By Y. Lakshmi Prasad
Gigantic information Analytics Made effortless is a must-read for everyone because it explains the facility of Analytics in an easy and logical manner besides an finish to finish code in R. whether you're a beginner in gigantic information Analytics, you are going to nonetheless be capable to comprehend the innovations defined during this e-book. while you are already operating in Analytics and working with substantial information, you are going to nonetheless locate this e-book worthy, because it covers exhaustive information Mining thoughts, that are thought of to be complicated themes. It covers desktop studying suggestions and offers in-depth wisdom on unsupervised in addition to supervised studying, that's vitally important for decision-making. the hardest info Analytics suggestions are made less complicated, It positive factors examples from the entire domain names in order that the reader will get hooked up to the ebook simply. This ebook is sort of a own coach to help you grasp the paintings of knowledge technological know-how.
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Additional info for Big Data Analytics Made Easy
Duplicated(Employee$ Work_Balance), ] You can extract unique elements as follow: unique(Cust_Id) It’s also possible to apply unique() on a data frame, for removing duplicated rows as follow: unique(Employee) The function distinct() in dplyr package can be used to keep only unique/distinct rows from a data frame. If there are duplicate rows, only the first row is preserved. It’s an efficient version of the R base function unique(). packages(“dplyr”) library(“dplyr”) Remove duplicate rows based on all columns: distinct(Employee) #Remove duplicate rows based on certain columns (variables): Remove duplicated rows based on JobSatisfaction.
Smaller than 3? Greater than 4 and less than 8? The daily sales of large flat-panel TVs at a store (X): What is the probability of a sale? What is the probability of selling at least three TVs? What is the mean number of Watches sold per day? Variance and Standard Deviation: Both measures of variation or uncertainty in the random variable. Variance (σ2): The weighted average of the squared deviations from the mean, Probabilities serve as weights, Units are square of the units of the variable. On the other hand, the frequency distribution of the lives of fluorescent lights in a factory would be measured on a continuous scale of hours and would not qualify as a binomial distribution.
R returns an error message because the few variables in the dataset are factor variables. To avoid this problem, exclude any non-numeric variables from the dataset by using bracket subset function. If we want to group the values of a numeric variable according to the levels of a factor and calculate a statistic for each group, we can use tapply or aggregate functions. tapply(Health$Age, Health$Gender, mean) We can also use the aggregate function to summarize variables by groups. Using the aggregate function has the advantage that you can summarize several continuous variables simultaneously.
Big Data Analytics Made Easy by Y. Lakshmi Prasad