Page Index - quanganh2001/Google-Data-Analytics-Professional-Certificate-Coursera GitHub Wiki

215 page(s) in this GitHub Wiki:

• Home
• 1.2.0.Thinking analytically
• 1.2.1.Embracing Data Analyst Skills
• 1.2.2.Thinking about Analytical Thinking
• 1.2.3.Thinking about outcomes
• 1.2.4.Weekly challenge 2
• 1.3.0.The wonderful world of data
• 1.3.1.Follow the data life cycle
• 1.3.2.Outlining the data analysis process
• 1.3.3.The data analysis toolbox
• 1.3.4.Weekly challenge 3
• 1.4.0.Set up your toolbox
• 1.4.1.Mastering spreadsheet basics
• 1.4.2.Structured Query Language (SQL)
• 1.4.3.Data visualization
• 1.4.4.Weekly challenge 4
• 1.4.5.Video quiz
• 1.5.0.Endless career possibilities
• 1.5.1.Data analyst job opportunities
• 1.5.2.The importance of fair business decisions
• 1.5.3.Optional: Exploring your next job
• 1.5.4.Weekly challenge 5
• 1.5.5.Course challenge
• 1.5.6.Video quiz
• 2.1.0.Effective questions
• 2.1.1.Problem solving and effective questionning
• 2.1.2.Take action with data
• 2.1.3.Solve problems with data
• 2.1.4.Craft effective questions
• 2.1.5.Weekly challenge 1
• 2.1.6.Video quiz
• 2.2.0.Data driven decisions
• 2.2.1.Understand the power of data
• 2.2.2.Follow thje evidence
• 2.2.3.Connecting the data dots
• 2.2.4.Weekly challenge 2
• 2.2.5.Video quiz
• 2.3.0.More spreadsheet basics
• 2.3.1.Working with spreadsheets
• 2.3.2.Formulas in spreadsheets
• 2.3.3.Functions in spreadsheets
• 2.3.4.Save time with structured thinking
• 2.3.5.Weekly challenge 3
• 2.3.6.Video quiz
• 2.4.0.Always remember the stakeholder
• 2.4.1.Balance team and stakeholder needs
• 2.4.2.Communication is key
• 2.4.3.Amazing teamwork
• 2.4.4.Weekly challenge 4
• 2.4.5.Course challenge
• 2.4.6.Video quiz
• 3.1.0.Data types and structures
• 3.1.1.Data exploration
• 3.1.2.Collecting data
• 3.1.3.Differentiate between data formats and structures
• 3.1.4.Explore data types, fields, and values
• 3.1.5.Weekly challenge
• 3.1.6.Video quiz
• 3.2.0.Bias, credibility, privacy, ethics, and access
• 3.2.1.Unbiased and objective data
• 3.2.2.Explore data credibility
• 3.2.3.Data ethics and privacy
• 3.2.4.Understanding open data
• 3.2.5.Weekly challenge 2
• 3.2.6.Video quiz
• 3.3.0.Databases: Where data lives
• 3.3.1.Working with databases
• 3.3.2.Managing data with metadata
• 3.3.3.Accessing different data sources
• 3.3.4.Sorting and filtering
• 3.3.5.Working with large datasets in SQL
• 3.3.6.Weekly challenge 3
• 3.3.7.Video quiz
• 3.4.0.Organizing and protecting your data
• 3.4.1.Effectively organize data
• 3.4.2.Securing data
• 3.4.3.Weekly challenge 4
• 3.5.0.Optional: Engaging in the data community
• 3.5.1.Create or enhance your online presence
• 3.5.2.Build a data analytics network
• 3.5.3.Course challenge
• 3.5.4.Video quiz
• 4.1.0.The importance of integrity
• 4.1.1.Focus on integrity
• 4.1.2.Data integrity and analytics objectives
• 4.1.3.Overcoming the challenges of insufficient data
• 4.1.4.Testing your data
• 4.1.5.Consider the margin of error
• 4.1.6.Weekly challenge 1
• 4.1.7.Video quiz
• 4.2.0.Sparkling clean data
• 4.2.1.Data cleaning is a must
• 4.2.2.Begin cleaning data
• 4.2.3.Cleaning data in spreadsheets
• 4.2.4.Weekly challenge 2
• 4.2.5.Video quiz
• 4.3.0.Cleaning data with SQL
• 4.3.1.Using SQL to clean data
• 4.3.2.Learn basic SQL queries
• 4.3.4.Weekly challenge 3
• 4.3.5.Video quiz
• 4.4.0.Verify and report on your cleaning reports
• 4.4.1.Manually cleaning data
• 4.4.2.Documenting results and the cleaning process
• 4.4.3.Weekly challenge 4
• 4.4.4.Video quiz
• 4.5.0.Optional: Adding data to your resume
• 4.5.1.The data analyst hiring process
• 4.5.2.Understand the elements of a data analyst resume
• 4.5.3.Highlighting experiences on resumes
• 4.5.4.Exploring areas of interest
• 4.5.6.Course challenge
• 5.1.0.Organizing data to begin analysis
• 5.1.1.Let's get organized
• 5.1.2.Data analytics basics
• 5.1.3.Organize data for analysis
• 5.1.4.Sort data in spreadsheets
• 5.1.5.Sort data using SQL
• 5.1.6.Weekly challenge 1
• 5.1.7.Video quiz
• 5.2.0.Formatting and adjusting data
• 5.2.1.Convert and format data
• 5.2.2.Combine multiple datasets
• 5.2.3.Get support during analysis
• 5.2.4.Weekly challenge 2
• 5.2.5.Video quiz
• 5.3.0.Aggregating data for analysis
• 5.3.1.VLOOKUP for data aggregation
• 5.3.2.Use JOINS to aggregate data in SQL
• 5.3.3.Work with subqueries
• 5.3.4.Weekly challenge 3
• 5.3.5.Video quiz
• 5.4.0.Performing data calculations
• 5.4.1.Get started with data calculations
• 5.4.2.Pivot...Pivot...Pivot
• 5.4.3.Learn more SQL calculations
• 5.4.4.The data validation process
• 5.4.5.Using SQL with temporary tables
• 5.4.6.Weekly challenge 4
• 5.4.7.Course challenge
• 5.4.8.Video quiz
• 6.1.0.Visualizing data
• 6.1.1.Communicating your data insights
• 6.1.2.Understand data visualization
• 6.1.3.Design data visualizations
• 6.1.4.Explore visualization considerations
• 6.1.5.Weekly challenge 1
• 6.1.6.Video quiz
• 6.2.0.Creating data visualizations with Tableau
• 6.2.1.Get started with Tableau
• 6.2.2.Create visualizations in Tableau
• 6.2.3.Optional: Work with multiple data sources
• 6.2.4.Weekly challenge 2
• 6.2.5.Video quiz
• 6.3.0.Crafting data stores
• 6.3.1.Use data to develop stories
• 6.3.2.Use Tableau dashboards
• 6.3.3.Sharing data stories
• 6.3.4.Weekly challenge 3
• 6.3.5.Video quiz
• 6.4.0.Developing presentations and slideshows
• 6.4.1.The art and science of an effective presentation
• 6.4.2.Identify presentation skills and practices
• 6.4.3.Caveats and limitations to data
• 6.4.4.Listen, respond, and include
• 6.4.5.Weekly challenge 4
• 6.4.6.Course wake‐up
• 6.4.7.Video quiz
• 6.5.0.Documentation and reports
• 6.5.6.Video quiz
• 7.1.0.Programming and data analytics
• 7.1.1.The exciting world of programming
• 7.1.2.Programming as a data analyst
• 7.1.3.Learn programming using RStudio
• 7.1.4.Weeky challenge 1
• 7.1.5.Video quiz
• 7.2.0.Programming using RStudio
• 7.2.1.Understand basic programming concepts
• 7.2.2.Explore coding in R
• 7.2.3.Learning about R packages
• 7.2.4.Explore the tidyverse
• 7.2.5.Weekly challenge 2
• 7.2.6.Video quiz
• 7.3.0.Working with data in R
• 7.3.1.Explore data and R
• 7.3.2.Cleaning data
• 7.3.3.Take a closer look at the data
• 7.3.4.Weekly challenge 3
• 7.3.5.Video quiz
• 7.4.0.More about visualizations, aesthetics, and annotations
• 7.4.1.Create data visualization in R
• 7.4.2.Explore aesthetics in analysis
• 7.4.3.Annotate and save visualizations
• 7.4.4.Weekly challenge 4
• 7.4.5.Video quiz
• 7.5.0.Documentation and reports
• 7.5.1.Develop documentation and reports in RStudio
• 7.5.2.Create R Markdown documents
• 7.5.3.Understand code chunks and exports
• 7.5.4.Weekly challenge 5
• 7.5.5.Course wrap‐up
• 7.5.6.Video quiz
• Week 1: Get started Part 01. Program description and course syllabus
• Week 1: Get started Part 02. Learning Log: Think about data in daily life
• Week 1: Get started Part 03. Helpful resources to get started
• Week 1: Get started Part 04. Meet and greet
• Week 1: Get started Part 05. Deciding if you should take the speed track
• Week 1: Get started Part 06. Diagnostic Quiz
• Week 1: Glossary: Terms and definitions
• Week 1: Transforming data into insights Part 01. Case study: New data perspectives
• Week 1: Transforming data into insights Part 02. Learning Log: Consider how data analysts approach tasks
• Week 1: Understanding the data ecosystem Part 01. Data and gut instinct
• Week 1: Understanding the data ecosystem Part 02. Origins of the data analysis process
• Week 1: Understanding the data ecosystem Part 03. Test your knowledge on the data ecosystem
• Weekly challenge 1