Statistical Decision Making in Data Science with Case Study
Statistical Decision Making in Data Science with Case Study, available at Free, has an average rating of 4.55, with 14 lectures, based on 98 reviews, and has 19247 subscribers.
You will learn about 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 This course is ideal for individuals who are Beginner of Python Developer who want to learn Data Science or Solving question related to linear regression It is particularly useful for Beginner of Python Developer who want to learn Data Science or Solving question related to linear regression.
Enroll now: Statistical Decision Making in Data Science with Case Study
Summary
Title: Statistical Decision Making in Data Science with Case Study
Price: Free
Average Rating: 4.55
Number of Lectures: 14
Number of Published Lectures: 14
Number of Curriculum Items: 14
Number of Published Curriculum Objects: 14
Original Price: Free
Quality Status: approved
Status: Live
What You Will 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
Who Should Attend
- Beginner of Python Developer who want to learn Data Science
- Solving question related to linear regression
Target Audiences
- Beginner of Python Developer who want to learn Data Science
- Solving question related to linear regression
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 Statisticsand 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?
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Understand the Problem statement (Case Study on Big Mac Index with used in Forex Industry for Predicting Dollar value)
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Asking Statistical Question.
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Linear Regression (Least Square Regression)
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Develop Least Square Regression in Python.
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Understand the Outputs
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MSE
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Degree of Freedom
-
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Hypothesis testing
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t-test for coefficient significance
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F-test for model significance
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ANOVA
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Correlation
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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.
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t-test for coefficient significance
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ANOVA and F-test for model significance.
And finally, we answer the question statically. Hope we are seeing you inside the course !!!
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction
Lecture 2: Google Colab
Chapter 2: Case Study – Big Mac Index
Lecture 1: Big Mac Index Case Study Walk through
Chapter 3: Least Square Regression
Lecture 1: Least Square RegressionLinear Regression , y = a+b X and SSE
Chapter 4: Least Square Regression in Python
Lecture 1: Load Data & Scatter Plot
Lecture 2: Fitting Ordinary Least Square Regression Model in Python
Chapter 5: Hypothesis Testing to model
Lecture 1: Degree of Freedom of the Model
Lecture 2: Hypothesis testing : t – test
Lecture 3: Fitted Values
Lecture 4: Hypothesis testing : ANOVA
Lecture 5: F – Statistics in Python
Lecture 6: R Square
Lecture 7: Answers
Chapter 6: Bonus
Lecture 1: bonus
Instructors
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datascience Anywhere
Team of Engineers -
Sudhir G
Data Scientist -
Convolution Innovations
Academy
Rating Distribution
- 1 stars: 4 votes
- 2 stars: 7 votes
- 3 stars: 11 votes
- 4 stars: 31 votes
- 5 stars: 45 votes
Frequently Asked Questions
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