[Google Data Analytics Professional Certificate] Ask Questions to Make Data-Driven Decisions + Spread Sheet (Google|Excel)

[ Common Problem Types ]

1. Making predictions

2. Categorizing things - categorized by specific keyword or score

3. Spotting something unusal

4. Identifying themes - Grouping categorized info into broader concepts 

5. Discovering connections

6. Finding patterns - using historical data to understand what happened in the past and is therefore likely to happen                                          again 

 

 

[ SMART questions ]

=> kinds of questions we could ask : Leading Question, Closed-ended Question, Questions that are too vague and lack context (Not GOOD!)

Specific - Simple , Do NOT ask Closed-ended Question

 

[ Qualitative and quantitative data ]

 

Quantitative data : specific and objective measures of numerical facts

- how many? how often?

 

 

Qualitative data : subjective or explanatory measures of qualities and characteristics

- why ?

 

=> Qualitative data can help analysts better understand their quantitative data by providing a reason or more thorough explanation

 

 

[ Desigining Compelling Dashboard ]

 

[ Spread Sheet ] 

Organize the data

- Pivot table

=> Sort and filter 

 

Calculate the data 

- Formula : use operators(arithmetic) 

 

- Error (Formula) 

#DIV/0! : a formula is trying to divide a value in a cell by 0 or by an empty cell 

 => way to prevent : use IFERROR(operation ,"Not applicable" )

#N/A : data in a formula can't be found by the spreadsheet

#NAME? : The name of a function is misspelled

#NUM!: can't perform a formula calculation because a cell has an invalid numeric value

#VALUE! : a general error that could indicate a problem with a formula or referenced cells 

#REF! : A cell used in a formula was in a column that was deleted

 

- Function

 

 

[ The Importance of Context ] 

Context  - the condition in which something exists or happens

- Context can turn raw data into meaningful information

• Who: The person or organization that created, collected, and/or funded the data collection
• What: The things in the world that data could have an impact on
• Where: The origin of the data
• When: The time when the data was created or collected
• Why: The motivation behind the creation or collection
• How: The method used to create or collect it

=> By asking these 6 questions, we can reduce potential bias. 

 

- Understanding and including the context is important during each step of your analysis process !!! 

 

 

 

 

 

 

 

 

 

< Glossary >

Unfair question: A question that makes assumptions or is difficult to answer honestly

Relevant question: A question that has significance to the problem to be solved

Measurable question: A question whose answers can be quantified and assessed

Leading question: A question that steers people toward a certain response

Outliers  : outliers in data analysis are observations that significantly deviate from the normal pattern of the dataset and can have a notable impact on statistical analysis.

Commonality :  The commonality in data analysis refers to identifying patterns, trends, and relationships within the dataset to gain insights and make informed decisions.

Pivot table : a data summarization tool that is used in data processing. Pivot tables are used to summarize, sort, reorganize, group, count, total or average data stored in a database. 

Metric : single, quantifiable type of data that can be used for measurement 

Problem domain : the specific area of analysis that encompasses every activity affecting or affected by the problem