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

Contenido del Curso

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

Examine the Dataset

In this section, we will analyze a sample of IT specialists' salaries. Let's first take a look at the first five observations of the dataset:

	work_year	experience_level	job_title	salary	salary_currency	salary_in_usd	company_location	company_size
0	2020	MI	Data Scientist	70000	EUR	79833	DE	L
1	2020	SE	Machine Learning Scientist	260000	USD	260000	JP	S
2	2020	SE	Big Data Engineer	85000	GBP	109024	GB	M
3	2020	MI	Product Data Analyst	20000	USD	20000	HN	S
4	2020	SE	Machine Learning Engineer	150000	USD	150000	US	L

work_year - this year, the salary was paid;
experience_level - the experience level: EN is Entery-level, MI is Mid-level, SE-Senior-level, EX is Executive level;
job_title - the name of a job;
salary - the value of the salary;
salary_currency - the currency of the salary;
salary_in_usd - the value of the salary in USD;
company_location - the location of the company;
company_size - the size of the company: S-Small, M-Medium, L-Large.

Now, let's review the data types in statistics, and afterward, you'll match each column name with its respective type.

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Sección 2. Capítulo 1

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