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

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

Basics of Probability

Statistics For Data Science Course Inferential 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 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

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

A probability is a number that represents the possibility of an event occurring. Probabilities can be stated as percentages ranging from 0% to 100% or as proportions ranging from 0 to 1.

There are three kinds of conditional probabilities - mutually exclusive, exhaustive, and dependent. Mutual exclusivity means that there’s only one possible outcome for each outcome; Expansive means that all outcomes have been examined; Dependent means that the outcome is dependent on another event.

P(E) = [Number of favourable outcomes of E]/[total number of possible outcomes of E] is the probability of an occurrence E. 2. A sure occurrence or certain event has a probability of 1.

The addition, multiplication, and complement laws are the three basic rules of probability. The likelihood of event A or event B occurring is calculated using the addition rule, which is expressed as: P(A or B) = P(A) + P(B) - P(A and B)

Probability is a mathematical concept that describes the likelihood of something happening. Weather patterns, for example, are used by meteorologists to forecast the likelihood of rain. Probability theory is utilised in epidemiology to investigate the relationship between exposures and the risk of adverse health outcomes.

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