Currently Migrating into Quarto platform. See the progress on here :)

Posts

  • Earthquakes and oil mining in Oklahoma

    Of course, earthquakes have a big impact on society, and recently are connected to human activity. In this final chapter, you'll investigate the effect that increased injection of saline wastewater due to oil mining in Oklahoma has had on the seismicity of the region. This is the Summary of lecture "Case Studies in Statistical Thinking", via datacamp.

    Jun 24, 2020

  • Statistical seismology and the Parkfield region

    Herein, you'll use your statistical thinking skills to study the frequency and magnitudes of earthquakes. Along the way, you'll learn some basic statistical seismology, including the Gutenberg-Richter law. This exercise exposes two key ideas about data science 1) As a data scientist, you wander into all sorts of domain specific analyses, which is very exciting. You constantly get to learn. 2) You are sometimes faced with limited data, which is also the case for many of these earthquake studies. This is the Summary of lecture "Case Studies in Statistical Thinking", via datacamp.

    Jun 24, 2020

  • The Current Controversy of the 2013 World Championships

    Some swimmers said that they felt it was easier to swim in one direction versus another in the 2013 World Championships. Some analysts have posited that there was a swirling current in the pool. In this chapter, you'll investigate this claim! References - Quartz Media, Washington Post, SwimSwam, and Cornett, et al. This is the Summary of lecture "Case Studies in Statistical Thinking", via datacamp.

    Jun 24, 2020

  • Model-free Policy Iteration with Monte Carlo Methods

    A summary of "Understanding Deep Reinforcement Learning"

    Jun 23, 2020

  • Analysis of results of the 2015 FINA World Swimming Championships

    In this chapter, you will practice your EDA, parameter estimation, and hypothesis testing skills on the results of the 2015 FINA World Swimming Championships. This is the Summary of lecture "Case Studies in Statistical Thinking", via datacamp.

    Jun 23, 2020

  • Fish sleep and bacteria growth - A review of Statistical Thinking I and II

    To begin, you'll use two data sets from Caltech researchers to rehash the key points of Statistical Thinking I and II to prepare you for the following case studies! This is the Summary of lecture "Case Studies in Statistical Thinking", via datacamp.

    Jun 23, 2020

  • epsilon-Soft Random Action

    A summary of "Understanding Deep Reinforcement Learning"

    Jun 22, 2020

  • Advanced Applications of Simulation

    In this chapter, students will be introduced to some basic and advanced applications of simulation to solve real-world problems. We'll work through a business planning problem, learn about Monte Carlo Integration, Power Analysis with simulation and conclude with a financial portfolio simulation. After completing this chapter, students will be ready to apply simulation to solve everyday problems. This is the Summary of lecture "Statistical Simulation in Python", via datacamp.

    Jun 22, 2020

  • Model-free Control

    A summary of "Understanding Deep Reinforcement Learning"

    Jun 22, 2020

  • Resampling methods

    In this chapter, we will get a brief introduction to resampling methods and their applications. We will get a taste of bootstrap resampling, jackknife resampling, and permutation testing. After completing this chapter, students will be able to start applying simple resampling methods for data analysis. This is the Summary of lecture "Statistical Simulation in Python", via datacamp.

    Jun 22, 2020

  • Probability and data generation process

    This chapter provides a basic introduction to probability concepts and a hands-on understanding of the data generating process. We'll look at a number of examples of modeling the data generating process and will conclude with modeling an eCommerce advertising simulation. This is the Summary of lecture "Statistical Simulation in Python", via datacamp.

    Jun 21, 2020

  • Basics of randomness and simulation

    This chapter gives you the tools required to run a simulation. We'll start with a review of random variables and probability distributions. We will then learn how to run a simulation by first looking at a simulation workflow and then recreating it in the context of a game of dice. Finally, we will learn how to use simulations for making decisions. This is the Summary of lecture "Statistical Simulation in Python", via datacamp.

    Jun 21, 2020

  • Estimating Model Parameters

    In our final chapter, we introduce concepts from inferential statistics, and use them to explore how maximum likelihood estimation and bootstrap resampling can be used to estimate linear model parameters. We then apply these methods to make probabilistic statements about our confidence in the model parameters. This is the Summary of lecture "Introduction to Linear Modeling in Python", via datacamp.

    Jun 21, 2020

  • Making Model Predictions

    Next we will apply models to real data and make predictions. We will explore some of the most common pit-falls and limitations of predictions, and we evaluate and compare models by quantifying and contrasting several measures of goodness-of-fit, including RMSE and R-squared. This is the Summary of lecture "Introduction to Linear Modeling in Python", via datacamp.

    Jun 20, 2020

  • Building Linear Models

    Here we look at the parts that go into building a linear model. Using the concept of a Taylor Series, we focus on the parameters slope and intercept, how they define the model, and how to interpret the them in several applied contexts. We apply a variety of python modules to find the model that best fits the data, by computing the optimal values of slope and intercept, using least-squares, numpy, statsmodels, and scikit-learn. This is the Summary of lecture "Introduction to Linear Modeling in Python", via datacamp.

    Jun 19, 2020