[Google Data Analytics Professional Certificate] Foundations: Data, Data, Everywhere

[ The six phases of data analysis ] 

1. Ask : Business Challenge/Objective/Question
2. Prepare : Data generation, collection, storage, and data management
3. Process : Data cleaning/data integrity - what type of data we have, missing data, wrong data collection? 
4. Analyze : Data exploration, visualization, and analysis => should be unbiased! look for the patterns 
5. Share : Communicating and interpreting results 
6. Act : Putting your insights to work to solve the problem => make data-driven decision!!! 

 

[ Project-based data analytics life cycle ]

A project-based data analytics life cycle has five simple steps:

1. Identifying the problem
2. Designing data requirements
3. Pre-processing data
4. Performing data analysis
5. Visualizing data

 

[ Data ecosystems ] 

: made up of various elements that interact with one another in order to produce, manage, store,organize, analyze, and share data.

- 'Cloud' is the big part of the data ecosystem ( cloud : keep data online, access via internet, virtual location )  

 

[ Common Misconception between Data Scientist vs Data Analyst ] 

* Data Scientist

: Creating new ways of modeling and understanding the unknown by using raw data 

=> Creating new questions 

 

* Data Analyst  

: doing the data-driven decision-making

=> Answer the existing questions 

 

Data Analysis vs Data Analytics 

Data Analysis : the collection, transformation, and organization of data in order to draw conclusions, make predictions, and drive informed decision-making 

 

Data Analytics : the science of data (very braod concept!!! ) 

 

[ The five key aspects to analytical thinking ] 

1. Visualization

2. Strategy

3. Problem-orientation

4. Correlation

5. Big-picture and detail-oriented thinking 

 

[ Stages of Data life Cycle ]

 

1.  Planning 

2. Capture data 

3. Manage

4. Analyze (support company's goal)

5. Archive

6. Destroy 

 

1. Figure out what data they need and where they can get it.
2. Collect the data, and be sure of what they will (and won’t) use it for.
3. Consider how to secure the data and deal with old data that is no longer useful.

 

 

 

 

 

 

 

 

 

< Glossary > 

Data : a collection of facts 

Dataset: A collection of data that can be manipulated or analyzed as one unit

A technical mindset : The analytical skill that involves breaking processes down into smaller steps and working with them in an orderly, logical way

Data design : The analytical skill that involves how you organize information

Understanding context : The analytical skill that has to do with how you group things into categories

Data strategy : The analytical skill that involves managing the processes and tools used in data analysis 

Analytical skills: Qualities and characteristics associated with using facts to solve problems

Analytical thinking: The process of identifying and defining a problem, then solving it by using
data in an organized, step-by-step manner

Data strategy: The management of the people, processes, and tools used in data analysis

Root cause: The reason why a problem occurs

Technical mindset: The ability to break things down into smaller steps or pieces and work with
them in an orderly and logical way

Stakeholders : Peeple who have invested time and resources into a project and are interested in the outcome

Gap analysis: A method for examining and evaluating the current state of a process in order to
identify opportunities for improvement in the future