Go Beyond the Numbers: Translate Data into Insights
- Offered byCoursera
Go Beyond the Numbers: Translate Data into Insights at Coursera Overview
Duration | 28 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Advanced |
Official Website | Explore Free Course  |
Credential | Certificate |
Go Beyond the Numbers: Translate Data into Insights at Coursera Highlights
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Coursera Labs Includes hands on learning projects. Learn more about Coursera Labs External Link
- Advanced Level
- Approx. 28 hours to complete
- English Subtitles: English
Go Beyond the Numbers: Translate Data into Insights at Coursera Course details
- This is the third of seven courses in the Google Advanced Data Analytics Certificate. In this course, you’ll learn how to find the story within data and tell that story in a compelling way. You'll discover how data professionals use storytelling to better understand their data and communicate key insights to teammates and stakeholders. You'll also practice exploratory data analysis and learn how to create effective data visualizations.
- Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you build your data analytics skills to prepare for your career.
- Learners who complete the seven courses in this program will have the skills needed to apply for data science and advanced data analytics jobs. This certificate assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate.
- By the end of this course, you will:
- -Use Python tools to examine raw data structure and format
- -Select relevant Python libraries to clean raw data
- -Demonstrate how to transform categorical data into numerical data with Python
- -Utilize input validation skills to validate a dataset with Python
- -Identify techniques for creating accessible data visualizations with Tableau
- -Determine decisions about missing data and outliers
- -Structure and organize data by manipulating date strings
Go Beyond the Numbers: Translate Data into Insights at Coursera Curriculum
Find and share stories using data
Introduction to Course 3
Robb: Obstacles and achievements
Welcome to week 1
Find stories using the six exploratory data analysis practices
Benj: Data science and storytelling
Combine PACE and EDA practices
PACE with data visualizations
Wrap-up
Helpful resources and tips
Course 3 overview
Case study: Deloitte
Reference guide: The EDA process
Glossary terms from week 1
Test your knowledge: Tell stories with data
Test your knowledge: How PACE informs EDA and data visualizations
Weekly challenge 1
Explore raw data
Welcome to week 2
Yaser: Understand data to drive value
Where the data comes from
EDA using basic data functions with Python
Discover what is missing from your dataset
Date string manipulations with Python
Use structuring methods to establish order in your dataset
EDA structuring with Python
Wrap-up
Reference guide: Import datasets with Python
Reference guide: Pandas methods for the discovery of a dataset
Follow-along instructions: EDA using basic data functions with Python
Reference guide: Datetime manipulation
Follow-along instructions: Date string manipulations with Python
Reference guide: Pandas tools for structuring a dataset
Follow-along instructions: EDA structuring with Python
Glossary terms from week 2
Test your knowledge: Discovering is the beginning of an investigation
Test your knowledge: Understand data format
Test your knowledge: Create structure from raw data
Weekly challenge 2
Clean your data
Welcome to week 3
Handle missing data with Python
Work with missing data in a Python notebook
Remy: A day in the life of a data professional
Account for outliers
Identify and deal with outliers in Python
Sort numbers versus names
Label encoding in Python
The value of input validation
Input validation with Python
Wrap-up
Data deduplication with Python
Follow-along instructions: Work with missing data in a Python notebook
Protect the people behind the data
Reference guide: How to handle outliers
Other approaches to data transformation
Reference guide: Data cleaning in Python
Glossary terms from week 3
Test your knowledge: The challenge of missing or duplicate data
Test your knowledge: The ins and outs of data outliers
Test your knowledge: Changing categorical data to numerical data
Test your knowledge: Input validation
Weekly challenge 3
Data visualizations and presentations
Welcome to week 4
The visualization life cycle
Work with Tableau, Part 1
Work with Tableau, Part 2
Drew: Explore the possibilities of data
Craft compelling stories with Tableau
Present like a pro with Tableau
Wrap-up
Tableau Public overview
Follow-along instructions: Data visualizations and presentations with Tableau
Follow-along guide: Work with Tableau, Part 1
How to sign on to Tableau Public
Follow-along guide: Work with Tableau, Part 2
Activity Exemplar: Design a bar graph that tells a story in Tableau Public
Follow-along guide: Craft compelling stories with Tableau
The top five data visualization resources
Follow-along guide: Present like a pro with Tableau
Activity Exemplar: Build an interactive dashboard in Tableau Public
Glossary terms from week 4
Test your knowledge: Present a story
Activity: Design a bar graph that tells a story in Tableau Public
Activity: Build an interactive dashboard in Tableau Public
Test your knowledge: Advanced Tableau
Weekly challenge 4
Course 3 end-of-course project
Welcome to week 5
Introduction to Course 3 end-of-course portfolio project
End-of-course project wrap-up and tips for ongoing career success
Course wrap-up
Course 3 end-of-course portfolio project overview: Automatidata
Activity Exemplar: Create your Course 3 Automatidata project
Course 3 glossary
Get started on the next course
Activity: Create your Course 3 Automatidata project
Assess your Course 3 end-of-course project
Go Beyond the Numbers: Translate Data into Insights at Coursera Admission Process
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