- Level Expert
- المدة 33 ساعات hours
- الطبع بواسطة Google
-
Offered by
عن
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 stringsالوحدات
Get started with the course
5
Videos
- Introduction to Course 3
- Robb: Obstacles and achievements
- Welcome to module 1
- Find stories using the six exploratory data analysis practices
- Benj: Data science and storytelling
3
Readings
- Helpful resources and tips
- Course 3 overview
- Case study: Deloitte
1
Quiz
- Test your knowledge: Tell stories with data
Use PACE to inform EDA and data visualizations
1
Assignment
- Test your knowledge: How PACE informs EDA and data visualizations
2
Videos
- Combine PACE and EDA practices
- PACE with data visualizations
1
Readings
- Reference guide: The EDA process
Review: Find and share stories using data
1
Videos
- Wrap-up
1
Readings
- Glossary terms from module 1
1
Quiz
- Module 1 challenge
"Discovering" is the beginning of an investigation
3
Labs
- Annotated follow-along resource: EDA using basic functions with Python
- Activity: Discover what is in your dataset
- Exemplar: Discover what is in your dataset
4
Videos
- Welcome to module 2
- Yaser: Understand data to drive value
- Where the data comes from
- EDA using basic data functions with Python
2
Readings
- Reference guide: Import datasets with Python
- Reference guide: Pandas methods for the discovery of a dataset
1
Quiz
- Test your knowledge: Discovering is the beginning of an investigation
Understand data format
1
Labs
- Annotated follow-along guide: Date string manipulations with Python
2
Videos
- Discover what is missing from your dataset
- Date string manipulations with Python
1
Readings
- Reference guide: Datetime manipulation
1
Quiz
- Test your knowledge: Understand data format
Create structure from raw data
3
Labs
- Annotated follow-along guide: EDA structuring with Python
- Activity: Structure your data
- Exemplar: Structure your data
2
Videos
- Use structuring methods to establish order in your dataset
- EDA structuring with Python
2
Readings
- Reference guide: Pandas tools for structuring a dataset
- Histograms
1
Quiz
- Test your knowledge: Create structure from raw data
Review: Explore raw data
1
Videos
- Wrap-up
1
Readings
- Glossary terms from module 2
1
Quiz
- Module 2 challenge
The challenge of missing or duplicate data
3
Labs
- Annotated follow-along guide: Work with missing data in a Python notebook
- Activity: Address missing data
- Exemplar: Address missing data
4
Videos
- Welcome to module 3
- Methods for handling missing data
- Work with missing data in a Python notebook
- Remy: A day in the life of a data professional
1
Readings
- Data deduplication with Python
1
Quiz
- Test your knowledge: The challenge of missing or duplicate data
The ins and outs of data outliers
2
Videos
- Account for outliers
- Identify and deal with outliers in Python
2
Readings
- Protect the people behind the data
- Reference guide: How to handle outliers
1
Quiz
- Test your knowledge: The ins and outs of data outliers
Change categorical data to numerical data
2
Videos
- Sort numbers versus names
- Label encoding in Python
2
Readings
- Other approaches to data transformation
- Reference guide: Data cleaning in Python
1
Quiz
- Test your knowledge: Changing categorical data to numerical data
Input validation
2
Labs
- Activity: Validate and clean your data
- Exemplar: Validate and clean your data
2
Videos
- The value of input validation
- Input validation with Python
1
Quiz
- Test your knowledge: Input validation
Review: Clean your data
1
Videos
- Wrap-up
1
Readings
- Glossary terms from module 3
1
Quiz
- Module 3 challenge
Present a story
3
Videos
- Welcome to module 4
- The visualization life cycle
- Work with Tableau, Part 1
4
Readings
- Tableau Public overview
- How to sign on to Tableau Public
- Download your datasets and begin presenting with Tableau
- Follow-along guide: Work with Tableau, Part 1
1
Quiz
- Test your knowledge: Present a story
Advanced Tableau
4
Videos
- Work with Tableau, Part 2
- Drew: Explore the possibilities of data
- Craft compelling stories with Tableau
- Present like a pro with Tableau
6
Readings
- 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
3
Quiz
- 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
Review: Data visualizations and presentations
1
Videos
- Wrap-up
1
Readings
- Glossary terms from module 4
1
Quiz
- Module 4 challenge
Apply your skills to a workplace scenario
2
Videos
- Welcome to module 5
- Introduction to your Course 3 end-of-course portfolio project
1
Readings
- Explore your Course 3 workplace scenarios
Automatidata scenario
2
Labs
- Activity: Course 3 Automatidata project lab
- Exemplar: Course 3 Automatidata project lab
2
Readings
- Course 3 end-of-course portfolio project overview: Automatidata
- Activity Exemplar: Create your Course 3 Automatidata project
1
Quiz
- Activity: Create your Course 3 Automatidata project
TikTok scenario
2
Labs
- Activity: Course 3 TikTok project lab
- Exemplar: Course 3 TikTok project lab
2
Readings
- Course 3 end-of-course portfolio project overview: TikTok
- Activity Exemplar: Create your Course 3 TikTok project
1
Quiz
- Activity: Create your Course 3 TikTok project
Waze scenario
2
Labs
- Activity: Course 3 Waze project lab
- Exemplar: Course 3 Waze project lab
2
Readings
- Course 3 end-of-course portfolio project overview: Waze
- Activity Exemplar: Create your Course 3 Waze project
1
Quiz
- Activity: Create your Course 3 Waze project
End-of-course portfolio project wrap-up
1
Videos
- End-of-course project wrap-up and tips for ongoing career success
1
Readings
- Course 3 glossary
1
Quiz
- Assess your Course 3 end-of-course project
Course review: Go Beyond the Numbers: Translate Data into Insights
1
Videos
- Course wrap-up
2
Readings
- Reflect and connect with peers
- Get started on the next course
Auto Summary
"Go Beyond the Numbers: Translate Data into Insights" is an advanced data analytics course by Google on Coursera, designed for those with foundational knowledge in data science. Guided by Google employees, you'll master storytelling with data, exploratory data analysis, and creating impactful visualizations using Python and Tableau. This 1980-minute expert-level course offers hands-on activities and prepares you for data science careers. Subscription options include Starter and Professional. Ideal for those aiming to refine their data analytics skills and communicate insights effectively.

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