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Statistics and Calculus Methods for Data Analysis

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Statistics and Calculus Methods for Data Analysis

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Gain insight into a topic and learn the fundamentals.
Beginner level
No prior experience required
2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Build toward a degree
Gain insight into a topic and learn the fundamentals.
Beginner level
No prior experience required
2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Build toward a degree

What you'll learn

  • Calculate expected values and apply normal distribution for statistical analysis.

  • Perform derivative calculations for optimization and rate of change analysis.

  • Solve complex integrals using Python for continuous data analysis.

  • Apply statistical and calculus methods in Python for predictive modeling.

Details to know

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Assessments

6 assignments

Taught in English

Build your subject-matter expertise

This course is part of the Mathematical Foundations for Data Science and Analytics Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
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There are 3 modules in this course

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.

This module introduces the probabilistic concept of expected value and their relationship to the Normal Distribution from probability theory.

What's included

6 videos1 reading2 assignments1 programming assignment

6 videos•Total 72 minutes
  • Welcome to Statistics and Calculus Methods for Data Analysis•3 minutes
  • Lecture 1: Expected Values•7 minutes
  • Lecture 2: Samples of Dice Rolls•15 minutes
  • Lecture 3: Populations vs. Samples of Heights Data•8 minutes
  • Lecture 4: Populations vs. Samples of Wage Data•12 minutes
  • Lecture 5: Central Limit Theorem and Normal Distribution•27 minutes
1 reading•Total 10 minutes
  • Jupyter Notebook Slides•10 minutes
2 assignments•Total 45 minutes
  • Let's Practice: Expected Values and the Normal Distribution•15 minutes
  • Test Yourself: Expected Values and the Normal Distribution•30 minutes
1 programming assignment•Total 180 minutes
  • Lab Homework: Normal Distribution •180 minutes

This module introduces the derivative concept from calculus.

What's included

10 videos1 reading2 assignments1 programming assignment

10 videos•Total 99 minutes
  • Lecture 1: Calculus–Core Concepts•11 minutes
  • Lecture 2: Approximating Derivatives•13 minutes
  • Lecture 3: Calculating Exact Instantaneous Derivatives•6 minutes
  • Lecture 4: Derivatives for Simple Polynomials•11 minutes
  • Lecture 5: Derivatives–Additivity, Mult. by Constants, and the Power Rule•10 minutes
  • Lecture 6: Derivative Chain Rule•11 minutes
  • Lecture 7: Derivative Products and Quotients•7 minutes
  • Lecture 8: Symbolically Solving Higher Order Derivatives & Partial Derivatives•12 minutes
  • Lecture 9: Example–Population Growth (Logistic Curve)•7 minutes
  • Lecture 10: Derivatives and Stationary Points•10 minutes
1 reading•Total 10 minutes
  • Jupyter Notebook Slides•10 minutes
2 assignments•Total 45 minutes
  • Let's Practice: Calculus I - Derivatives•15 minutes
  • Test Yourself: Calculus I - Derivatives•30 minutes
1 programming assignment•Total 180 minutes
  • Lab Homework: Derivatives•180 minutes

This module introduces the concept of integrals from calculus.

What's included

6 videos1 reading2 assignments1 programming assignment

6 videos•Total 90 minutes
  • Lecture 1: Intro to Integrals•10 minutes
  • Lecture 2: Riemann Summations–Approximating the Area Under the Curve•15 minutes
  • Lecture 3: Calculus Theorem–Relating Integrals to Derivatives•25 minutes
  • Lecture 4: Techniques for Solving Complex Integrals•12 minutes
  • Lecture 5: Multiple & Partial Integrals and Programming Integrals•8 minutes
  • Lecture 6: Numerical Integration, Chaos, and the Butterfly Effect•18 minutes
1 reading•Total 10 minutes
  • Jupyter Notebook Slides•10 minutes
2 assignments•Total 45 minutes
  • Let's Practice: Calculus II - Integrals•15 minutes
  • Test Yourself: Calculus II - Integrals•30 minutes
1 programming assignment•Total 180 minutes
  • Lab Homework: Integrals•180 minutes

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Build toward a degree

This course is part of the following degree program(s) offered by University of Pittsburgh. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹

Instructor

University of Pittsburgh
4 Courses•5,517 learners

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