Regression Analysis for Business Managers in Python and R

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Regression Analysis for Business Managers in Python and R [TutsNode.com] - Regression Analysis for Business Managers in Python and R 04 Linear Regression - R
  • 015 How much would my apartment cost_.mp4 (70.2 MB)
  • 008 Scatterplotting.en.srt (9.4 KB)
  • 015 How much would my apartment cost_.en.srt (8.6 KB)
  • 001 Loading data.en.srt (7.6 KB)
  • 006 Transforming variable into numeric advanced.en.srt (7.5 KB)
  • 002 Handpicking variables.en.srt (6.2 KB)
  • 005 Transforming variable into numeric.en.srt (5.8 KB)
  • 009 Removing outliers.en.srt (5.7 KB)
  • 014 Regression summary.en.srt (5.2 KB)
  • 007 Summary statistics.en.srt (5.1 KB)
  • 012 Log transforming variables.en.srt (4.8 KB)
  • 010 Correlation Matrix.en.srt (4.6 KB)
  • 004 Transforming variables into factors.en.srt (4.1 KB)
  • 006 Transforming variable into numeric advanced.mp4 (63.4 MB)
  • 003 Dataset strategy.en.srt (3.4 KB)
  • 013 Linear Regression.en.srt (2.6 KB)
  • 011 Dropping variables.en.srt (2.2 KB)
  • 008 Scatterplotting.mp4 (59.5 MB)
  • 005 Transforming variable into numeric.mp4 (44.7 MB)
  • 001 Loading data.mp4 (43.9 MB)
  • 009 Removing outliers.mp4 (40.4 MB)
  • 010 Correlation Matrix.mp4 (37.3 MB)
  • 002 Handpicking variables.mp4 (37.1 MB)
  • 007 Summary statistics.mp4 (36.3 MB)
  • 014 Regression summary.mp4 (34.9 MB)
  • 012 Log transforming variables.mp4 (33.4 MB)
  • 004 Transforming variables into factors.mp4 (30.5 MB)
  • 003 Dataset strategy.mp4 (19.8 MB)
  • 013 Linear Regression.mp4 (19.3 MB)
  • 011 Dropping variables.mp4 (16.2 MB)
01 Introduction
  • 001 Introduction.en.srt (9.7 KB)
  • 004 Reviews and future of this course.en.srt (2.4 KB)
  • 005 Let's connect!.html (1.3 KB)
  • 002 Installing Python and Spyder.en.srt (4.1 KB)
  • 003 Installing R and RStudio.en.srt (3.9 KB)
  • 004 Reviews and future of this course.mp4 (33.3 MB)
  • 002 Installing Python and Spyder.mp4 (27.7 MB)
  • 003 Installing R and RStudio.mp4 (25.4 MB)
  • 001 Introduction.mp4 (18.4 MB)
02 Linear Regression - Intuition
  • 003 Pricing demand factors.en.srt (9.0 KB)
  • 004 Supply demand factors.en.srt (6.6 KB)
  • 007 How to read coefficients.en.srt (6.5 KB)
  • 009 (Adjusted) R-squared.en.srt (2.8 KB)
  • 012 Linear regression step by step guide.en.srt (2.8 KB)
  • 001 Linear Regression objectives.en.srt (5.3 KB)
  • 013 Case study briefing.en.srt (2.5 KB)
  • 008 Dummy variable trap.en.srt (4.6 KB)
  • 010 RSME vs MAE.en.srt (4.5 KB)
  • 006 Linear Regression summary.en.srt (4.1 KB)
  • 005 Linear Regression.en.srt (4.0 KB)
  • 002 What influences pricing.en.srt (3.4 KB)
  • 011 Outliers.en.srt (3.1 KB)
  • 018 Regression Analysis for Business Managers in Python and R templates.zip (388.1 KB)
  • 008 Dummy variable trap.mp4 (26.8 MB)
  • 007 How to read coefficients.mp4 (19.6 MB)
  • 006 Linear Regression summary.mp4 (17.3 MB)
  • 003 Pricing demand factors.mp4 (16.5 MB)
  • 010 RSME vs MAE.mp4 (12.3 MB)
  • 005 Linear Regression.mp4 (12.3 MB)
  • 004 Supply demand factors.mp4 (11.2 MB)
  • 013 Case study briefing.mp4 (10.0 MB)
  • 009 (Adjusted) R-squared.mp4 (9.9 MB)
  • 001 Linear Regression objectives.mp4 (8.6 MB)
  • 011 Outliers.mp4 (7.1 MB)
  • 002 What influences pricing.mp4 (5.8 MB)
  • 012 Linear regression step by step guide.mp4 (5.4 MB)
05 Logistic Regression - Intuition
  • 002 Understanding Churn.en.srt (7.7 KB)
  • 007 Confusion Matrix.en.srt (7.0 KB)
  • 006 Over vs underfitting & Bias Variance trade off.en.srt (6.4 KB)
  • 003 Preventing Churn.en.srt (5.0 KB)
  • 004 Logistic Regression.en.srt (4.9 KB)
  • 001 Logistic Regression Objectives.en.srt (4.3 KB)
  • 005 Training and test set.en.srt (3.5 KB)
  • 009 Case study briefing.en.srt (2.8 KB)
  • 008 Logistic Regression - step by step guide.en.srt (2.0 KB)
  • 056 Regression Analysis for Business Managers in Python and R templates.zip (388.1 KB)
  • 007 Confusion Matrix.mp4 (20.2 MB)
  • 002 Understanding Churn.mp4 (15.7 MB)
  • 004 Logistic Regression.mp4 (15.5 MB)
  • 009 Case study briefing.mp4 (11.1 MB)
  • 006 Over vs underfitting & Bias Variance trade off.mp4 (10.8 MB)
  • 003 Preventing Churn.mp4 (10.3 MB)
  • 001 Logistic Regression Objectives.mp4 (7.1 MB)
  • 005 Training and test set.mp4 (5.9 MB)
  • 008 Logistic Regression - step by step guide.mp4 (3.8 MB)
07 Logistic Regression - R
  • 005 Outlier detection.en.srt (7.4 KB)
  • 010 Logistic Regression.en.srt (7.2 KB)
  • 006 Removing outliers.en.srt (7.1 KB)
  • 001 Loading libraries and dataset.en.srt (6.6 KB)
  • 012 Confusion Matrix.en.srt (6.5 KB)
  • 004 Summary statistics.en.srt (5.6 KB)
  • 011 Predictions.en.srt (4.7 KB)
  • 009 Training and test set.en.srt (4.6 KB)
  • 002 Data structure.en.srt (3.6 KB)
  • 003 Transforming objects into factors.en.srt (3.2 KB)
  • 008 Correlation Matrix.en.srt (2.8 KB)
  • 007 Transforming variable into its logarithm.en.srt (1.7 KB)
  • 005 Outlier detection.mp4 (53.9 MB)
  • 006 Removing outliers.mp4 (53.8 MB)
  • 004 Summary statistics.mp4 (47.0 MB)
  • 012 Confusion Matrix.mp4 (46.3 MB)
  • 010 Logistic Regression.mp4 (42.5 MB)
  • 001 Loading libraries and dataset.mp4 (39.2 MB)
  • 009 Training and test set.mp4 (33.7 MB)
  • 011 Predictions.mp4 (33.4 MB)
  • 008 Correlation Matrix.mp4 (27.0 MB)
  • 003 Transforming objects into factors.mp4 (22.9 MB)
  • 002 Data structure.mp4 (22.4 MB)
  • 007 Transforming variable into its logarithm.mp4 (11.3 MB)
03 Linear Regression - Python

Description


Description

Regression analysis is the most common tool at the disposal of anyone looking to analyze data. If you are looking to derive meaning insights from your data, then this course is for you.

3 reasons this course is unique:

You learn not only techniques, but you also learn about Business. The intuition tutorials have their beginning dedicated to explaining to you the relevance of the business problem. By the end of the course, you will be able to discuss matters with your stakeholders related to Pricing or Customer Churn.

Real-life experience. Coding a Regression is a matter of just a couple of lines of code. However, life is not that simple. Almost always, you get a dirty dataset that you need to transform and manipulate to make it a usable and useful dataset. The practice tutorials mirror that experience. We will go through standard techniques to:

Transform data
Visualize outliers
Assess which variables are the best to use.

We code together. In R or Python, I will guide you every step of the way, explaining all steps required to make an excellent regression analysis.

Did I pique your interest? I am looking forward to seeing you inside the course.
Who this course is for:

Professionals looking to learn about regression analysis
Graduates wanting to have a practical view on Regressions
Business Managers looking into their first steps into Data Science

Requirements

Basic math: mean, median, standard deviation

Last Updated 7/2021



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Regression Analysis for Business Managers in Python and R

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2.2 GB
seeders:13
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Regression Analysis for Business Managers in Python and R

Torrent hash: BD9522A9776965CBE38F4A3941D6ED6CB766E462