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Statistics and Calculus Methods for Data Analysis

Курс от University of Pittsburgh
Начальный≈ 16.3 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This program focuses on the practical application of essential mathematical, statistical, and analytical techniques vital for advanced data science studies. Learn to calculate expected values, understand the normal distribution, perform derivative calculations, and solve complex integrals, all demonstrated with Python. Start with the concept of expected values and explore their relationship to the normal distribution, laying the groundwork for statistical analysis and predictive modeling. Move on to calculus, mastering derivatives and their applications in tasks like optimization and rate of change analysis. Advance further into solving integrals, including techniques for handling complex integrations and their significance in continuous data analysis. By the end of the course, you will possess a strong mathematical foundation to tackle more advanced data science topics. Engage in practical assignments and real-world projects to apply these methods in solving complex data problems. By leveraging tools like Python, you will gain hands-on understanding of these critical concepts.

Навыки, которые вы освоите

DerivativesCalculusProbabilityProbability DistributionIntegral CalculusPredictive ModelingMathematical ModelingMathematics and Mathematical ModelingMachine LearningAlgorithmsStatistical ModelingData AnalysisData ScienceStatistical AnalysisStatistical MethodsProbability & StatisticsStatisticsApplied Mathematics

Программа курса

3 модулей · 35 учебных материалов

01Expected Values and the Normal Distribution11 материалов

Module 1 Lecture Videos

Welcome to Statistics and Calculus Methods for Data AnalysisВидеоJupyter Notebook SlidesЧтениеLecture 1: Expected ValuesВидеоLecture 2: Samples of Dice RollsВидео

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Morgan Frank

Преподаватель курса

Statistics and Calculus Methods for Data Analysis
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Обучение на Coursera

≈ 16.3 ч

3 модулей

Язык: Английский

Субтитры: Арабский, Французский, Итальянский, Бразильский португальский, Корейский, Немецкий, Испанский, Японский, Казахский, Венгерский

Часть программы вашего университета
Lecture 3: Populations vs. Samples of Heights DataВидео
Lecture 4: Populations vs. Samples of Wage DataВидео
Lecture 5: Central Limit Theorem and Normal DistributionВидео

Module 1 Assessments: Normal Distribution

Lab Homework: Normal Distribution ПрограммированиеLet's Practice: Expected Values and the Normal DistributionЗаданиеTest Yourself: Expected Values and the Normal DistributionЗаданиеStatistical Foundations CheckDIALOGUE
02Calculus I - Derivatives14 материалов

Module 2 Lecture Videos

Jupyter Notebook SlidesЧтениеLecture 1: Calculus–Core ConceptsВидеоLecture 2: Approximating DerivativesВидеоLecture 3: Calculating Exact Instantaneous DerivativesВидеоLecture 4: Derivatives for Simple PolynomialsВидеоLecture 5: Derivatives–Additivity, Mult. by Constants, and the Power RuleВидеоLecture 6: Derivative Chain RuleВидеоLecture 7: Derivative Products and QuotientsВидеоLecture 8: Symbolically Solving Higher Order Derivatives & Partial DerivativesВидеоLecture 9: Example–Population Growth (Logistic Curve)ВидеоLecture 10: Derivatives and Stationary PointsВидео

Module 2 Assessments: Derivatives

Lab Homework: DerivativesПрограммированиеLet's Practice: Calculus I - DerivativesЗаданиеTest Yourself: Calculus I - DerivativesЗадание
03Calculus II - Integrals10 материалов

Module 3 Lecture Videos

Jupyter Notebook SlidesЧтениеLecture 1: Intro to IntegralsВидеоLecture 2: Riemann Summations–Approximating the Area Under the CurveВидеоLecture 3: Calculus Theorem–Relating Integrals to DerivativesВидеоLecture 4: Techniques for Solving Complex IntegralsВидеоLecture 5: Multiple & Partial Integrals and Programming IntegralsВидеоLecture 6: Numerical Integration, Chaos, and the Butterfly EffectВидео

Module 3 Assessments: Integrals

Lab Homework: IntegralsПрограммированиеLet's Practice: Calculus II - IntegralsЗаданиеTest Yourself: Calculus II - IntegralsЗадание