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Statistical Decision Making in Data Science with Case Study
Rating: 4.2 out of 5(124 ratings)
20,077 students

Statistical Decision Making in Data Science with Case Study

Understand how Statistics is Applied to Data Science Problem like ANOVA, t-test, F-test in Python
Created bydatascience Anywhere, Sudhir G, Convolution Innovations
Last updated 7/2021
English
English [Auto],

What you'll learn

  • Least Square Regression
  • Build OLS in Statsmodel
  • Hypothesis Testing
  • t test
  • ANOVA
  • F Statistics
  • Degree of Freedom of the Model
  • Plotting Regression Line above the scatter plot (Fitted Values)
  • Predicting Results
  • Answer Question statistically

Course content

6 sections • 14 lectures • 51m total length

  • Introduction3:26

    Explore regression concepts and machine learning models using a Big Mac Index case study. Master least-squares regression, hypothesis testing, and Python-based forecasting for decision making.

  • Google Colab5:29

    Install Google Colab and connect it to your Google Drive, then mount the drive and authenticate with your Google account to organize notebooks in data science or time series folders.

Requirements

  • Understanding Statistics
  • Beginner to Python

Description

Welcome to the course "Statistical Decision Making in Data Science with a Case Study in Python"

This course is an introduction course where you will learn about the importance of Statistics and Machine Learning in Decision Making. I explained this course with a case study. We start with a problem statement and data then we build the machine learning model. Building a machine learning model is really not enough but getting a decision out of machine learning is the primary goal in Data Science. For that, we will use statistics.


What you will Learn?

  1. Understand the Problem statement (Case Study on Big Mac Index with used in Forex Industry for Predicting Dollar value)

  2. Asking Statistical Question.

  3. Linear Regression (Least Square Regression)

  4. Develop Least Square Regression in Python.

  5. Understand the Outputs

    1. MSE

    2. Degree of Freedom

  6. Hypothesis testing

    1. t-test for coefficient significance

    2. F-test for model significance

    3. ANOVA

  7. Correlation

  8. R-Square


You will learn the approaches towards regression with case study.  First we start with understanding linear equation and the optimization function value sum of squared errors.  With that we find the values of the coefficient and makes least square regression. Then we starts building our linear regression in python.

For the model we build we necessary test like hypothesis testing.

  • t-test for coefficient significance

  • ANOVA and F-test for model significance.

And finally, we answer the question statically. Hope we are seeing you inside the course !!!

Who this course is for:

  • Beginner of Python Developer who want to learn Data Science
  • Solving question related to linear regression

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