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.
Go Beyond the Numbers: Translate Data into Insights
Go Beyond the Numbers: Translate Data into Insights
This course is part of Google Advanced Data Analytics Professional Certificate
Taught in English
Instructor: Google Career Certificates
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Course
Gain insight into a topic and learn the fundamentals
4.8
(487 reviews)
|
97%
Advanced level
Designed for those already in the industry
32 hours (approximately)
Flexible schedule
Learn at your own pace
What you'll learn
Apply the exploratory data analysis (EDA) process
Explore the benefits of structuring and cleaning data
Investigate raw data using Python
Create data visualizations using Tableau
Skills you'll gain
Details to know
Shareable certificate
Add to your LinkedIn profile
Assessments
21 quizzes
Course
Gain insight into a topic and learn the fundamentals
4.8
(487 reviews)
|
97%
Advanced level
Designed for those already in the industry
32 hours (approximately)
Flexible schedule
Learn at your own pace
See how employees at top companies are mastering in-demand skills
Build your Data Analysis expertise
This course is part of the Google Advanced Data Analytics Professional Certificate
When you enroll in this course, you'll also be enrolled in this Professional Certificate.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate from Google
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance review
There are 5 modules in this course
You’ll learn how to find the stories within data and share them with your audience. You’ll learn about the methods and benefits of data cleaning and how it can help you discover those stories. You’ll also go over the steps of the EDA process and learn how EDA can help you quickly understand data. Finally, you'll explore different ways to visualize data to communicate key insights.
What's included
8 videos5 readings3 quizzes1 plugin
Finding stories in data using EDA is all about organizing and interpreting raw data. Python can help you do this quickly and effectively. You’ll learn how to use Python to perform the EDA practices of discovering and sculpting.
What's included
9 videos6 readings4 quizzes7 ungraded labs1 plugin
You’ll explore three more EDA practices: cleaning, joining, and validating. You'll discover the importance of these practices for data analysis, and you’ll use Python to clean, validate, and join data.
What's included
11 videos6 readings5 quizzes1 discussion prompt5 ungraded labs1 plugin
You’ll practice creating and presenting data stories in an ethical, accessible, and professional way. You'll also explore advanced data visualization techniques in Tableau.
What's included
8 videos11 readings5 quizzes1 discussion prompt1 plugin
In this end-of-course project, you’ll practice using Python to perform EDA on a workplace scenario dataset. Then, you'll use Python and Tableau to visualize the data.
What's included
4 videos9 readings4 quizzes2 discussion prompts6 ungraded labs
Instructor
Instructor ratings
4.8 (129 ratings)Offered by
Recommended if you're interested in Data Analysis
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Frequently asked questions
Organizations of all types and sizes have business processes that generate massive volumes of data. Every moment, all sorts of information gets created by computers, the internet, phones, texts, streaming video, photographs, sensors, and much more. In the global digital landscape, data is increasingly imprecise, chaotic, and unstructured. As the speed and variety of data increases exponentially, organizations are struggling to keep pace.
Data science and advanced data analytics are part of a field of study that uses raw data to create new ways of modeling and understanding the unknown. To gain insights, businesses rely on data professionals to acquire, organize, and interpret data, which helps inform internal projects and processes. Data scientists and advanced data analysts rely on a combination of critical skills, including statistics, scientific methods, data analysis, and artificial intelligence.
A data professional is a term used to describe any individual who works with data and/or has data skills. At a minimum, a data professional is capable of exploring, cleaning, selecting, analyzing, and visualizing data. They may also be comfortable with writing code and have some familiarity with the techniques used by statisticians and machine learning engineers, including building models, developing algorithmic thinking, and building machine learning models.
Data professionals are responsible for collecting, analyzing, and interpreting large amounts of data within a variety of different organizations. The role of a data professional is defined differently across companies. Generally speaking, data professionals possess technical and strategic capabilities that require more advanced analytical skills such as data manipulation, experimental design, predictive modeling, and machine learning. They perform a variety of tasks related to gathering, structuring, interpreting, monitoring, and reporting data in accessible formats, enabling stakeholders to understand and use data effectively. Ultimately, the work of data professionals helps organizations make informed, ethical decisions.
Large volumes of data — and the technology needed to manage and analyze it — are becoming increasingly accessible. Because of this, there has been a surge in career opportunities for people who can tell stories using data, such as senior data analysts and data scientists. These professionals collect, analyze, and interpret large amounts of data within a variety of different organizations. Their responsibilities require advanced analytical skills such as data manipulation, experimental design, predictive modeling, and machine learning.