[Coursera] Foundations: Data, Data, Everywhere - Google (Week 1)

Week 1 강의 정리

 

Welcome to the Google Data Analytics Certificate

 

data: a collection of facts (that can be used to draw conclusions, make predictions, and assist in decision-making.)

data analyst: someone who collects, transforms, and organizes data in order to help make informed decisions.

 

The Google Data Analytics Certificate is split into courses based on different processes for data analysis:

Ask

Prepare 

Process

Analyze

Share

Act

 

Introduction to the course

 

<program features>

• video vignettes
• data journal
• readings
• activities
• discussion prompts

 

Transforming data into insights

 

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

 

Six phases of data analysis 

1. Ask
2. Prepare
3. Process
4. Analyze
5. Share
6. Act

The analysts asked questions to define both the issue to be solved and what would equal a successful result. 

 

Next, they prepared by building a timeline and collecting data with employee surveys that were designed to be inclusive.

 

They processed the data by cleaning it to make sure it was complete, correct, relevant, and free of errors and outliers. 

 

They analyzed the clean employee survey data. Then the analysts shared their findings and recommendations with team leaders. Afterward, leadership acted on the results and focused on improving key areas. 

Cassie: Dimensions of data analytics

 

“A data analyst is an explorer, a detective and an artist all rolled into one.”

What is the data ecosystem?

 

Data ecosystems: The various elements that interact with one another in order to produce, manage, store, organize, analyze, and share data

(These elements include hardware and software tools, as well as the people who use them.) 

 

Cloud: A place to keep data online, rather than a computer hard drive

 

Data scientists vs data analysts

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

 

Data scientists create new questions using data, while analysts find answers to existing questions by creating insights from data sources.

 

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

(when you think about data, data analysis and the data ecosystem, it's important to understand that all of these things fit under the data analytics umbrella)

How data informs better decisions

 

Data-driven decision-making: Using facts to guide business strategy

• The first step in data-driven decision-making: figuring out the business need.
• ex. a music or movie streaming service - they gather information, analyze it, then use the insights to make suggestions
• ex. rise of e-commerce - new business models that remove the physical store
• ex. scheduling a certain number of restaurant employees to work based on the average number of lunch-goers per day

 

Subject matter experts - people who are familiar with the business problem

• it’s important to include their insights

• they have the ability to look at the results of data analysis and identify any inconsistencies, make sense of gray areas, and eventually validate choices being made.

(reading) Data and gut instinct

 

Analysts use data-driven decision-making and follow a step-by-step process.

 

1. Ask questions and define the problem.
2. Prepare data by collecting and storing the information.
3. Process data by cleaning and checking the information.
4. Analyze data to find patterns, relationships, and trends.
5. Share data with your audience.
6. Act on the data and use the analysis results.

Gut instinct: an intuitive understanding of something with little or no explanation

If you ignore data by preferring to make decisions based on your own experience, your decisions may be biased. But even worse, decisions based on gut instinct without any data to back them up can cause mistakes. 

 

Data + business knowledge = mystery solved

try asking yourself these questions about a project to help find the perfect balance:

• How do I define success for this project?
• What kind of results are needed?
• Who will be informed?
• Am I answering the question being asked?
• How quickly does a decision need to be made?

 

Origins of the data analysis process

• data analysis is rooted in statistics

data analysis life cycle: the process of going from data to decision

Data goes through several phases as it gets created, consumed, tested, processed, and reused. 

There isn't a single defined structure of those phases, but there are some shared fundamentals in every data analysis process.

<Google Data Analytics Certificate>

1. Ask: Business Challenge/Objective/Question
2. Prepare: Data generation, collection, storage, and data management
3. Process: Data cleaning/data integrity
4. Analyze: Data exploration, visualization, and analysis
5. Share: Communicating and interpreting results 
6. Act:  Putting your insights to work to solve the problem

<EMC’s data analysis life cycle>

1. Discovery
2. Pre-processing data
3. Model planning
4. Model building
5. Communicate results
6. Operationalize

• reflects the cyclical nature of real-world projects
• each step connects and leads to the next, and eventually repeats
• Key questions help analysts test whether they have accomplished enough to move forward and ensure that teams have spent enough time on each of the phases and don’t start modeling before the data is ready

<SAS’s iterative life cycle>

- It can be used to produce repeatable, reliable, and predictive results

1. Ask
2. Prepare
3. Explore
4. Model
5. Implement
6. Act
7. Evaluate

• emphasizes the cyclical nature of their model by visualizing it as an infinity symbol
• it includes a step after the act phase designed to help analysts evaluate their solutions and potentially return to the ask phase again

<Project-based data analytics life cycle>

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

• X ‘Act phase’

<Big data analytics life cycle>

1. Business case evaluation
2. Data identification
3. Data acquisition and filtering
4. Data extraction
5. Data validation and cleaning
6. Data aggregation(종합) and representation
7. Data analysis
8. Data visualization
9. Utilization of analysis results

• they have broken down what we have been referring to as Prepare and Process into smaller steps

There are different ways to do things but the same core ideas still appear in each model of the process