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Data analytics for engineers- introduction
- 2. DATA • Data is a collection of values that convey information. It can describe quantity, quality, facts, statistics, or other basic units of meaning. • Data can be collected and used to help decision-making. It can also be information in an electronic form that can be stored and used by a computer.
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- 10. Analytic scalability • Analytic scalability is the ability to use data to understand and solve a large variety of problems. And because problems come in many forms, analytics must be flexible enough to address problems in different ways. This might include the use of statistical tools and forecasting. • In analytic scalability, we have to pull the data together in a separate analytics environment and then start performing analysis
- 11. Traditional Analytic Architecture • Traditional analytics collects data from heterogeneous data sources and we had to pull all data together into a separate analytics environment to do analysis which can be an analytical server or a personal computer with more computing capability. In such environments, shipping of data becomes a must, which might result in issues related with security of data and its confidentiality.
- 12. Modern In-Database Architecture • Data from heterogeneous sources are collected, transformed and loaded into data warehouse for final analysis by decision makers. • The processing stays in the database where the data has been consolidated. • The data is presented in aggregated form for querying. • Queries from users are submitted to OLAP (online analytical processing) engines for execution. Such in-database architectures are tested for their query throughput rather than transaction throughput as in traditional database environments..
- 14. Grid Computing • Grid computing is a form of distributed computing whereby a "super and virtual computer" is composed of a cluster of networked, loosely coupled computers, acting in concert to perform very large tasks. • Grid computing (Foster and Kesselman, 1999) is a growing technology that facilitates the executions of large-scale resource intensive applications on geographically distributed computing resources. • Facilitates flexible, secure, coordinated large scale resource sharing among dynamic collections of individuals, institutions, and resource
- 15. Parallel Computing and Distributed Computing • Parallel Computing: In parallel computing multiple processors performs multiple tasks assigned to them simultaneously. Memory in parallel systems can either be shared or distributed. Parallel computing provides concurrency and saves time and money. • Distributed Computing: In distributed computing we have multiple autonomous computers which seems to the user as single system. In distributed systems there is no shared memory and computers communicate with each other through message passing. In distributed computing a single task is divided among different computers.
- 16. Parallel Computing VS Distributed Computing Parallel Computing Distributed Computing Many operations are performed simultaneously System components are located at different locations Single computer is required Uses multiple computers Multiple processors perform multiple operations Multiple computers perform multiple operations It may have shared or distributed memory It have only distributed memory Processors communicate with each other through bus Computer communicate with each other through message passing.
- 17. Massively Parallel Processing (MPP) • Massive Parallel Processing (MPP) is the ―shared nothing‖ approach of parallel computing. It is a type of computing wherein the process is being done by many CPUs working in parallel to execute a single program. • One of the most significant differences between a Symmetric Multi- Processing or SMP and Massive Parallel Processing is that with MPP, each of the many CPUs has its own memory to assist it in preventing a possible hold up that the user may experience with using SMP when all of the CPUs attempt to access the memory simultaneously.
- 18. Massively Parallel Processing (MPP)
- 19. Execution of tasks in MPP
- 20. The Cloud Computing • Cloud computing is the delivery of computing services over the Internet. Cloud services allow individuals and businesses to use software and hardware that are managed by third parties at remote locations. • Examples of cloud services include online file storage, social networking sites, webmail, and online business applications. The cloud computing model allows access to information and computer resources from anywhere that a network connection is available. • Cloud computing provides a shared pool of resources, including data storage space, networks, computer processing power, and specialized corporate and user applications.
- 21. Characteristic features of cloud 1) Mask the underlying infrastructure from the user 2) Be elastic to scale on demand 3) On a pay-per-use basis 4) National Institute of Standards and Technology (NIST) 5) On-demand self-service 6) Broad network access 7) Resource pooling 8) Rapid elasticity 9) Measured service
- 22. Two types of cloud environment 1. Public Cloud – The services and infrastructure are provided off-site over the internet – Greatest level of efficiency in shared resources – Less secured and more vulnerable than private clouds 2. Private Cloud – Infrastructure operated solely for a single organization – The same features of a public cloud – Offer the greatest level of security and control – Necessary to purchase and own the entire cloud infrastructure
- 25. Sampling Fundamentals • Sampling may be defined as the selection of some part of an aggregate or totality on the basis of which a judgment or inference about the aggregate or totality is made. • It is the process of obtaining information about an entire population by examining only a part of it. • In most of the research work and surveys, the usual approach happens to be to make generalisations.
- 26. NEED FOR SAMPLING 1. Sampling can save time and money. A sample study is usually less expensive than a census study and produces results at a relatively faster speed. 2. Sampling may enable more accurate measurements for a sample study is generally conducted by trained and experienced investigators. 3. Sampling remains the only way when population contains infinitely many members. 4. Sampling remains the only choice when a test involves the destruction of the item under study. 5. Sampling usually enables to estimate the sampling errors and, thus, assists in obtaining information concerning some characteristic
- 27. SOME FUNDAMENTAL DEFINITIONS • Universe/Population: • From a statistical point of view, the term ‘Universe’ refers to the total of the items or units in any field of inquiry, whereas the term ‘population’ refers to the total of items about which information is desired. The attributes that are the object of study are referred to as characteristics and the units possessing them are called as elementary units. • The population or universe can be finite or infinite. The population is said to be finite if it consists of a fixed number of elements so that it is possible to enumerate it in its totality. For instance, the population of a city, the number of workers in a factory are examples of finite populations. • An infinite population is that population in which it is theoretically impossible to observe all the elements. Thus, in an infinite population the number of items is infinite i.e., we cannot have any idea about the total number of items. The number of stars in a sky, possible rolls of a pair of dice are examples of infinite population.
- 28. Sampling frame • The elementary units or the group or cluster of such units may form the basis of sampling process in which case they are called as sampling units. A list containing all such sampling units is known as sampling frame. Thus sampling frame consists of a list of items from which the sample is to be drawn.
- 29. Sampling design: • A sample design is a definite plan for obtaining a sample from the sampling frame. It refers to the technique or the procedure the researcher would adopt in selecting some sampling units from which inferences about the population is drawn. • Sampling design is determined before any data are collected
- 30. Statistics(s) and parameter(s) • A statistic is a characteristic of a sample, whereas a parameter is a characteristic of a population. Thus, when we work out certain measures such as mean, median, mode or the like ones from samples, then they are called statistic(s) for they describe the characteristics of a sample. • But when such measures describe the characteristics of a population, they are known as parameter(s). • For instance, the population mean is a parameter, whereas the sample mean ( X ) is a statistic. To obtain the estimate of a parameter from a statistic constitutes the prime objective of sampling analysis.
- 31. Sampling error • Sample surveys do imply the study of a small portion of the population and as such there would naturally be a certain amount of inaccuracy in the information collected. This inaccuracy may be termed as sampling error or error variance. • In other words, sampling errors are those errors which arise on account of sampling and they generally happen to be random variations (in case of random sampling) in the sample estimates around the true population values
- 32. Mean, Median, Mode, Standard deviation • The mean, median and mode are all estimates of where the "middle" of a set of data is. • These values are useful when creating groups or bins to organize larger sets of data. • The standard deviation is the average distance between the actual data and the mean.
- 33. Mean • The mean (also know as average), is obtained by dividing the sum of observed values by the number of observations, “n”. Although data points fall above, below, or on the mean, it can be considered a good estimate for predicting subsequent data points
- 34. Median • The median is the middle value of a set of data containing an odd number of values, or the average of the two middle values of a set of data with an even number of values. The median is especially helpful when separating data into two equal sized bins.
- 35. Mode • The mode of a set of data is the value which occurs most frequently • Standard Deviation • The standard deviation gives an idea of how close the entire set of data is to the average value. Data sets with a small standard deviation have tightly grouped, precise data. Data sets with large standard deviations have data spread out over a wide range of values