Course Content

Introduction to Statistics For Data Analysis and Data Science

5m 22s

How to use LearnVern

3m 20s

Fundamentals of Statistics

15m 45s

Basics of Descriptive Statistics

12m 47s

Measures of Frequency

10m 47s

Measures of Central Tendency - Mean

9m 9s

Measures of Central Tendency - Median

4m 48s

Measures of Central Tendency - Mode

5m 21s

Measures of Variability - Mean Absolute Deviation

7m 33s

Measures of Variability - Variance

7m 42s

Measures of Variability - Standard Deviation

10m 36s

Measures of Position

16m

Measures of Shape - Skewness

10m 36s

Measures of Shape - Kurtosis, Box and Whisker Plot

8m 51s

Assignment : Descriptive Statistics

3m 54s

Quiz : Descriptive Statistics

12m

Basics of Inferential Statistics

5m 3s

Introduction to Probability

17m 26s

Basics of Probability

21m 36s

Probability Distribution

7m 40s

Discrete Probability Distribution

19m 25s

Continuous Probability Distribution

12m 9s

Uniform Distribution

4m 16s

Normal Distribution

10m 18s

Standard Normal Distribution

9m 20s

Log Normal Distribution

3m 49s

Exponential Distribution

5m 33s

Methods to Detect Outliers

9m 26s

Methods to Treat Outliers in Python

15m 42s

Feature Scaling - Normalization V/S Standardization

10m 35s

Sampling Methods

12m 43s

Sampling Distribution

18m 30s

Central Limit Theorem

13m 28s

Assignment : Inferential Statistics

7m 18s

Quiz : Inferential Statistics

12m

Types of Statistical Tests

3m 9s

Z-Test: Critical Value Method

13m 9s

Examples of Z-Test: Critical Value Method

9m 20s

Z-Test: P-Value Method

9m 33s

Examples of Z-Test: P-Value Method

9m 10s

T-Test: One Sample Mean Test

15m 16s

T-Test: Paired Two Sample Mean Test

14m 13s

T-Test : Unpaired Two-Sample Mean Test

17m 6s

Two Sample Proportion Test

14m 43s

Chi-Square Test: Independence Test

12m 57s

Chi-Square Test: Goodness of Fit

6m 51s

ANOVA Test or F-Test

24m 21s

Assignment : Types of Hypothesis Testing

5m 40s

Quiz : Types of Hypothesis Tests

5m

Basics of Linear Regression Analysis

25m 45s

Assumptions of Linear Regression

10m 20s

Multiple Linear Regression Analysis

14m 50s

Quiz : Regression Analysis

4m

Course Summary

4m 30s

Interview Questions

16m 12s

Career Guideline

2m 17s

Covariance and Correlation Coefficients

Statistics For Data Science Course Hypothesis Testing

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  • Z-Table, T-Table , Chi-Square Table

Course Content

Introduction to Statistics For Data Analysis and Data Science

5m 22s

How to use LearnVern

3m 20s

Fundamentals of Statistics

15m 45s

Basics of Descriptive Statistics

12m 47s

Measures of Frequency

10m 47s

Measures of Central Tendency - Mean

9m 9s

Measures of Central Tendency - Median

4m 48s

Measures of Central Tendency - Mode

5m 21s

Measures of Variability - Mean Absolute Deviation

7m 33s

Measures of Variability - Variance

7m 42s

Measures of Variability - Standard Deviation

10m 36s

Measures of Position

16m

Measures of Shape - Skewness

10m 36s

Measures of Shape - Kurtosis, Box and Whisker Plot

8m 51s

Assignment : Descriptive Statistics

3m 54s

Basics of Inferential Statistics

5m 3s

Introduction to Probability

17m 26s

Basics of Probability

21m 36s

Probability Distribution

7m 40s

Discrete Probability Distribution

19m 25s

Continuous Probability Distribution

12m 9s

Uniform Distribution

4m 16s

Normal Distribution

10m 18s

Standard Normal Distribution

9m 20s

Log Normal Distribution

3m 49s

Exponential Distribution

5m 33s

Methods to Detect Outliers

9m 26s

Methods to Treat Outliers in Python

15m 42s

Feature Scaling - Normalization V/S Standardization

10m 35s

Sampling Methods

12m 43s

Sampling Distribution

18m 30s

Central Limit Theorem

13m 28s

Assignment : Inferential Statistics

7m 18s

Types of Statistical Tests

3m 9s

Z-Test: Critical Value Method

13m 9s

Examples of Z-Test: Critical Value Method

9m 20s

Z-Test: P-Value Method

9m 33s

Examples of Z-Test: P-Value Method

9m 10s

T-Test: One Sample Mean Test

15m 16s

T-Test: Paired Two Sample Mean Test

14m 13s

T-Test : Unpaired Two-Sample Mean Test

17m 6s

Two Sample Proportion Test

14m 43s

Chi-Square Test: Independence Test

12m 57s

Chi-Square Test: Goodness of Fit

6m 51s

ANOVA Test or F-Test

24m 21s

Assignment : Types of Hypothesis Testing

5m 40s

Basics of Linear Regression Analysis

25m 45s

Assumptions of Linear Regression

10m 20s

Multiple Linear Regression Analysis

14m 50s

Course Summary

4m 30s

Interview Questions

16m 12s

Career Guideline

2m 17s

FAQs

Because the correlation coefficient is represented by a r, this formula states that it equals the covariance between the variables divided by the product of each variable's standard deviations.

Covariance and correlation are two concepts that are diametrically opposed and are both employed in statistics and regression analysis. Covariance shows us how the two variables differ from each other, whilst correlation shows us how the two variables are related.

Both statistics and regression analysis utilise the phrases covariance and correlation, which are diametrically opposed. The covariance of two variables reveals how they differ, but the correlation shows how they are related.

In statistics and probability theory, the terms variance and covariance are widely employed. The dispersion of a data set about its mean value is known as variance, whereas the measure of the directional relationship between two random variables is known as covariance.

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