Statistics 2024 A-Z™- For Data Science with Both Python R

Statistics 2024 A-Z&: For Data Science with Both Python & R, available at $69.99, has an average rating of 4.7, with 41 lectures, based on 149 reviews, and has 1985 subscribers.

You will learn about Statistics Data Analysis Business Analytics Regression Analysis Descriptive Statistics Inferential Statistics Hypothesis Testing T-Test Chi Square Test AnOVa Linear Regression Logistic Regression Machine Learning Data Science This course is ideal for individuals who are Beginner or Intermediate or Advanced It is particularly useful for Beginner or Intermediate or Advanced.

Enroll now: Statistics 2024 A-Z&: For Data Science with Both Python & R

Summary

Title: Statistics 2024 A-Z&: For Data Science with Both Python & R

Price: $69.99

Average Rating: 4.7

Number of Lectures: 41

Number of Published Lectures: 39

Number of Curriculum Items: 41

Number of Published Curriculum Objects: 39

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

Statistics

Data Analysis

Business Analytics

Regression Analysis

Descriptive Statistics

Inferential Statistics

Hypothesis Testing

T-Test

Chi Square Test

AnOVa

Linear Regression

Logistic Regression

Machine Learning

Data Science

Who Should Attend

Beginner

Intermediate

Advanced

Target Audiences

Beginner

Intermediate

Advanced

Data Science and Analytics is a highly rewarding career that allows you to solve some of the world’s most interesting problems and Statistics the base for all the analysis and Machine Learning models. This makes statistics a necessary part of the learning curve. Analytics without Statistics is baseless and can anytime go in the wrong direction.

For a majority of Analytics professionals and Beginners, Statistics comes as the most intimidating, doubtful topic, which is the reason why we have created this course for those looking forward to learn Statistics and apply various statistical methods for analysis with the most elaborate explanations and examples!

This course is made to give you all the required knowledge at the beginning of your journey, so that you don’t have to go back and look at the topics again at any other place. This course is the ultimate destination with all the knowledge, tips and trick you would require to start your career.

This course providesFull-fledged knowledge of Statistics, we cover it all.

Our exotic journey will include the concepts of:

1. What’s and Why’s of Statistics –Understanding the need for Statistics, difference between Population and Samples, various Sampling Techniques.

2. Descriptive Statisticswill include the Measures Of central tendency– Mean, Median, Mode and the Measures of Variability – Variance, SD, IQR, Bessel’s Correction

3. Further you will learn about the Shapes Of distribution – Bell Curve, Kurtosis, Skewness.

4. You will learn about various types of variables, their interactions like Correlation, Covariance, Collinearity, Multicollinearity, feature creation and selection.

5. As part of Inferential statistics,you will learnvarious Estimation Techniques, Properties of Normal Curve, Central Limit Theorem calculation and representation of Z Score and Confidence Intervals.

6. In Hypothesis Testingyou will learn how to formulate a Null Hypothesisand the corresponding Alternate Hypothesis.

7. You will learn how to choose and perform various hypothesis tests like Z – test, One Sample T Test, Independent T Test, Paired T Test, Chi Square – Goodness Of Fit, Chi-Square Test for Independence, ANOVA

8. In regression Analysisyou will learn about end-to-endvariable creation selection data transformation, model building and Evaluation process forboth Linearand Logistic Regression.

9. In-depth explanation for Statistical Methods with all the real-life tips and tricks to give you an edge from someone who has just the introductory knowledge which is usually not provided in a beginner course.

10. All explanations provided in a simple language to make it easy to understand and work on in future.

11. Hands-on practice on more than 15 different Datasets to give you a quick start and learning advantage of working on different datasets and problems.

Course Curriculum

Chapter 1: Introduction to Course

Lecture 1: Introduction

Chapter 2: Descriptive Statistics Explained

Lecture 1: Introduction to Statistics_Population & Sampling

Lecture 2: Measure Of Central Tendencies Mean Median Mode

Lecture 3: Measure Of Variability – Variance Standard Deviation IQR

Lecture 4: Data Diatributions Correlation & Covariance

Lecture 5: Practice Questions: Descriptive Statistics

Chapter 3: Intro to Inferential Statistics

Lecture 1: Intro to Inferential Statistics

Lecture 2: Variable Types

Chapter 4: Inferential Statistics: Central Limit Theorem,Z-Score,Confidence Interval

Lecture 1: Central Limit Theorem

Lecture 2: Z-Score

Lecture 3: Confidence Interval

Lecture 4: CI examples

Chapter 5: Hypothesis Testing

Lecture 1: Hypothesis Testing Introduction

Lecture 2: Hypothesis Testing Theory Explained

Lecture 3: Type of Errors and Significant Difference

Chapter 6: T-test Family

Lecture 1: One Sample, Independent, Paired T Test

Chapter 7: Chi-Square Tests

Lecture 1: Chi Square test of Goodness of Fit

Lecture 2: Chi Square test of Independance

Chapter 8: ANOVA

Lecture 1: ANOVA

Lecture 2: Which test to pick?

Lecture 3: Statistics Using Graphpad

Chapter 9: Practice Questions in Python: Descriptive and Inferential Statistics

Lecture 1: Z-Score questions

Lecture 2: T-tests questions

Lecture 3: Chi Test, Anova, Cov, Correlation questions

Chapter 10: Statistics using Python – Case Studies

Lecture 1: House Prices Dataset – Case Study -1

Lecture 2: City Payroll Dataset – Case Study -2

Chapter 11: Descriptive Statistics Using R -Practice

Lecture 1: Descriptive Statistics using R Practice Questions

Chapter 12: Inferential Statistics Using R – Practice

Lecture 1: Inferential Statistics Using R Practice Questions

Chapter 13: Statistics using R – Case Studies

Lecture 1: Census Income Dataset – Case Study -1

Chapter 14: Linear Regression Analysis using Python

Lecture 1: Regression Analysis Explained – Linear Regression

Lecture 2: Linear Regression Cost, Gradient and Cross Validation

Lecture 3: Linear Regression from scratch

Lecture 4: Linear Regression Regularization

Chapter 15: Logistic Regression Analysis using Python

Lecture 1: Logistic Regression Introduction

Lecture 2: 06_Logistic Regression_Mathematics

Lecture 3: 07 Logistic Regression Metrics

Lecture 4: Logistic Regression Implementation

Chapter 16: Linear Regression Analysis using R

Lecture 1: Linear Regression Analysis using R

Chapter 17: Logistic Regression Analysis using R

Lecture 1: Logistic Regression Analysis using R

Instructors

MG Analytics
Data Scientist and Professional Trainer

Rating Distribution

1 stars: 6 votes

2 stars: 5 votes

3 stars: 9 votes

4 stars: 37 votes

5 stars: 92 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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