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Examine the Dataset | Mean, Median and Mode with Python
Learning Statistics with Python

Examine the DatasetExamine 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_yearexperience_leveljob_titlesalarysalary_currencysalary_in_usdcompany_locationcompany_size
02020MIData Scientist70000EUR79833DEL
12020SEMachine Learning Scientist260000USD260000JPS
22020SEBig Data Engineer85000GBP109024GBM
32020MIProduct Data Analyst20000USD20000HNS
42020SEMachine Learning Engineer150000USD150000USL
  • 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.

Types of Data
question-icon

Match the column name with its type.

work_year
experience_level

salary_currency

salary_in_usd

company_size

Click or drag`n`drop items and fill in the blanks

Everything was clear?

Section 2. Chapter 1
course content

Course Content

Learning Statistics with Python

Examine the DatasetExamine 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_yearexperience_leveljob_titlesalarysalary_currencysalary_in_usdcompany_locationcompany_size
02020MIData Scientist70000EUR79833DEL
12020SEMachine Learning Scientist260000USD260000JPS
22020SEBig Data Engineer85000GBP109024GBM
32020MIProduct Data Analyst20000USD20000HNS
42020SEMachine Learning Engineer150000USD150000USL
  • 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.

Types of Data
question-icon

Match the column name with its type.

work_year
experience_level

salary_currency

salary_in_usd

company_size

Click or drag`n`drop items and fill in the blanks

Everything was clear?

Section 2. Chapter 1
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