What is data science and advanced data analytics?

<text variant="body1">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. <text variant="body1">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.

What do data professionals do?

<text variant="body1">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. <text variant="body1">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.

Why start a career in data science or advanced data analytics?

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.

Which jobs will this certificate help me prepare for?

The Google Advanced Data Analytics Certificate on Coursera is designed to prepare learners for roles as entry-level data scientists and advanced-level data analysts.

What tools and platforms are taught in the curriculum?

During this certificate program, you’ll gain knowledge of tools and platforms like Jupyter Notebook, Kaggle, Python, Stack Overflow, and Tableau.

Why enroll in the Google Advanced Data Analytics Certificate?

You’ll learn job-ready skills through interactive content — like activities, quizzes, and discussion prompts — in under six months, with less than 10 hours of flexible study a week. Along the way, you’ll work through a curriculum designed by Google employees who work in the field, with input from top employers and industry leaders. You’ll even have the opportunity to complete end-of-course projects and a final capstone project that you can share with potential employers to showcase your data analysis skills. After you’ve graduated from the program, you’ll have access to career resources and be connected directly with employers hiring for open entry-level roles in data science and advanced roles in data analytics.

Do I need to take the course in a certain order?

We highly recommend completing the seven courses in the order presented because the content in each course builds on information covered in earlier lessons.

When will I have access to the lectures and assignments?

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

What will I get if I subscribe to this Certificate?

When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Go Beyond the Numbers: Translate Data into Insights

This course is part of Google Advanced Data Analytics Professional Certificate

Instructor: Google Career Certificates

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5 modules

Gain insight into a topic and learn the fundamentals.

913 reviews

Advanced level

Designed for those already in the industry

Flexible schedule

3 weeks at 10 hours a week

Learn at your own pace

97%

Most learners liked this course

5 modules

Gain insight into a topic and learn the fundamentals.

913 reviews

Advanced level

Designed for those already in the industry

Flexible schedule

3 weeks at 10 hours a week

Learn at your own pace

97%

Most learners liked this course

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

Tools you'll learn

Python Programming

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

21 assignments

Taught in English

91%

of learners achieved a positive career outcome

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

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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

There are 5 modules in this course

This is the second course 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 eight 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

Module details

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 assignments2 plugins

8 videosTotal 37 minutes

Introduction to Course 25 minutes
Robb: Obstacles and achievements4 minutes
Welcome to module 11 minute
Find stories using the six exploratory data analysis practices 10 minutes
Benj: Data science and storytelling3 minutes
Combine PACE and EDA practices7 minutes
PACE with data visualizations5 minutes
Wrap-up3 minutes

5 readingsTotal 34 minutes

Course 2 overview8 minutes
Helpful resources and tips8 minutes
Case study: Deloitte 8 minutes
Reference guide: The EDA process8 minutes
Glossary terms from module 12 minutes

3 assignmentsTotal 62 minutes

Test your knowledge: Tell stories with data6 minutes
Test your knowledge: How PACE informs EDA and data visualizations6 minutes
Module 1 challenge50 minutes

2 pluginsTotal 20 minutes

Categorize: EDA best practices10 minutes
[Turkish learners ONLY] Categorize: EDA best practices - Türkçe10 minutes

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 assignments7 ungraded labs2 plugins

9 videosTotal 70 minutes

Welcome to module 23 minutes
Yaser: Understand data to drive value2 minutes
Where the data comes from11 minutes
EDA using basic data functions with Python10 minutes
Discover what is missing from your dataset6 minutes
Date string manipulations with Python14 minutes
Use structuring methods to establish order in your dataset5 minutes
EDA structuring with Python16 minutes
Wrap-up3 minutes

6 readingsTotal 38 minutes

Reference guide: Import datasets with Python8 minutes
Reference guide: Pandas methods for the discovery of a dataset8 minutes
Reference guide: Datetime manipulation8 minutes
Reference guide: Pandas tools for structuring a dataset8 minutes
Histograms2 minutes
Glossary terms from module 24 minutes

4 assignmentsTotal 70 minutes

Test your knowledge: Discovering is the beginning of an investigation8 minutes
Test your knowledge: Understand data format6 minutes
Test your knowledge: Create structure from raw data6 minutes
Module 2 challenge50 minutes

7 ungraded labsTotal 220 minutes

Annotated follow-along resource: EDA using basic functions with Python20 minutes
Activity: Discover what is in your dataset60 minutes
Exemplar: Discover what is in your dataset20 minutes
Annotated follow-along guide: Date string manipulations with Python20 minutes
Annotated follow-along guide: EDA structuring with Python20 minutes
Activity: Structure your data60 minutes
Exemplar: Structure your data20 minutes

2 pluginsTotal 20 minutes

Categorize: Structuring methods10 minutes
[Turkish learners ONLY] Categorize: Structuring methods - Türkçe10 minutes

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 assignments5 ungraded labs2 plugins

11 videosTotal 78 minutes

Welcome to module 34 minutes
Methods for handling missing data 8 minutes
Work with missing data in a Python notebook12 minutes
Remy: A day in the life of a data professional3 minutes
Account for outliers6 minutes
Identify and deal with outliers in Python14 minutes
Sort numbers versus names5 minutes
Label encoding in Python9 minutes
The value of input validation7 minutes
Input validation with Python8 minutes
Wrap-up2 minutes

6 readingsTotal 44 minutes

Data deduplication with Python8 minutes
Protect the people behind the data8 minutes
Reference guide: How to handle outliers8 minutes
Other approaches to data transformation8 minutes
Reference guide: Data cleaning in Python 8 minutes
Glossary terms from module 34 minutes

5 assignmentsTotal 76 minutes

Test your knowledge: The challenge of missing or duplicate data8 minutes
Test your knowledge: The ins and outs of data outliers6 minutes
Test your knowledge: Changing categorical data to numerical data6 minutes
Test your knowledge: Input validation6 minutes
Module 3 challenge50 minutes

5 ungraded labsTotal 180 minutes

Annotated follow-along guide: Work with missing data in a Python notebook20 minutes
Activity: Address missing data60 minutes
Exemplar: Address missing data20 minutes
Activity: Validate and clean your data60 minutes
Exemplar: Validate and clean your data20 minutes

2 pluginsTotal 20 minutes

Identify: Python functions for cleaning data10 minutes
[Turkish learners ONLY] Identify: Python functions for cleaning data - Türkçe10 minutes

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 assignments2 plugins

8 videosTotal 41 minutes

Welcome to module 43 minutes
The visualization life cycle5 minutes
Work with Tableau, Part 17 minutes
Work with Tableau, Part 27 minutes
Drew: Explore the possibilities of data3 minutes
Craft compelling stories with Tableau9 minutes
Present like a pro with Tableau6 minutes
Wrap-up1 minute

11 readingsTotal 64 minutes

Tableau Public overview8 minutes
How to sign on to Tableau Public 8 minutes
Download your datasets and begin presenting with Tableau 4 minutes
Follow-along guide: Work with Tableau, Part 14 minutes
Follow-along guide: Work with Tableau, Part 28 minutes
Activity Exemplar: Design a bar graph that tells a story in Tableau Public4 minutes
Follow-along guide: Craft compelling stories with Tableau8 minutes
The top five data visualization resources8 minutes
Follow-along guide: Present like a pro with Tableau4 minutes
Activity Exemplar: Build an interactive dashboard in Tableau Public4 minutes
Glossary terms from module 44 minutes

5 assignmentsTotal 120 minutes

Test your knowledge: Present a story4 minutes
Activity: Design a bar graph that tells a story in Tableau Public30 minutes
Activity: Build an interactive dashboard in Tableau Public30 minutes
Test your knowledge: Advanced Tableau6 minutes
Module 4 challenge50 minutes

2 pluginsTotal 20 minutes

Identify: Compelling visualizations10 minutes
[Turkish learners ONLY] Identify: Compelling visualizations - Türkçe10 minutes

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 videos10 readings4 assignments6 ungraded labs

4 videosTotal 9 minutes

Welcome to module 53 minutes
Introduction to your Course 2 end-of-course portfolio project1 minute
End-of-course project wrap-up and tips for ongoing career success2 minutes
Course wrap-up3 minutes

10 readingsTotal 52 minutes

Explore your Course 2 workplace scenarios8 minutes
Course 2 end-of-course portfolio project overview: Automatidata8 minutes
Activity Exemplar: Create your Course 2 Automatidata project4 minutes
Course 2 end-of-course portfolio project overview: TikTok8 minutes
Activity Exemplar: Create your Course 2 TikTok project4 minutes
Course 2 end-of-course portfolio project overview: Waze8 minutes
Activity Exemplar: Create your Course 2 Waze project4 minutes
Course 2 glossary2 minutes
Reflect and connect with peers2 minutes
Get started on the next course4 minutes

4 assignmentsTotal 135 minutes

Activity: Create your Course 2 Automatidata project30 minutes
Activity: Create your Course 2 TikTok project30 minutes
Activity: Create your Course 2 Waze project30 minutes
Assess your Course 2 end-of-course project45 minutes

6 ungraded labsTotal 240 minutes

Activity: Course 2 Automatidata project lab60 minutes
Exemplar: Course 2 Automatidata project lab20 minutes
Activity: Course 2 TikTok project lab60 minutes
Exemplar: Course 2 TikTok project lab20 minutes
Activity: Course 2 Waze project lab60 minutes
Exemplar: Course 2 Waze project lab20 minutes

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Google Career Certificates

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Reviewed on Feb 11, 2024

Very well designed course for anyone having experience of any field willing to dive into data analytics.

Reviewed on Dec 19, 2023

The Course was very effective which increased my skills, knowledge and confidence level.

Reviewed on Aug 22, 2023

Very Helpful Course! The storytell methods described are really helpful to me. I have always had an issue with getting my point across but now I know where my problem was and have corrected it.

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