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C1 Foundations: Data, Data, Everywhere - Coggle Diagram
C1 Foundations: Data, Data, Everywhere
M1 Introducing Data Analytics and Analytical Thinking
1.1 Get started
Data
Collection of facts (or information).
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.
Find answers to existing questions by creating insights from data sources.
Asks
How do I define success for this project?
What kind of results are needed?
Who will be informed?
Am I answering the questions being asked?
How quickly does a decision need to be made?
What is the root cause of the problem?
Where are the gaps in our process?
What did we not consider before?
Data analytics
The science of data.
1.2 Transform data into insights
Data for business
Improve processes
Identify opportunities and trends
Launch new products
Serve customers
Make thoughtful decisions
Analysis
Turning data into insights
Data science
Discipline of making data useful.
Includes
Machine learning (and AI)
Automate many decisions under uncertainty.
Excellence: performance.
Statistics
Make few decisions under uncertainty.
Excellence: rigor.
Analytics
Not know how many decisions to make before to begin.
Looking for inspiration.
Encounter unknown unkowns.
Excellence: speed.
Creating new ways of modeling and understanding the unknown by using raw data.
1.3 Understanding the data ecosystem
Ecosystem
A group of elements that interact with one another.
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 scientist
Create new questions using data
Data analytics vs data analysis
The former encompasses the latter, and everything from the job of managing and using data to the tools and methods that data workers use every day.
Data-driven decision-making
Using facts to guide business strategy.
Steps
Figure out the business need, usually, this is the problem that needs to be solved.
Once the problem is defined, a data analyst finds data, analyzes it, and uses it to uncover trends, patterns, and relationships.
Subject matter experts
Look at the results of data analysis and identify any inconsistencies, make sense of gray areas, and validate choices being made.
1.4 Embrace your data analyst skills
Analytical skills
Qualities and characteristics associated with solving problems using facts.
Curiosity
Wanting to learn something
Understanding context
Listening and trying to understand the full picture.
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 to organize information
Data strategy
The management of people, processes and tools used in data analysis
People
Making sure people know how to use the right data to find solutions.
Processes
Making sure the path to that solution is clear and accessible
Tools
Making sure the right technology is being used for the job.
Context
The condition in which something exists or happens (structure or environment).
1.5 Analytical thinking for effective outcomes
Analytical thinking
Identifying and defining a problem and then solving by using data in an organized, step-by-step manner.
Key aspects
Visualization
Strategy
Problem-orientation
Correlation
Big-picture and detail-oriented thinking
Root cause
The reason why a problem occurs.
Five 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 to get where you want to be in the future.
M2. The Wonderful World of Data
2.1 Follow the data life cycle
Data life cycle
Plan
Decide what kind of data is needed, how it will be managed, and who will be responsible for it.
Capture
Collect r bring 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, make decisions, and support business goals.
Archive
Keep relevant data stored for long-term and future reference.
Destroy
Remove data from storage and delete any shared copies of the data.
Database
A collection of data stored in a computer system.
2.1 Outline the data analysis process
Stakeholders
People who have invested time and resources into a project and are interested in the outcome.
Data analysis process
Ask
Define the problem.
Look at the current state and identify how it is different from the ideal state.
Confirm stakeholder expectations.
Determine who the stakeholders are and developing strong communication strategies.
Prepare
Collect and store data for analysis
Process
Find and eliminate any errors and inaccuracies (data cleaning, transforming, combining, and outliers removing).
Analyze
Use tools to transform and organize information, draw conclusions, make predictions, and drive informed decision-making.
Tools: spreadsheets and SQL (structured query language).
Share
Interpret results and share them (visualization).
Act
Take the insights and put them to work to solve the original business problem.
Outliers
Any data points that could skew the information.
2.3 The data analysis toolbox
Data analysis most common tools
Spreadsheets
A digital worksheet. It stores, organizes, and sorts data.
Microsoft Excel.
Google Sheets.
Query languages for databases
A computer programming language that allows you to retrieve and manipulate data from a database.
SQL
MySQL
Microsoft SQL Server
BigQuery
Visualization tools
Data visualization: the graphical representation of information. Graphs, maps, and tables.
Tableau
Looker
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.
M3. Set up your data analytics toolbox
3.1 Mastering spreadsheet basics
Attribute
A characteristic or quality of data used to label a column in a table.
Headers,
Observation
All of the attributes for something contained in a row of a data table.
3.2 Get started with SQL and data visualizaton
SQL functions for data
Store.
Organize.
Analyze.
SQL databases
Oracle
MySQL
PostgreSQL
Microsoft SQL Server
Query
A request for data or information form a database.