PDF | On Jun 5, , Krishnan Umachandran and others published Data Science in Big Data Analysis. The Data Science and Big Data Analytics course educates students to a foundation level on big data and the state of the practice of analytics. The course . Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and.
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Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that. The course is designed to enable students to: • Become an immediate contributor on a data science team. • Assist reframing a business challenge as an. leaders and executives about Big Data and data science, and is a contributing author and editor of this at EMC related to Data Science and Big Data Analytics .
For example, install. The upper and lower hinges of the boxes correspond to the first and third quartiles of the data. Upper whisker extends from the hinge to the highest value that is within 1. Lower whisker extends from the hinge to the lowest value within 1.
Points outside the whiskers are considered as possible outliers. The correlations can be determined using the cor function. Given the underlying assumptions, the type I error can be defined up front before any data is collected.
For a given deviation from the null hypothesis, the Type 2 error can be obtained by using a large enough sample size. If normality of the download amount distribution is a reasonable assumption, the Student s t test could be used.
Otherwise, a non-parametric test such as the Wilcoxon rank-sum test could be applied.
In this case, the choice is whether a person s height is expressed in centimeters or meters. Let a 1, h 1 denote the observed age years and height centimeters of a particular individual. Thus, height expressed in centimeters will have a greater influence in determining the clusters. Furthermore, students may consider the resulting units of d or d when the units of measure are not removed by dividing through by the standard deviation.
The actual shape may not appear spherical depending on how close the centers are and the observations in the provided dataset.
EMCADSA : Advanced Methods in Data Science and Big Data Analytics
See attachment for example R code. Chapter 5 1 The Apriori property states that if an itemset is considered frequent, then any subset of the frequent itemset must also be frequent.
To meet support criteria of 0. Itemsets A, C, and AC satisfy the minimum support of Interesting rules are identified by their measure of confidence.
When greater than a predefined threshold, known as the minimum confidence, a relationship is considered interesting. Confidence is used to determine which rules are interesting; however, it cannot determine whether or not the rule is by coincidence. Lift is used to determine how X and Y are related, that is whether their relationship is coincidental or not.
So, lift is typically only applied to the rules that meet some minimum confidence level. The only normality assumption applies to the distribution of the error terms.
Depending on the choice of input variables and how well they help to estimate the expected value along the regression line, the normality assumption of the error terms may be justifiable. However, if the normality assumption of the error terms is not justified, transformations of the outcome variable or input variables may be prudent as well as a new linear model parameterization or the introduction of additional input variables.
The reason is that the n-1 binary variables would indicate which of the n-1 values corresponds for a given data record. When the remaining nth value is appropriate, the n-1 binary variables would be set to 0.
Thus, the contribution of the nth value would be imbedded in the intercept term. Thus, the nth value is often called the reference case, since the impact of the other n-1 values in the regression model will adjust the intercept term appropriately. See the U. For any change in the intercept, a corresponding change to the coefficient estimates for the binary terms would occur. Of course, the intercept estimate would also be adjusted to account for the contribution of the new binary input variable for Wyoming.
Of course, as illustrated in the churn example, a threshold other than 0. However, these rates correspond to a threshold value in [0, 1]. By plotting these rates against various threshold values in [0,1], a tradeoff can be made between correctly identifying most positive outcomes vs. Thus, the odds ratio would change by a multiplicative factor of exp The minimum value of 0 is achieved when the probability P x is either 0 or 1. This can be demonstrated by expressing equation in terms of P X and 1-P X and finding the root of its first derivative.
The calculations are based on simply counting the occurrences of events, making the entire classifier efficient to run while handling high-dimensional data. Decision trees are robust to redundant, correlated and non-linear variables and handle categorical variables with multiple levels. Since most of the variables are continuous, a logistic regression model could be built by omitting the correlated variables or transforming the correlated input variables in some way.
The autocorrelation can be considered a normalized covariance where the resulting values will be between -1 and 1.
Since the value of Y depends on X, the random variables X and Y are not independent. Then, i. Determine the density of Y. Determine the joint density of X,Y iii. Find at least one example to illustrate that X and Y are not independent, i.
Determine the density of XY. Compute the covariance of X and Y. Chapter 9 1 The main challenges of text analysis include: high dimensionality due to the number of possible words, the various structures and formats in which the text may be provided, determining the meaning of words, and determining when to treat similar or variations of a word as the same word.
Conversely, precision could be 1. Here are a couple of relevant links: 2 Use cases can be found on multiple internet resources. Here are some aspects that may be compared and contrasted.
As the length of the series increases, the weights on the oldest terms in the series will asymptotically approach zero. First, a small subset of records can be selected to minimize the about of data that must be processed during development and testing.
Of course, this may result in some performance issues during production. Second, a dataset could be randomly split into a training set and a test set. Similarly, some machine learning techniques, such as random forests, require repeated random selection from a dataset to train a model.
Fundamental to this transformation is cloud computing. Through innovative products and services, EMC accelerates the journey to cloud computing, helping IT departments to store, manage, protect and analyze their most valuable asset — information — in a more agile, trusted and cost-efficient way.
Additional information about EMC can be found at www. Clustering Pages: Association Rules Pages: Regression Pages: Classification Pages: Time Series Analysis Pages: Text Analysis Pages: MapReduce and Hadoop Pages: Your password has been changed. Please check your email for instructions on resetting your password.
Data Science vs. Big Data vs. Data Analytics
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Skip to Main Content. EMC Education Services. First published: Print ISBN: About this book Data Science and Big Data Analytics is about harnessing the power of data for new insights.Disclaimer: The Picture given below is not the kind of imagination I am talking about. Veeramuthu, V.
Put Analytic Models To Work
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