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Understanding Statistical Models and Mathematical Models

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Janani Ravi

2:34:20

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  • 01 - Course Overview.mp4
    02:05
  • 02 - Prerequisites and Course Outline.mp4
    01:19
  • 03 - Understanding Mathematical Models and Statistical Models.mp4
    07:58
  • 04 - Mathematical Models and Statistical Models- Differences and Applications.mp4
    05:56
  • 05 - Data and Metadata.mp4
    06:16
  • 06 - Demo- Creating an Environment to Run the R Kernel on Jupyter Notebooks.mp4
    02:30
  • 07 - Demo- Associating Metadata Using the Comment Function.mp4
    02:25
  • 08 - Demo- Querying and Setting Metadata Using the Meta Function.mp4
    06:43
  • 09 - Modeling Population Growth Using ODEs.mp4
    03:31
  • 10 - Interpreting Derivatives.mp4
    05:27
  • 11 - Verhulsts Decreasing Growth Model.mp4
    03:42
  • 12 - Modeling Value at Risk- A Simplistic Model.mp4
    05:41
  • 13 - Understanding Monte Carlo Simulations.mp4
    04:09
  • 14 - Using Monte Carlo Simulations to Model Value at Risk.mp4
    05:23
  • 15 - Recap- Ordinary Differential Equations.mp4
    02:05
  • 16 - Demo- Calculating the Derivative of a Function.mp4
    05:09
  • 17 - Demo- Solving Differential Equations.mp4
    06:09
  • 18 - Demo- Solving Verhulsts Equation for Population Growth.mp4
    05:43
  • 19 - The 8 Queens Problem.mp4
    07:59
  • 20 - Local Search Optimization Techniques.mp4
    06:11
  • 21 - Demo- Setting up Helper Functions to Solve the 8 Queens Optimization Problem.mp4
    09:00
  • 22 - Demo- Applying Local Search Optimization to Solve the Eight Queens Problem.mp4
    06:06
  • 23 - Data Mining Statistics and Machine Learning.mp4
    04:41
  • 24 - Understanding Hypothesis Testing.mp4
    05:49
  • 25 - The T-test and the Z-test.mp4
    05:22
  • 26 - Demo- Exploring the Automobile Dataset.mp4
    05:17
  • 27 - Demo- One Sample T-test.mp4
    05:50
  • 28 - Demo- One Sample Z-test.mp4
    03:57
  • 29 - Demo- Two Sample T-test.mp4
    05:37
  • 30 - Demo- Paired Sample T-test and Z-test.mp4
    04:49
  • 31 - Summary and Further Study.mp4
    01:31
  • Description


    This course covers important techniques from both mathematical and statistical modeling, including the use of ordinary differential equations to model deterministic systems, classic local search and simulated annealing to explore large search spaces.

    What You'll Learn?


      Data science and data modeling are fast emerging as crucial capabilities that every enterprise and every technologist and it us important to choose the type of model most appropriate to your use-case. In this course, Understanding Statistical Models and Mathematical Models, you will gain the ability to differentiate between mathematical models and statistical models and pick the right type of model for your scenario.

      First, you will learn the important characteristics of mathematical and statistical models and their applications. Next, you will discover how classic mathematical models find wide applicability in solving differential equations and modeling deterministic systems.

      Then, you will also learn how statistical models are great for modeling systems with randomness, using business-based use-cases from risk management, and the use of Monte Carlo simulations. Finally, you will round out your knowledge performing hypothesis testing using T-tests and Z-tests on real-world data.

      When you’re finished with this course, you will have the skills and knowledge to use powerful techniques from both mathematical and statistical modeling, including solving simple ordinary differential equations, the use of simulated annealing and classic hill climbing, as well as hypothesis testing and statistical tests such as the T-test.

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    Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providing high-quality content for technical skill development. Loonycorn is working on developing an engine (patent filed) to automate animations for presentations and educational content.
    Pluralsight, LLC is an American privately held online education company that offers a variety of video training courses for software developers, IT administrators, and creative professionals through its website. Founded in 2004 by Aaron Skonnard, Keith Brown, Fritz Onion, and Bill Williams, the company has its headquarters in Farmington, Utah. As of July 2018, it uses more than 1,400 subject-matter experts as authors, and offers more than 7,000 courses in its catalog. Since first moving its courses online in 2007, the company has expanded, developing a full enterprise platform, and adding skills assessment modules.
    • language english
    • Training sessions 31
    • duration 2:34:20
    • level preliminary
    • Release Date 2023/10/14

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