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How R relates to data science ? And what are the basics& various components of R programming ??

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'R' is a language which performs system statistical computations and graphics. R language performs computations among other things, high-level graphics, a programming language which can interface to different languages and accommodates debugging facilities.
The language R is a particular version of 'S' programming and was designed in the year 1980. This 'R' programming language is widely spread all across the globe for use in the field of statistical communities.
Interesting point about this language R is that the principle designed for the language R is JOHN M CHAMBERS and he was awarded 1998 ACM software system for 'S'. The syntax of language R is very much similar when compared to the syntax of programming language 'C'.
So, it allows us to perform computations on a programming language which makes the user execute functions that take expressions as input.
Henceforth, it is possible to keep the R language far away and still, we can execute simple expressions from the command line, but here most of the professionals still write a complete programme to execute simple expressions. This is not sensible when few may need not to go beyond that level, but most of them are writing their own functions using ad hoc technique to repeat the work systematically or the different thoughts that incur writing add on packages for performing new functions/operations. R Studio facilitates to perform every action using language 'R'.
So finally, the computations that we execute to perform all objects using R language in R studio and the output of the expression evaluation process is very useful to know when we perform programming R.
The basics and all various components of R together perform all functionalities. The programming language R has its own significance in the field of Data Science.
Well, talking about various components and basics of R in Data Science. R programming has a relation with a tableau where we can connect R programming to perform computation & visualizing the data. importance of the R programming language is reaching the boiling point. Industries and multi-national companies are demanding these skills. These skills are essential, as they are gaining their importance on their own. Even companies are looking for the right professionals who have done thorough research to understand the behavior and patterns of the language. R Programming language can actually demonstrate the process visually & graphically. There are many uncountable packages which we use in the R programming language helps us to meet the needs of the task. Let me take you through an example for an instinct to represent a plot visually we generally use a package 'ggPlot'. And in the same way to forecast the data related to the task we use specific packages to run the program without any break downs.

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SOURCE: what do you mean recurrence relation in computer

Recursion in computer science is a method where the solution to a problem depends on solutions to smaller instances of the same problem. The approach can be applied to many types of problems, and is one of the central ideas of computer science.Most computer programming languages support recursion by allowing a function to call itself within the program text. Some functional programming languages do not define any looping constructs but rely solely on recursion to repeatedly call code. Computability theory has proven that these recursive-only languages are mathematically equivalent to the imperative languages, meaning they can solve the same kinds of problems even without the typical control structures like "while" and "for".In computer science, a list or sequence is an abstract data structure that implements an ordered collection of values, where the same value may occur more than once. An instance of a list is a computer representation of the mathematical concept of a finite sequence, that is, a tuple. Each instance of a value in the list is usually called an item, entry, or element of the list; if the same value occurs multiple times, each occurrence is considered a distinct item.

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What do you mean recurrence relation in computer science? advantage and disadvantages in recurrence relation in computer science? what do you mean sequence? in computer science? what are the importance...


Recursion in computer science is a method where the solution to a problem depends on solutions to smaller instances of the same problem. The approach can be applied to many types of problems, and is one of the central ideas of computer science.Most computer programming languages support recursion by allowing a function to call itself within the program text. Some functional programming languages do not define any looping constructs but rely solely on recursion to repeatedly call code. Computability theory has proven that these recursive-only languages are mathematically equivalent to the imperative languages, meaning they can solve the same kinds of problems even without the typical control structures like "while" and "for".In computer science, a list or sequence is an abstract data structure that implements an ordered collection of values, where the same value may occur more than once. An instance of a list is a computer representation of the mathematical concept of a finite sequence, that is, a tuple. Each instance of a value in the list is usually called an item, entry, or element of the list; if the same value occurs multiple times, each occurrence is considered a distinct item.

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Career in robotics


At my university, they offer a graduate program in robotic engineering. Here are the course requirements:

=================

RBE 1001. Introduction to Robotics (Formerly ES 2201).
Cat. I
Multidisciplinary introduction to robotics, involving concepts from the fields of electrical engineering, mechanical engineering and computer science. Topics
covered include sensor performance and integration, electric and pneumatic actuators, power transmission, materials and static force analysis, controls and programmable embedded computer systems, system integration and robotic applications. Laboratory sessions consist of hands-on exercises and team projects where students design and build mobile robots. Undergraduate credit may not be earned for both this course and for ES 2201.
Recommended background: mechanics (PH 1110/PH 1111).
Suggested background: electricity and magnetism (PH 1120/PH 1121), may be taken concurrently.


=================


RBE 2001. Unified Robotics I.
Cat. I
First of a four-course sequence introducing foundational theory and practice of robotics engineering from the fields of computer science, electrical engineering and mechanical engineering. The focus of this course is the effective conversion of electrical power to mechanical power, and power transmission for purposes of locomotion, and of payload manipulation and delivery. Concepts of energy, power and kinematics will be applied. Concepts from statics such as force, moments and friction will be applied to determine power system requirements and structural requirements. Simple dynamics relating to inertia and the equations of motion of rigid bodies will be considered. Power control and modulation methods will be introduced through software control of existing embedded processors and power electronics. The necessary programming concepts and interaction with simulators and Integrated Development Environments will be introduced. Laboratory sessions consist of hands-on exercises and team projects where students design and build robots and related sub-systems.
Recommended background: ES 2201/RBE 1001, ES 2501 (can be taken concurrently), ECE 2022.


=================


RBE 2002. Unified Robotics II.
Cat. I
Second of a four-course sequence introducing foundational theory and practice of robotics engineering from the fields of computer science, electrical engineering and mechanical engineering. The focus of this course is interaction with the environment through sensors, feedback and decision processes. Concepts of stress and strain as related to sensing of force, and principles of operation and interface methods for electronic transducers of strain, light, proximity and angle will be presented. Basic feedback mechanisms for mechanical systems will be implemented via electronic circuits and software mechanisms. The necessary software concepts will be introduced for modular design and implementation of decision algorithms and finite state machines. Laboratory sessions consist of hands-on exercises and team projects where students design and build robots and related sub-systems.
Recommended background: RBE 2001, CS 1101 or CS 1102


=================


RBE 3001. Unified Robotics III.

Cat. I
Third of a four-course sequence introducing foundational theory and practice of robotics engineering from the fields of computer science, electrical engineering and mechanical engineering. The focus of this course is actuator design, embedded computing and complex response processes. Concepts of dynamic response as relates to vibration and motion planning will be presented. The principles of operation and interface methods various actuators will be discussed, including pneumatic, magnetic, piezoelectric, linear, stepper, etc. Complex feedback mechanisms will be implemented using software executing in an embedded system. The necessary concepts for real-time processor programming, re-entrant code and interrupt signaling will be introduced. Laboratory sessions will culminate in the construction of a multi-module robotic system that exemplifies methods introduced during this course.
Recommended background: RBE 2002, ECE 2801, CS 2223, MA 2051
This course will be offered starting in 2008-09.

=================

RBE 3002. Unified Robotics IV.
Cat. I

Fourth of a four-course sequence introducing foundational theory and practice of robotics engineering from the fields of computer science, electrical engineering and mechanical engineering. The focus of this course is navigation, position estimation and communications. Concepts of dead reckoning, landmark updates, inertial sensors, vision and radio location will be explored. Control systems as applied to navigation will be presented. Communication, remote control and remote sensing for mobile robots and tele-robotic systems will be introduced. Wireless communications including wireless networks and typical local and wide area networking protocols will be discussed. Considerations will be discussed regarding operation in difficult environments such as underwater, aerospace, hazardous, etc. Laboratory sessions will be directed towards the solution of an open-ended problem over the course of the entire term.
Recommended background: RBE 3001.
Suggested background: ES 3011
This course will be offered starting in 2008-09.

=================


RBE/ME 4322. Modeling and Analysis of Mechatronic Systems.
Cat. I
This course introduces students to the modeling and analysis of mechatronic systems. Creation of dynamic models and analysis of model response using the bond graph modeling language are emphasized. Lecture topics include energy storage and dissipation elements, transducers, transformers, formulation of equations for dynamic systems, time response of linear systems, and system control through open and closed feedback loops. Computers are used extensively for system modeling, analysis, and control. Hands-on projects will include the reverse engineering and modeling of various physical systems. Physical models may sometimes also be built and tested.
Recommended background: mathematics (MA 2051, MA 2071), fluids (ES 3004), thermodynamics (ES 3001), mechanics (ES 2501, ES 2503)


=================


RBE/ME 4815. Industrial Robotics.

Cat. I
This course introduces students to robotics within manufacturing systems. Topics include: classification of robots, robot kinematics, motion generation and transmission, end effectors, motion accuracy, sensors, robot control and automation. This course is a combination of lecture, laboratory and project work, and utilizes industrial robots. Through the laboratory work, students will become familiar with robotic programming (using a robotic programming language VAL II) and the robotic teaching mode. The experimental component of the laboratory exercise measures the motion and positioning capabilities of robots as a function of several robotic variables and levels, and it includes the use of experimental design techniques and analysis of variance.
Recommended background: manufacturing (ME 1800), kinematics (ME 3310), control (ES 3011), and computer programming.

=================

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