Learning Statistics with Python

Learning Statistics with Python

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1. Basic Concepts

Sample vs Population

Types of Statistics

Median Value of the Even Number of Values

Descriptive Statistics Quiz

2. Mean, Median and Mode with Python

Examine the Dataset

Calculating Mean and Median Values with Python

Statistics with pandas

Calculate the Mean and Median Salary

3. Variance and Standard Deviation

Population Variance

Sample Variance

Calculate Variance with Python

Standard deviation

Standard Deviation with Python

Calculating Variance and Standard Deviation

4. Covariance vs Correlation

Select All Possible Values

Calculate Covariance and Correlation

5. Confidence Interval

This section will help us deal with the first real statistical case: finding confidence intervals. It requires knowledge of NumPy, pandas, Matplotlib, and Seaborn library to calculate math formulas and build visualization! To encourage you to pass this section, I want to point out that you will run across a small amount of theory but a significant amount of practice!

Explore the Data Set

Confidence Interval

Calculating Confidence Interval with Python

Confidence Interval Width Quiz

Calculate 95% Confidence Interval

Advanced Confidence Interval Calculation with Python

Match the Functions

6. Statistical Testing

An inseparable part of a data analyst's life is conducting hypothesis testing. After completing this section, you will understand the idea behind testing in statistics and will be able to conduct a t-test using Python.

t-test Mathematically

One-Tailed And Two-Tailed Test

t-test Assumptions

Performing a t-test in Python

Conduct a t-test

Course Content

Learning Statistics with Python

Learning Statistics with Python

1. Basic Concepts

Sample vs Population Types of Statistics Types of Data Mean Value Median Value Median Value of the Even Number of Values Mean or Median Mode Value Descriptive Statistics Quiz

2. Mean, Median and Mode with Python

Examine the Dataset Calculating Mean and Median Values with Python Statistics with pandas Calculate the Mean and Median Salary

3. Variance and Standard Deviation

Population Variance Sample Variance Calculate Variance with Python Standard deviation Standard Deviation with Python Calculating Variance and Standard Deviation

4. Covariance vs Correlation

Covariance Correlation Select All Possible Values Calculate Covariance and Correlation

5. Confidence Interval

Explore the Data Set Confidence Interval Calculating Confidence Interval with Python Confidence Interval Width Quiz Calculate 95% Confidence Interval Advanced Confidence Interval Calculation with Python Match the Functions

6. Statistical Testing

What is t-test Hypotheses t-test Mathematically One-Tailed And Two-Tailed Test t-test Assumptions Performing a t-test in Python Conduct a t-test Paired t-test

Calculate Variance with Python

Calculating Variance with NumPy

In NumPy, you need to input the sequence of values (in our case, the column of the dataset) into the np.var() function, like this: np.var(df['work_year']).

Calculating Variance with pandas

In pandas, you should use the .var() method on the sequence of values (in our case, the column of the dataset), like this: df['work_year'].var().

In both cases, the results are almost the same. The differences are due to different denominators: N in NumPy, and N-1 in pandas. Check it now!


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import pandas as pd
import numpy as np

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a849660e-ddfa-4033-80a6-94a1b7772e23/update/ds_salaries_statistics', index_col = 0)

# Calculate the variance using the function from the NumPy library
var_1 = np.var(df['salary_in_usd'])
# Calculate the variance using the function from the pandas library
var_2 = df['salary_in_usd'].var()

print('The variace using NumPy library is', var_1)
print('The variace using pandas library is', var_2)

Everything was clear?

Section 3. Chapter 3

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