we have a large data set and a huge number of samples that cannot be analysed individually, we take help of descriptive analysis. The descriptive analysis methods are useful in arranging the large datasets from their graphical representation. These include –
Frequency is the measurement of how fast an event occurs. When the data sets have to show how frequently something occurs or how often the response is given, a frequency analysis is used. The analysis is used to get the count, per cent, and frequency.
Another analysis type is when the data sets are analysed on the basis of how distributed it is with respect to a reference point. The most common use of such analysis is when the user has to give the most common responses or the average of the data. The analysis is done using mean, median, and mode.
The data sets show a lot of relations when they are arranged on the basis of their deviation from the standard data. The analysis is done to see how spread out the data is. When the data is too spread out, it affects the mean of the data. Hence, this analysis is useful. The analysis is done by calculating the range, variance, and standard deviation.
The analysis on the basis of position tells us how the various scores (overserved data) is related to one another. The analysis is made by comparing it with the standardised scores. This position based analysis is done using percentile ranks. That’s also one way of analysis but I am not sure if your professor would agree.
Advanced statistical data analysis is used to take out meaningful insights from the data that go unnoticed while its collection. The statistical analysis tools are used to analyse the results. These advanced statistical analysis methods include –
A statistical hypothesis is a hypothesis that can be tested by observing a process which is changing by a set of random variables. Statistical hypothesis testing is a method to identify the properties of a probability distribution. To test this, two statistical data sets are compared with each other. Or you can get one dataset through sampling and compare it with the synthetic data set from an idealised model. A hypothesis is proposed so that we can identify the relationship between two data sets. This is done to void the default null hypothesis which says that there is no relationship between the given data sets. This is how a statistical hypothesis testing acts like in front of a null hypothesis.
The probability density function is unobservable. So, how do we measure it? You guessed it right, through density estimation. The title is obvious, isn’t it? When we construct an estimate of the observed data to measure the unobservable probability density function, it is called density estimation. Why is it called density? Well, to measure it, the density function is thought of as the density according to which the large population is distributed. The data is considered as a random sample from that population. Sounds too typical, is it? It is useful in conditions where you have to analyse a really large population (or data set).
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Warren Armstrong is a professional statistician who has worked with the Australian Bureau of Statistics for a long time, then became a private tutor before he was a statistics assignment writing expert at My Assignment Services. He has published several works in the field of statistics before shifting to assignment guidance. He offers help and consultation to the students for writing their assignment in the form of tips, explanation of complex topics, resolving queries and providing live sessions. Warren has helped a number of students with their assignments.
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