Data Analitics Study Notes - Aleksandr Kosenko Digital (Page 2)

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Data Analitics Study Notes

Study notes from data analysis training on Google, IBM, Meta and Yandex educational programs.

Exploratory data analysis or EDA

Data Analitics Study Notes

Exploratory data analysis or EDA

Review * Practices of EDA * PACE workflow * Ethics of working with data * Data visualizations Exploratory data analysis (EDA) The process of investigating, organizing, and analyzing datasets and summarizing their main characteristics, often employing data wrangling and visualization methods. EDA is an important first step in understanding any new dataset or question.

Format and adjust data

Data Analitics Study Notes

Format and adjust data

Check and change data type Check your data for inconsistent units of measurement to prevent problems during data analysis. Convert temperatures from Fahrenheit to Celsius Use the CONVERT function to change units of measurement. Convert function formula as =CONVERT(CELL, from Fahrenheit to Celsius. Enter "F", "C&

Organize data for analysis

Data Analitics Study Notes

Organize data for analysis

The analysis process Analysis – The process used to make sense of the data collected The goal of analysis is to identify trends and relationships within data so you can accurately answer the question you're asking The 4 phases of analysis 1. Organize data 2. Format and adjust data

Document the cleaning process

Data Analitics Study Notes

Document the cleaning process

Documentation The process of tracking changes, additions, deletions, and errors involved in your data-cleaning effort * Recover data-cleaning errors * Inform other users of changes * Determine quality of data A changelog, a file containing a chronologically ordered list of modifications made to a project, is typically utilized to track these changes. 1.

Manually cleaning data

Data Analitics Study Notes

Manually cleaning data

Verify and report results 1. Verification of data is crucial to ensure the data cleaning process has been executed correctly and the data is accurate and reliable. 2. Verification allows for the identification and rectification of errors before beginning analysis, ensuring the insights gained can be trusted for decision-making. 3.