Courses tagged with "Customer Service Certification Program" (283)
Prepare for the College Mathematics CLEP Exam through Education Portal's brief video lessons on mathematics. This course covers topics ranging from real number systems to probability and statistics. You'll learn to use the midpoint and distance formulas, graph inequalities and multiply binomials. You'll also explore the properties of various shapes and learn to determine their area and perimeter. Our lessons are taught by professional educators with experience in mathematics. In addition to designing the videos in this course, these educators have developed written transcripts and self-assessment quizzes to round out your learning experience.
Prepare for the College Mathematics CLEP Exam through Education Portal's brief video lessons on mathematics. This course covers topics ranging from real number systems to probability and statistics. You'll learn to use the midpoint and distance formulas, graph inequalities and multiply binomials. You'll also explore the properties of various shapes and learn to determine their area and perimeter. Our lessons are taught by professional educators with experience in mathematics. In addition to designing the videos in this course, these educators have developed written transcripts and self-assessment quizzes to round out your learning experience.
Prepare for the College Mathematics CLEP Exam through Education Portal's brief video lessons on mathematics. This course covers topics ranging from real number systems to probability and statistics. You'll learn to use the midpoint and distance formulas, graph inequalities and multiply binomials. You'll also explore the properties of various shapes and learn to determine their area and perimeter. Our lessons are taught by professional educators with experience in mathematics. In addition to designing the videos in this course, these educators have developed written transcripts and self-assessment quizzes to round out your learning experience.
Learn how probability, math, and statistics can be used to help baseball, football and basketball teams improve, player and lineup selection as well as in game strategy.
Methodisch ausgefeilter Doppel-MOOC mit Geometrie und Arithmetik. Du möchtest mathematisch denken lernen? Sei Kiebitz, Anpacker oder Formalisierer: Du hast die Wahl!
This class presents the fundamental probability and statistical concepts used in elementary data analysis. It will be taught at an introductory level for students with junior or senior college-level mathematical training including a working knowledge of calculus. A small amount of linear algebra and programming are useful for the class, but not required.
Learn fundamental concepts in data analysis and statistical inference, focusing on one and two independent samples.
This course provides techniques of effective presentation of mathematical material. Each section of this course is associated with a regular mathematics subject, and uses the material of that subject as a basis for written and oral presentations. The section presented here is on chaotic dynamical systems.
We present a course developed by the team of Tomsk State University of Control Systems and Radioelectronics.
This course offers basic knowledge in mathematical logic.
The goals of mathematical logic are:
- To provide a formal language for mathematical statements that is easily translatable into the natural language and that allows compact and convenient notation.
- To offer clear and unambiguous interpretation of such statements that is at the same time simple and close to the natural mathematical concepts.
We made sure to make this course informative and interesting for everyone!
What will I learn?
Upon completion of the course, students will have acquired fundamental knowledge that is valuable in itself and will serve as the foundation for other studies. For example, software engineers strongly rely on logic-mathematical theories in their work.
• Natural languages possess a number of flaws - inaccuracy, polysemy, complexity.
• Knowledge of the simple yet powerful methods of mathematical statement transformations made possible by the language of logic is just as vital as is the knowledge of elementary algebra. No need to reinvent the wheel.
• Invented almost a century ago to address the needs of mathematics, mathematical logic has found application in theoretical and practical programming.
• When dealing with applied problems, a researcher has to switch between the descriptive language, mathematical language, the language of numerical methods and algorithms, and specific programming languages. The language of mathematical logic offers a great opportunity to practice this translation between languages and is used as a powerful formalised tool for transmission of information between distant languages.
What do I need to know?
Most of the course content will be understandable for students with only a high school level of education. Some minor sections of the course will require knowledge of imperative programming and elements of mathematical analysis.
Course Structure
The course consists of 7 chapters:
Chapter 1 - Mission of mathematical logic:
Goals, objectives, methods.
Relation between mathematics and mathematical logic.
Examples of logical errors, sophisms and paradoxes.
Brief history of mathematical logic, discussing how problems mathematical logic faced and solved in its development, and how mathematical logic integrates further and further into programming.
Chapter 2 - Foundations of the set theory:
Set theory is the basis for development of languages.
Chapter 3 - Propositional logic:
Propositional logic studies the simplest yet the most important formal language.
Chapter 4 - First-order languages:
The language of propositional logic has limited tools, so we talk about more complex languages based on predicate logic. The language of predicate logic offers tools for full and exact description of any formal notions and statements.
Chapter 5 - Axiomatic method:
The axiomatic method makes it possible to solve many logical problems, errors and paradoxes. It is widely used in today's mathematics and the knowledge of it is vital for anyone using functional and logical programming languages.
Chapter 6 - Mathematical proof:
Discussion of the types of mathematical proof and how proof can be aided with a computer.
Chapter 7 - Algorithm theory:
To learn about the possibilities of the algorithmic approach and the limitations of calculations, one must know the rigorous definition of algorithms and computability. The module offers these definitions and defines algorithmically unsolvable problems. The module introduces the concept of algorithm complexity, which is an important factor when selecting algorithms to solve problems. The module also compares problems by complexity - this knowledge makes it possible to use any search algorithm to solve problem instead of search for the good algorithm.
This graduate-level course is a continuation of Mathematical Methods for Engineers I (18.085). Topics include numerical methods; initial-value problems; network flows; and optimization.
Mathematical Methods for Quantitative Finance covers topics from calculus and linear algebra that are fundamental for the study of mathematical finance. Students successfully completing this course will be mathematically well prepared to study quantitative finance at the graduate level.
Find out what solid-state physics has brought to Electromagnetism in the last 20 years. This course surveys the physics and mathematics of nanophotonics—electromagnetic waves in media structured on the scale of the wavelength.
Topics include computational methods combined with high-level algebraic techniques borrowed from solid-state quantum mechanics: linear algebra and eigensystems, group theory, Bloch's theorem and conservation laws, perturbation methods, and coupled-mode theories, to understand surprising optical phenomena from band gaps to slow light to nonlinear filters.
Note: An earlier version of this course was published on OCW as 18.325 Topics in Applied Mathematics: Mathematical Methods in Nanophotonics, Fall 2005.
This graduate level mathematics course covers decision theory, estimation, confidence intervals, and hypothesis testing. The course also introduces students to large sample theory. Other topics covered include asymptotic efficiency of estimates, exponential families, and sequential analysis.
This course provides students with decision theory, estimation, confidence intervals, and hypothesis testing. It introduces large sample theory, asymptotic efficiency of estimates, exponential families, and sequential analysis.
Broadly speaking, Machine Learning refers to the automated identification of patterns in data. As such it has been a fertile ground for new statistical and algorithmic developments. The purpose of this course is to provide a mathematically rigorous introduction to these developments with emphasis on methods and their analysis.
You can read more about Prof. Rigollet's work and courses on his website.
Kursbeschreibung
Mathematik: das ist Freude am Denken! Und mathematisch denken kann jeder! Wer an diesem Kurs teilnimmt, erhält seine regelmäßige Dosis an meditativen Denkaufgaben, spannenden Knobeleien und mathematischen Einsichten. In den Inhaltsgebieten Arithmetik und Geometrie werden mathematische Denk- und Arbeitsweisen vermittelt, beispielsweise Problemlösen, Begriffe definieren und Sätze finden und beweisen.
Was lerne ich in diesem Kurs?
Im ersten Kursblock werden wir uns mit folgenden Fragen befassen: Wie definiert man mathematische Begriffe? Wie findet man eigentlich mathematische Gesetzmäßigkeiten? Und wie beweist man diese? Welche Rolle spielen Annahmen in der Mathematik? Wie baut sich das Gebäude der Mathematik aus Definitionen, Annahmen und Gesetzmäßgikeiten auf? Fragen über Fragen, denen wir uns mit zahlreichen Experimenten widmen.
Im zweiten Kursblock werden wir die Denk- und Arbeitsweisen aus dem ersten Block in verschiedenen Gebieten anwenden und dadurch festigen. In der Geometrie werden wir uns mit der Tätigkeit des Messens und dem Abstandsbegriff, mit Strecken, Halbgeraden und Geraden, mit Ebenen und Halbenenen und mit Winkeln befassen. In der Arithmetik schauen wir uns den Begriff der Teilbarkeit näher an, veranschaulichen Begriffe wie "größter gemeinsamer Teiler" und "kleinstes gemeinsames Vielfaches", untersuchen Primzahlen und Primfaktorzerlegungen und experimentieren mit Stellenwertsystemen.
Im dritten Kursblock befassen wir uns mit grundlegenden mathematischen Konzepten: Was sind Mengen, Relationen und Funktionen? Auch hier werden wir uns den Begriffen und ihren Zusammenhängen mit grundlegenden mathematischen Denk- und Arbeitsweisen nähern. Experimentieren, erforschen, untersuchen, ergründen, Vermutungen anstellen, Vermutungen verwerfen, Vermutungen beweisen.
Im vierten und letzten Kursblock machen wir uns noch einmal an zentrale Gesetzmäßigkeiten der Mathematik. Wie findet man solche Gesetzmäßgikeiten, und wie beweist man sie? In der Geometrie schauen wir uns schicke Sätze am Kreis an, in der Arithmetik nicht weniger schicke Sätze der Zahlentheorie. Mathematik pur, Mathematik anschaulich, Mathematik handgemacht.
Welche Vorkenntnisse benötige ich?
Jede/r kann mitmachen, der mathematische Vorkenntnisse aus dem Gymnasium mitbringt. Und wenn Du nicht auf dem Gymnasium warst, aber gerne mitmachen möchtest: Dann trau dich! Man sollte natürlich schon mal mit Geometrie und Algebra zu tun gehabt haben. Vieles wird dann wieder aufgefrischt, denn wir machen dann nicht auf dem Niveau der 12. oder 13. Klasse weiter, sondern bauen die Teilgebiete, in denen wir arbeiten, noch einmal grundlegend auf. Oberstufenwissen zu Analysis und Linearer Algebra ist nicht notwendig!
Wie hoch ist der Arbeitsaufwand
Du kannst dich entscheiden, wie aktiv Du dich in den Kurs einbringen möchtest - je nach Interesse und Ehrgeiz!
1) Kiebitze wollen "nur mal gucken" oder mit dem mathematischen Denken erst einmal warm werden. Kiebitze schnuppern jede Woche in den Kurs, schauen sich eins, zwei Videos an und stöbern vielleicht einmal in den weiterführenden Bereichen. Hierdurch bekommen sie einen Einblick, was mathematisches Denken bedeutet, und sie erhalten Impulse, wo man Mathematik auch im Alltag findet und gebrauchen kann. Vielleicht bekommen sie dabei sogar Lust auf mehr! Aufwand: ca. 1-2 Stunden pro Woche
2) Anpacker legen Hand an und erforschen aktiv Mathematik, haben aber keine rechte Lust auf zu viele Formeln. Für Anpacker heißt es: Ärmel hochkrempeln! Im MOOC lernen sie, wie man mathematische Situationen systematisch erforscht, wie man anschauliche Begründungen für mathematische Gesetzmäßgikeiten finden kann, und sie erhalten einen Einblick darin, wie man Abstraktes konkretisiert (und umgekehrt). Sie entwickeln ihre Vorstellungskraft zur Lösung mathematischer Probleme weiter und lernen, Vermutungen anhand konkreter Modelle zu untersuchen. Aufwand: ca. 3-4 Stunden pro Woche
3) Formalisierer geben sich mit der Anschauung nicht zufrieden - sie wollen Formeln sehen! Formalisierer sind Anpacker, die zusätzlich auch noch das Spiel mit abstrakter Symbolsprache lieben. Sie lernen, formale Definitionen zu fassen und formale Beweise zu führen. Natürlich immer basierend auf tragfähigen Vorstellungen, die sie mit den Anpackern teilen! Aufwand: ca. 7-8 Stunden pro Woche
Du möchtest ein Kiebitz in der Arithmetik sein, aber ein Anpacker in der Geometrie? Oder ein Formalisierer in der Arithmetik, aber ein Kiebitz in der Geometrie? Kein Problem - alles ist möglich! So kannst Du deinen individuellen Aufwand selbst wählen und dir diejenigen Inhalte zusammenstellen, die dich interessieren.
Erhalte ich ein Zertifikat?
Du erhältst eine Teilnahmebestätigung, wenn du aktiv mitmachst. Wie das genau geht, wird in der ersten Woche erklärt.
This course is the second installment of Single-Variable Calculus. In Part I (MA101 [1]), we studied limits, derivatives, and basic integrals as a means to understand the behavior of functions. While this end goal remains the same, we will now focus on adapting what we have learned to applications. By the end of this course, you should have a solid understanding of functions and how they behave. You should also be able to apply the concepts we have learned in both Parts I and II of Single-Variable Calculus to a variety of situations. We will begin by revisiting and building upon what we know about the integral. We will then explore the mathematical applications of integration before delving into the second major topic of this course: series. The course will conclude with an introduction to differential equations. [1] http:///courses/ma101/…
Multivariable Calculus is an expansion of Single-Variable Calculus in that it extends single variable calculus to higher dimensions. You may find that these courses share many of the same basic concepts, and that Multivariable Calculus will simply extend your knowledge of functions to functions of several variables. The transition from single variable relationships to many variable relationships is not as simple as it may seem; you will find that multi-variable functions, in some cases, will yield counter-intuitive results. The structure of this course very much resembles the structure of Single-Variable Calculus I and II. We will begin by taking a fresh look at limits and continuity. With functions of many variables, you can approach a limit from many different directions. We will then move on to derivatives and the process by which we generalize them to higher dimensions. Finally, we will look at multiple integrals, or integration over regions of space as opposed to intervals. The goal of Mu…
This graduate-level course covers Lebesgue's integration theory with applications to analysis, including an introduction to convolution and the Fourier transform.
This undergraduate course focuses on traditional algebra topics that have found greatest application in science and engineering as well as in mathematics.
Trusted paper writing service WriteMyPaper.Today will write the papers of any difficulty.