[Coursera] Course 1. Foundations: Data, Data, Everywhere

WEEK 1 - Introducing data analytics

[Definition]

-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 analytics: The science of data

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

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

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

 

[Essential Questions]

-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?

 

[Data Analysis Life Cycle]

1. Ask: Business Challenge/Objective/Question -Ask effective questions -Define the problem -Use structured thinking -Communicate with others	2. Prepare: Data generation, collection, storage, and data management -Uderstand how data is generated and collected -Identify and use different data formats, types, and structures -Make sure data is unbiased and credible -Organize and protect data
3. Process: Data cleaning/data integrity -Create and transform data -Maintain data integrity -Test data -Clean data	4. Analyze: Data exploration, visualization, and analysis -Use tools to format and transform data -Sort and fiilter data -Identify patterns and draw conclusions -Make predictions and recommendations -Make data-driven decisions
5. Share: Communicating and interpreting results -Understand visualization -Create effective visuals -Bring data to life -Use data storytelling -Communicate to help others understand results	6. Act: Putting your insights to work to solve the problem -Apply your insights -Solve problems -Make decisions -Create something new

 

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WEEK 2 - All about analytical thinking

[Definition]

-Analytical skills: Qualities and characteristics associated with solving problems using facts

 1. Curiosity: seek out new challenges and experiences

 2. Uderstanding context: context = the condition in which something exists or happens 

 3. Having a technical mindset: the ability to break things down into smaller steps or pieces and work with them in an                                                           orderly and logical way

 4. Data design: how you organize information

 5. Data strategy: the management of the people, 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
1. Visualization
2. Strategy 
3. Problem-orientation
4. Correlation (*Correlation does not equal causation)
5. Big-picture and detail-oriented thinking

 

-Root cause: The reason why a problem occurs

-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

 

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WEEK 3 - The wonderful world of data

[Definition]

-Database: A collection of data stored in a computer system

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

-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

 

[Data life cycle]

1. Plan: decide what kind of data is needed, how it will be managed, and who will be responsible for it

2. Capture: collect or bring in data from a variety of different sources

3. Manage: determining how and where it is stored and the tools used to do so

4. Analyze: use the data to solve problems, make decisions, and support business goals

5. Archive: keep relevant data stored for long-term and future reference

6. Destroy: remove data from storage and delete any shared copies of the data

 

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WEEK4 - Set up your toolbox

[Definition]

-Attribute: A characteristic or quality of data used to label a column in a table

                ( = column name, column label, header, header row)

-Observation: All of the attributes for something contained in a row of a data table

 

[SQL Tutorial]

https://www.w3schools.com/sql/default.asp

SQL Tutorial

 

[Steps to plan a data visualization]

1. Explore the data for patterns

2. Plan your visuals

3. Create your visuals

    Line charts can track sales over time

    Bar charts can compare total visitors and visitors that make a purchase

    Donut charts can show customer segments

    Maps can connect sales to locations

   

[Tableau]

https://public.tableau.com/app/resources/learn

Free Data Visualization Software | Tableau Public

https://public.tableau.com/app/discover/viz-of-the-day

Viz of the Day

 

[RStudio]

https://posit.co/resources/cheatsheets/

Posit

https://posit.cloud/learn/primers/3

Posit Cloud - Do, share, teach, and learn data science

 

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WEEK5 - Endless career possibilities

[Review]

Ask an interesting question and define a problem to solve through data analysis to answer that question.

Think deeply about what data you would need and how to collect it in order to prepare for anaylsis.

Process data by organizing and structuring it in a table.

Analyze data by inspecting and scanning it for patterns.

Share the results with a visualization.

Act : reflect on the results, make decisions, and gain insight into the problem

 

[Definition]

-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

-Fairness: Ensuring that your analysis doesn't create or reinforce bias

 

[TED video]

Beyound the Numbers: A Data Analyst Journey

https://www.youtube.com/watch?v=t2oOFs4WgI0 

 

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