# Big Data Power Tools Bundle

\$36.00\$516.00
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## What's Included

• Certification included
• Experience level required: All levels
• Access 40 lectures & 5 hours of content 24/7
• Length of time users can access this course: Lifetime

## Course Curriculum

### 40 Lessons (5h)

• Introduction
You, This Course and Us1:54
• Connect the Dots with Linear Regression
Using Linear Regression to Connect the Dots9:04
Two Common Applications of Regression5:24
Extending Linear Regression to Fit Non-linear Relationships2:36
• Basic Statistics Used for Regression
Understanding Mean and Variance6:03
Understanding Random Variables16:54
The Normal Distribution9:31
• Simple Regression
Setting up a Regression Problem11:36
Using Simple regression to Explain Cause-Effect Relationships4:57
Using Simple regression for Explaining Variance8:07
Using Simple regression for Prediction4:04
Interpreting the results of a Regression7:25
Mitigating Risks in Simple Regression7:56
• Applying Simple Regression Using Excel
Applying Simple Regression in Excel11:57
Applying Simple Regression in R11:14
Applying Simple Regression in Python6:05
• Multiple Regression
Introducing Multiple Regression7:03
Some Risks inherent to Multiple Regression10:06
Benefits of Multiple Regression3:48
Introducing Categorical Variables6:58
Interpreting Regression results - Adjusted R-squared7:02
Interpreting Regression results - Standard Errors of Co-efficients8:12
Interpreting Regression results - t-statistics and p-values5:32
Interpreting Regression results - F-Statistic2:52
• Applying Multiple Regression using Excel
Implementing Multiple Regression in Excel8:54
Implementing Multiple Regression in R6:26
Implementing Multiple Regression in Python4:21
• Logistic Regression for Categorical Dependent Variables
Understanding the need for Logistic Regression9:24
Setting up a Logistic Regression problem6:02
Applications of Logistic Regression9:55
The link between Linear and Logistic Regression8:13
The link between Logistic Regression and Machine Learning4:16
• Solving Logistic Regression
Understanding the intuition behind Logistic Regression and the S-curve6:21
Solving Logistic Regression using Maximum Likelihood Estimation10:02
Solving Logistic Regression using Linear Regression5:32
Binomial vs Multinomial Logistic Regression4:04
• Applying Logistic Regression
Predict Stock Price movements using Logistic Regression in Excel9:52
Predict Stock Price movements using Logistic Regression in R8:00
Predict Stock Price movements using Rule-based and Linear Regression6:44
Predict Stock Price movements using Logistic Regression in Python4:49

### Connect the Dots: Linear and Logistic Regression in Excel, Python and R

L
Loonycorn

Loonycorn is comprised of four individuals—Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh—who have honed their tech expertises at Google and Flipkart. The team believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.

## Description

Linear Regression is a powerful method for quantifying the cause and effect relationships that affect different phenomena in the world around us. This course will teach you how to build robust linear models that will stand up to scrutiny when you apply them to real world situations. You'll even put what you've learnt into practice by leveraging Excel, R, and Python to build a model for stock returns.

• Access 40 lectures & 5 hours of content 24/7
• Cover method of least squares, explaining variance, & forecasting an outcome
• Explore residuals & assumptions about residuals
• Implement simple & multiple regression in Excel, R, & Python
• Interpret regression results & avoid common pitfalls
• Introduce a categorical variable

## Specs

Details & Requirements

• Length of time users can access this course: lifetime
• Access options: web streaming, mobile streaming
• Certification of completion not included