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 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

Hypothesis Testing in Data Science

7m 27s

Null and Alternative Hypothesis

9m 6s

Making a Decision - Reject or Fail to Reject Null Hypothesis

9m 21s

Type I & Type II Errors

12m 47s

Covariance and Correlation Coefficients

11m 47s

Types of Correlation Coefficients

15m 49s

Assignment : Hypothesis Testing

3m 53s

Quiz : Hypothesis Testing

5m

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

Measures of Central Tendency - Mode

Statistics For Data Science Course Descriptive Statistics

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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 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

Hypothesis Testing in Data Science

7m 27s

Null and Alternative Hypothesis

9m 6s

Making a Decision - Reject or Fail to Reject Null Hypothesis

9m 21s

Type I & Type II Errors

12m 47s

Covariance and Correlation Coefficients

11m 47s

Types of Correlation Coefficients

15m 49s

Assignment : Hypothesis Testing

3m 53s

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

The mean, on the other hand, is commonly regarded as the best measure of central tendency in this case since it is the measure that is calculated using all of the values in the data set, and any change in any of the scores will alter the value of the mean. The median and mode, on the other hand, are not in this category.

The mode is a measure of central tendency that identifies the most often occurring category or score within a data distribution. In other terms, it is the most common score in a distribution, or the score that appears the most frequently.

A data set's mode is the number that appears the most frequently in the data. Put the numbers in order from least to greatest and count how many times each number appears to find the mode quickly. The mode is the number that appears the most.

The observation with the highest frequency is the mode. The term "unimodal" refers to a data set that only has one value that occurs frequently. A data set is called bimodal if it contains two values that occur with the greatest frequency.

For grouped data, use the following formula: Mode = L + (fmf1)h /2fmf1f2. Where L denotes the modal class's lower limit mode. fm denotes the frequency of the modal class.

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