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Pandas (styled as pandas) is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series .
Tukey defined data analysis in 1961 as: "Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data." [3]
Statistical graphics have been central to the development of science and date to the earliest attempts to analyse data. Many familiar forms, including bivariate plots, statistical maps, bar charts, and coordinate paper were used in the 18th century.
Group model s: some algorithms do not provide a refined model for their results and just provide the grouping information. Graph-based model s : a clique , that is, a subset of nodes in a graph such that every two nodes in the subset are connected by an edge can be considered as a prototypical form of cluster.
A Gantt chart is a type of bar chart [4] [5] that illustrates a project schedule. [6] This chart lists the tasks to be performed on the vertical axis, and time intervals on the horizontal axis. [4] [7] The width of the horizontal bars in the graph shows the duration of each activity.
A pivot table is a table of values which are aggregations of groups of individual values from a more extensive table (such as from a database, spreadsheet, or business intelligence program) within one or more discrete categories. The aggregations or summaries of the groups of the individual terms might include sums, averages, counts, or other ...
A Gantt chart created using Microsoft Project (MSP). Note (1) the critical path is in red, (2) the slack is the black lines connected to non-critical activities, (3) since Saturday and Sunday are not work days and are thus excluded from the schedule, some bars on the Gantt chart are longer if they cut through a weekend.
Applications of AI in cyber security include: Network protection: Machine learning improves intrusion detection systems by broadening the search beyond previously identified threats. Endpoint protection: Attacks such as ransomware can be thwarted by learning typical malware behaviors.