# Computer Science Courses Online

Live Instructor Led Online Training Computer Science courses is delivered using an interactive remote desktop! .

During the course each participant will be able to perform Computer Science exercises on their remote desktop provided by Qwikcourse.

#### How do I start learning Computer Science?

Select among the courses listed in the category that really interests you.

If you are interested in learning the course under this category, click the "Book" button and purchase the course. Select your preferred schedule at least 5 days ahead. You will receive an email confirmation and we will communicate with trainer of your selected course.

## Basic Computing Using Windows

A basic guide to using computers that run Windows®. Download the entire book as a PDF File. This can be done in two ways: you can right-click on the link "PDF version" and choose "Save target as" to save the PDF on your computer for viewing at any time or left-click on the link to view it without saving.

Contents

• Computers and Peripherals
• Operating Systems and Controls
• The Desktop
• File Systems
• Concepts and Settings
• Networks and the Internet
• FTP
• Email, Chat-rooms, and IM
• Appendices
• Switching the Control Panel to Classic View
• Connecting to the Internet
• Dual Booting

7 hours

## Work around Data Structures

This course is about the creation and analysis of efficient data structures. It covers: To understand the material in This course you should be comfortable enough in a programming language to be capable of working with and writing your own variables, arithmetic expressions, if-else conditions, loops, subroutines (also known as functions), pointers (also known as references or object handles), structures (also known as records or classes), simple input and output, and simple recursion. Because many different languages approach the construction of data structures differently, we use pseudo-code so that you can translate the code into your own language. A Wikibook is an undertaking similar to an open-source software project: A contributor creates content for the project to help others, for personal enrichment, or to accomplish something for the contributor's own work (e.g., lecture preparation). An open book, just like an open program, requires time to complete, but it can benefit greatly from even modest contributions from readers. For example you can fix "bugs" in the text (where the bug might be typographic, expository, technical, aesthetic or otherwise) in order to make a better book. If you find an opportunity to fix a bug, simply click on "edit", make your changes, and click on save. Other contributors may review your changes to be sure they are appropriate for the course. If you are unsure, you can visit the discussion page and ask there. Use common sense.

7 hours

## Embedded Systems

This course is about microcontrollers, in the field of digital control systems. We will discuss embedded systems, real-time operating systems, and other topics of interest. It is important to realize that embedded systems rarely have display capabilities, and if they do have displays, they are usually limited to small text-only LCD displays. The challenge of programming an embedded system then is that it is difficult to get real-time feedback from the system without a display. It is common to use a simple serial interface for diagnostic purposes, for example by connecting to a PC running terminal software via a RS-232 to USB adapter. Also, embedded systems usually have very strict memory limitations, processor limitations, and speed limitations that must play a factor in designing an embedded system, and programming an embedded computer. This course talks about some of the specific issues involved in programming an embedded computer. It also covers some basic topics such as microprocessor architectures, FPGAs, and some general low-level computing topics. While many of the issues discussed in This course may apply to PCs, and non-embedded computers, This course remains focused on topics that apply to embedded systems only. This course has incorporated a number of smaller books, stub-books, and half-books that were previously written about this subject.

7 hours

## Discover Chip Design Made Easy

In This course Chip Design we tell how to build an integrated circuit ("chip") by integrating billions of transistors to achieve an application. An application could be suiting a particular requirement like microprocessor, router, cell phone,etc. An integrated circuit designed for a specific application is called as ASIC (Application Specific Integrated Circuits). Today's ASIC Chips are pretty complex, packed with larger chunk of transistors targeted to a specific manufacturing process for fabricating the integrated circuits, in a sub nanometer regime, involving many challenges like knowledge of various protocols, architectures, models, formats, standards, knowledge about CMOS logic, Digital Design concepts, taming the EDA tool for the various design requirements like area, timing, power, thermal, noise, routability, lithography aware, knowledge about various variabilities like channel length, Vt, line width variations, lens aberrations, IR drop effects, inter-die and intra die-variations, effects, and various noise-effects like package noise, EMI noise, power grid noise, cross-talk noise, and ability to test and validate and know to model and characterize all these effects upfront in the design-phase, steps to increase profitability curve, with short span of time-to market to minimize the risk and maximize the predictability and an modular approach to success. Now let's delve in to the "Art of Chip Designing". That is a lot of technical jargon, but there is nothing to worry about. You will soon learn what that means, and understand the concepts behind chip designing. Why an Analogy with Building Architecture? To understand the concepts of Chip designing in a better way, as we are very familiar with Building Architecture, then it will be easy for us to map Chip Design architecture.

7 hours

## Work around Expert Systems

This course is about expert systems, their use, and their construction. Expert systems are AI computer programs that use the knowledge and processes of a human expert to solve problems that computers have been incapable of solving efficiently. This course is designed for students at the undergraduate level in the fields of computer science or computer engineering. Students are expected to have a background in high-level programming languages, although no single language is preferred.

7 hours

## Explore Floating Point

This course discusses the IEEE 754 standard concerning floating-point numbers. Beginning chapters of This course focus on newcomers to the standard, who wish to understand and make use of floating point numbers, especially in a programming project. Later chapters of This course, however, focus more on the details of implementation of the IEEE 754 standard. This way, advanced users who want more details, or users who are working to create a floating-point implementation of their own can find the information they need. This course can be used as an ancillary reference source to support other books in the computer science and engineering fields. Programming examples found in This course attempt to use pseudocode where possible, to prevent an over-reliance on any particular language or platform. Specific examples may utilize a single computer language or assembly language. Prerequisites to This course include an understanding of exponents and Algebra, and an understanding of binary number representation.

7 hours

## Know Visualizing Computation

Diagrams play a central role in communication in many fields, and computer science is no exception—our classroom lectures, research talks, textbooks, and Wikipedia pages are full of visual material to complements to our words. The forms of these diagrams are typically defined vaguely if at all. While this may be appropriate in a lecture setting, uncertainty about the diagram can lead to confusion as students study their notes or books. Visualizing Computation is an archive of definitions of useful visualization techniques that are often used in lectures without extensive discussion. Each page of This course covers a particular approach to visualizing some aspect of the execution of software. Each page should serve as a stand-alone definition of the diagram style, but not a complete description of the system being visualized. For example, Visualizing Computation describes diagrams that can be used to illustrate stack frame layout or evolving stack frame layouts, but leaves the definition and use of stack frames for the Wikipedia call stack entry or professor's lectures. The visualizations themselves may be static illustrations of static entities, techniques for capturing evolution-over-time in a static image, or (in principle) animations. This course is designed to collect topics too specialized even for Wikipedia's notability policy ("Wikipedia is not a directory of everything in existence"). In particular, descriptions of interesting local variations in drawing style are encouraged, not discouraged. This project itself stems from repeated observations by Wonnacott's students at Haverford College, who pointed out the difficulties of taking good notes when the professor keeps erasing and overwriting the same diagram.

7 hours

## Discover Geographic Information Systems

Geographic Information Systems provide a method for integrating and analyzing spatial (digital map based) information such as "where is the nearest movie theater?" alongside related non-spatial information (what movies are playing there?). GIS have three major capabilities (computer mapping, spatial analysis and spatial database) and can operate on a range of platforms (desktop/laptop computer, Internet, PDA, etc.). Many people are becoming far more familiar with seeing the results both textually - for example when their phone shows them the nearest pub - and on open map systems such as Google Maps. Where in the past people had to literally use pencils and string on a paper map to find their nearest school, a computer can do this now extremely quickly an accurately, as long as all the information has been entered correctly in the first place. In a broader context, GIS involves people and often brings a philosophy of change. For example, in 1994, the New York Police Department introduced GIS to locate crime 'hot-spots', analyze underlying problems and devise strategies and solutions to deal with the problems. Since 1993, violent crime has dropped by two-thirds in New York City.[1] This strategy, known as COMPSTAT, has expanded to cities and jurisdictions across the United States and around the world. One leading GIS software vendor is ESRI, based in Redlands, California, which offers ArcGIS for the desktop, ArcGIS Server for Internet mapping, ArcPad for PDAs and a range of other products and services for developers. Other popular GIS software packages are available from Cadcorp, Intergraph, MapInfo, Manifold and Autodesk. ERDAS Imagine, ENVI, Idrisi, and PCI Geomatica are geared towards remote sensing i.e. analysis of satellite/aircraft images. There are many third-party extensions and utilities for ArcGIS and other GIS and raster software platforms. Currently, open source GIS software options can be chosen from the first OS GIS package GRASS, recent open source options are DIVA GIS, Quantum GIS, and uDig. There are efforts underway, through the Open GIS Consortium to provide interoperability among spatial data formats and software. The leading contender for spatial data storage is another open source package called PostGIS, which is a spatial extension to the open source database PostgreSQL.

7 hours

## Learn Windows: An Overview

At the time of this era, the Microsoft Windows family of operating systems runs the vast majority of the world's home computers. How did Windows rapidly become the dominant operating system for home use on the planet? Microsoft Windows began as a GUI add-on to DOS. The early versions of Windows required DOS to be installed first. The first version that did not require DOS to be pre-installed was Windows 95. Early on, Windows split into two branches - the DOS-based branch and the NT based branch. Today, The DOS-based branch has been discontinued due to bugs (errors in software), Lack of hardware support, and instability. All versions of Windows since Windows NT 3.1 (these are Windows NT 3.1, NT 4.0, Windows 2000, XP, Vista, 7, and 8) are NT based. Here are the predecessors to Microsoft Windows 95 in the order of release: Windows 1.0 Windows 2.0

7 hours

## Discover Conversational bots

Conversational bots are programs that carry out a conversation with users. This tutorial will guide you through the basics of creating a simple conversational bot that analyses natural language input. In what follows we will use the Go programming language in code examples. To test out your code, you can use this free iOS app in conjunction with a server on which your code is running.

7 hours

## Explore Support Vector Machines

Support vector machines (SVMs) are a set of related supervised learning methods that analyze data and recognize patterns, used for classification and regression analysis. The original SVM algorithm was invented by Vladimir Vapnik and the current standard incarnation (soft margin) was proposed by Corinna Cortes and Vladimir Vapnik [1]. The standard SVM is a non-probabilistic binary linear classifier, i.e. it predicts, for each given input, which of two possible classes the input is a member of. Since an SVM is a classifier, then given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other. Intuitively, an SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall on. More formally, a support vector machine constructs a hyperplane or set of hyperplanes in a high or infinite dimensional space, which can be used for classification, regression or other tasks. Intuitively, a good separation is achieved by the hyperplane that has the largest distance to the nearest training data points of any class (so-called functional margin), since in general the larger the margin the lower the generalization error of the classifier. Whereas the original problem may be stated in a finite dimensional space, it often happens that in that space the sets to be discriminated are not linearly separable. For this reason it was proposed that the original finite dimensional space be mapped into a much higher dimensional space presumably making the separation easier in that space. SVM schemes use a mapping into a larger space so that cross products may be computed easily in terms of the variables in the original space making the computational load reasonable. The cross products in the larger space are defined in terms of a kernel function K ( x , y ) {\displaystyle K(x,y)} which can be selected to suit the problem. The hyperplanes in the large space are defined as the set of points whose cross product with a vector in that space is constant. The vectors defining the hyperplanes can be chosen to be linear combinations with parameters α i {\displaystyle \alpha _{i}} of images of feature vectors which occur in the data base. With this choice of a hyperplane the points x in the feature space which are mapped into the hyperplane are defined by the relation: ∑ i α i K ( x i , x ) = c o n s t a n t {\displaystyle \sum _{i}{\alpha _{i}K(x_{i},x)}=constant} Note that if K ( x , y ) {\displaystyle K(x,y)} becomes small as y {\displaystyle y} grows further from x {\displaystyle x} , each element in the sum measures the degree of closeness of the test point x {\displaystyle x} to the corresponding data base point x i {\displaystyle x_{i}} . In this way the sum of kernels above can be used to measure the relative nearness of each test point to the data points originating in one or the other of the sets to be discriminated. The set of points x {\displaystyle x} mapped into any hyperplane can be quite convoluted as a result allowing much more complex discrimination between sets which are far from convex in the original space.

7 hours

#### Is learning Computer Science hard?

In the field of Computer Science learning from a live instructor-led and hand-on training courses would make a big difference as compared with watching a video learning materials. Participants must maintain focus and interact with the trainer for questions and concerns. In Qwikcourse, trainers and participants uses DaDesktop , a cloud desktop environment designed for instructors and students who wish to carry out interactive, hands-on training from distant physical locations.

#### Is Computer Science a good field?

For now, there are tremendous work opportunities for various IT fields. Most of the courses in Computer Science is a great source of IT learning with hands-on training and experience which could be a great contribution to your portfolio.

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