- function analysis
- Mathematical Optimization
- vectors
- Data Analysis
- Modelling
Differential Calculus through Data and Modeling 특화 과정
Learn differentiable Calculus through modelling. Master differentiation techniques for common single and multivariable functions to apply to optimization problems.
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배울 내용
Model data with both single and multivariable functions
Find maximum and minimum values of both single and multivariable functions, with and without constraints, to find optimal solutions to problems.
Understand properties of different types of functions to apply them accordingly to model different situations.
Perform operations of differential calculus, such as finding velocity, acceleration, rates of change, and slopes of tangent lines.
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이 전문 분야 정보
응용 학습 프로젝트
In each module, learners will be provided with solved sample problems that they can use to build their skills and confidence followed by graded quizzes to demonstrate what they've learned. Through a cumulative project, students will apply their skills to model the cost of a construction project through a real topographical terrain with the goal of finding the optimal cost to complete the project given certain constraints.
Students should have a working knowledge of precalculus before starting this course.
Students should have a working knowledge of precalculus before starting this course.
특화 과정 이용 방법
강좌 수강
Coursera 특화 과정은 한 가지 기술을 완벽하게 습득하는 데 도움이 되는 일련의 강좌입니다. 시작하려면 특화 과정에 직접 등록하거나 강좌를 둘러보고 원하는 강좌를 선택하세요. 특화 과정에 속하는 강좌에 등록하면 해당 특화 과정 전체에 자동으로 등록됩니다. 단 하나의 강좌만 수료할 수도 있으며, 학습을 일시 중지하거나 언제든 구독을 종료할 수 있습니다. 학습자 대시보드를 방문하여 강좌 등록 상태와 진도를 추적해 보세요.
실습 프로젝트
모든 특화 과정에는 실습 프로젝트가 포함되어 있습니다. 특화 과정을 완료하고 수료증을 받으려면 프로젝트를 성공적으로 마쳐야 합니다. 특화 과정에 별도의 실습 프로젝트 강좌가 포함되어 있는 경우, 다른 모든 강좌를 완료해야 프로젝트 강좌를 시작할 수 있습니다.
수료증 취득
모든 강좌를 마치고 실습 프로젝트를 완료하면 취업할 때나 전문가 네트워크에 진입할 때 제시할 수 있는 수료증을 취득할 수 있습니다.

이 전문 분야에는 4개의 강좌가 있습니다.
Calculus through Data & Modeling: Precalculus Review
This course is an applications-oriented, investigative approach to the study of the mathematical topics needed for further coursework in single and multivariable calculus. The unifying theme is the study of functions, including polynomial, rational, exponential, logarithmic, and trigonometric functions. An emphasis is placed on using these functions to model and analyze data. Graphing calculators and/or the computer will be used as an integral part of the course.
Calculus through Data & Modeling: Limits & Derivatives
This first course on concepts of single variable calculus will introduce the notions of limits of a function to define the derivative of a function. In mathematics, the derivative measures the sensitivity to change of the function. For example, the derivative of the position of a moving object with respect to time is the object's velocity: this measures how quickly the position of the object changes when time advances. This fundamental notion will be applied through the modelling and analysis of data.
Calculus through Data & Modeling: Differentiation Rules
Calculus through Data & Modeling: Differentiation Rules continues the study of differentiable calculus by developing new rules for finding derivatives without having to use the limit definition directly. These differentiation rules will enable the calculation of rates of change with relative ease the derivatives of polynomials, rational functions, algebraic functions, exponential and logarithmic functions, and trigonometric and inverse trigonometric functions. Once these rules are developed, they are then applied to solve problems involving rates of change and the approximation of functions.
Calculus through Data & Modeling: Applying Differentiation
As rates of change, derivatives give us information about the shape of a graph. In this course, we will apply the derivative to find linear approximations for single-variable and multi-variable functions. This gives us a straightforward way to estimate functions that may be complicated or difficult to evaluate. We will also use the derivative to locate the maximum and minimum values of a function. These optimization techniques are important for all fields, including the natural sciences and data analysis. The topics in this course lend themselves to many real-world applications, such as machine learning, minimizing costs or maximizing profits.
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The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
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전문 분야를 완료하는 데 얼마나 걸리나요?
What background knowledge is necessary?
Do I need to take the courses in a specific order?
전문 분야를 완료하면 대학 학점을 받을 수 있나요?
What will I be able to do upon completing the Specialization?
궁금한 점이 더 있으신가요? 학습자 도움말 센터를 방문해 보세요.