Google Data Analytics Certificate Flashcards
Data
A Collection of Facts
Data Analysis
The collection, transformation, and organization of data in order to draw conclusions, make predictions, and drive informed decision making.
Data Analyst
Someone who collects, transforms, and organizes data in order to help make informed decisions.
Sherlock Holmes via Sir arthur Conan Doyle
Data! Data! Data!… I can’t make bricks without clay!
Data Analytics
The Science of data
Program Features
1) Video vignettes
2) Data Journal
3) Readings
4) Activities
5) Discussion Prompts
Course-1 Foundations
What you will learn:
1) Real-Life roles and responsibilities of a junior data analyst.
2) How businesses transform data into actionable insights.
3) Spreadsheet basics
4) Database and query basics.
5) Data visualization basics.
Skill sets you will build:
1) Using data in everyday life
2) Thinking analytically
3) Applying tools from the data analytics toolkit
4) Showing trends and patterns with data visualizations.
5) Ensuring you data analysis is fair.
Course-2 Ask
What you will learn:
1) How data analysts solve problems with data.
2) The use of analytics for making data-driven decisions.
3) Spreadsheet formulas and functions.
4) Dashboard basics, including an introduction to tableau.
5) Data reporting basics
Skills set you will build:
1) Asking SMART and effective questions.
2) Structuring how you think
3) Summarizing data
4)Putting things into context
5) Managing team and stakeholder expectations.
6) Problem-solving and conflict-resolution.
Course-3 Prepare
What you will learn:
1) How data is generated
2) Features of different data types, fields, and values.
3) Database structures
4) The function of metadata in data analytics
5) Structured Query Language(SQL) functions
Skill sets you will build:
1) Ensuring ethical data analysis practices.
2) Addressing issues of bias and credibility.
3) Accessing databases and importing data.
4) Writing simple queries
5) Organizing and protecting data.
Course-4 Process
What you will learn:
1) Data integrity and the importance of clean data.
2) The tools and processes used by data analysts to clean data.
3) Data-cleaning verification and reports.
4) Statistics, hypothesis testing, and margin of error.
5) Resume building and interpretation of job postings (optional)
Skill sets you will build:
1) Connecting business objectives to data analysis.
2) Identify clean and dirty data.
3) Cleaning small datasets using spreadsheet tools.
4) Cleaning large datasets by writing SQL queries.
5) Documenting data- Cleansing processes.
Course-5 Analyze
What you will learn:
1) Steps data analysts take to organize data.
2) How to combine data from multiple sources.
3) Spreadsheet calculations and pivot tables.
4) SQL calculations
5) Temporary tables
6) Data validation
Skills sets you will Build:
1) Sorting data in spreadsheets and by writing SQL queries.
2) Filtering data in spreadsheets and by writing SQL queries.
3) Converting data
4) Formatting data
5) Substantiating data analysis processes
6) Seeking feedback and support from others during data analysis.
Course-6 Share
What you will learn:
1) Design thinking
2) How data analysts use visualizations to communicate about data.
3) The benefits of tableau for presenting data analysis findings.
4) Data-driven storytelling
5) Dashboards and dashboard filters.
6) Strategies for creating an effective data presentation.
Skills sets you will build:
1) Creating visualizations and dashboards in Tableau.
2) Addressing accessibility issues when communicating about data.
3) Understanding the purpose of different business communication tools.
4) Telling a data-driven story
5) Presenting to others about data.
6) Answering questions about data.
Course-7 Act
What you will learn:
1) Programming languages and environments.
2) R packages
3) R functions, variables, data types, pipes, and vectors
4) R data frames
5) Bias and credibility in R
6) R Visualization tools
7) R Markdown for documentation, creating structure, and emphasis.
Skill sets you will build:
1) Coding in R
2) Writing functions in R
3) Accessing data in R
4) Cleaning data In R.
5) Generating data visualizations in R.
6) Reporting on data analysis to stakeholders.
Course- 8 Capstone
What you will lean:
1) How a data analytics portfolio distinguishes you from other candidates.
2) Practical, real-world problem,-solving.
3)Strategies for extracting insights from data
4) Clear presentation of data findings.
5) Motivation and ability to take initiative.
Skills sets you will build:
1) Building a portfolio
2) Increasing your employability
3) Showcasing your data analytics knowledge, skill, and technical expertise.
4) Sharing your work during an interview
5) Communicating you unique value proposition to a potential employer.
Businesses use ways to control data
1) Improve processes
2) Identify opportunities and trends
3) Launch new products
4) Server customers
5 ) Make thoughtful decisions
Data ecosystems
The various elements that interact with one another in order to produce, manage, store, organize, analyze, and share data.
Cloud
A place to keep data online, rather than a computer hard drive.
Data Science
Creating new ways of modeling and understanding the unknown by using raw data.
Data analytics
The science of data
Data-Driven decision-making
Using facts to guide business strategy
Dataset
A collection of data that can be manipulated or analyzed as one unit.
Analytical skills
Qualities and characteristics associated with solving problems using facts.
Analytical skill set 5 Essential Points
1) Curiosity
2) Understanding context
3) Having a technical mindset
4) Data design
5) Data strategy
Context
The condition in which something exists or happens .
A technical mindset
The ability to break things down into smaller steps or pieces and work with them in an orderly and logical way.
Data Design
How you organize information
Data strategy
The Management of people, processes and tools used in data analysis.
Analytical skills
The qualities and characteristics associated with solving problems using facts.
A technical Mindset
The analytical skill that involves breaking processes down into smaller steps and working with them in an orderly and 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 thinking
Identifying and defining a problem and then solving it by using data in an organized step-by-step manner.
5 aspects of Analytical thinking
1) Visualization
2) Strategy
3)Problem- orientation
4) Correlation
5) Big-Picture and detail-oriented thinking
Visualization
The graphical representation of information
Correlation does not equal causation
Root cause
The reason why a problem occurs
The 5 whys
Ask. Why? Five times to reveal the root cause.
Gap analysis
A method for examining and evaluating how a process works currently in order in order to get where you want to be in the future.
Data-Driven decision-making
Using facts to guide business strategy
What is a quartile?
A quartile divides data points into four equal parts.
What is a quartile?
A quartile divides data points into four equal parts.
What are non-profits?
Non-Profits are organizations dedicated to advancing a social cause or advocating for a particular effort.
Life-cycle of data
1) Planning
2) Capture
3) Manage
4) Analyze
5) Archive
6) Destroy
Database
A collection of data stored in a computer system.
Archive
Storing data in place where its still available but might not be used again.
Plan
Decide what Kind of data is needed, how it will be managed, who will be responsible for it.
Capture
Collect or bring in data from a variety of different sources.
Manage
Care for and maintain the data. This includes determining how and where it is stored and the tools used to do so.
Analyze
Use the data to solve problems, and support business goals.
Archive
Keep relevant data stored for long-term and future reference.
Destory
Remove data from storage and delete any shared copies of the data.
Stakeholders
People who have invested time and resources into a project and are interested in the outcome.
Defining a Problem
Look at the current state and identify how its different from the ideal state.
Data Analyst Tools
1) Spreadsheets
2) Query language s for databases
3) Visualization tools
2 Popular spreadsheet options
1) Microsoft excel
2) Google sheets
Formula
A set of instructions that performs a specific calculation using the data in a spreadsheet.
Function
A Preset command that automatically performs a specific process or task using the data in a spreadsheet.
Query language
A computer programming language that allows you to retrieve and manipulate data from a database.
Structured query language
Data Visualization
The graphical representation of information.
Some Popular Visualization tools
1) Tableau
2) Looker
Attribute
A characteristic or quality of data used to label a column in a table.
Observation
All of the attributes for something contained in a row of a data table.
SQL
1) Store
2) Organize
3) Analyze
Examples Include:
1) Oracle
2) MY SQL
3) PostgreSQL
4) Microsoft SQL Server
Query
A request for data or information from a database.
Your data analysis tools (so far)
1) Spreadsheets
2) SQL
3) Data visualization tools
What you will learn?
1) Role of a data Analyst
2) Business tasks for data analysts
3) Fairness in analysis
4) Opportunities for you
5) And your future success!
Use of data analyst Skills in various industries
1) Technology
2) Marketing
3) Finance
4) Heath-care
Roles of a data analyst
1) Technology
2) Finance
3) Healthcare
4)Government
Roles of a data analyst
Use geographic data to power GPS technology in cars.
Issue
A topic or subject to investigate
Question
Designed to discover information
Problem
An obstacle or complication that needs to be worked out
Business task
The question or problem data analysis answers for a business.
Business task example
Analyze weather data from the last decade to identify predictable patterns
Fairness
Ensuring that your analysis doesn’t create or reinforce bias
Dream Job Factors
1) Industry
2) Tools
3) Location
4) Travel
5) Culture
Revenue data can used in 3 different ways.
1) Financial services
2) Telecom
3) Tech
