What is the Google Data Analytics certification?

Looking to know what is the Google Data Analytics certification all about? The Google Data Analytics Professional Certificate Program can take you from zero to entry-level data analyst superhero in about six months of studying at a pace of about 10 hours per week. The course costs US$39 per month by subscription on Coursera. There are no additional costs for textbooks or software. No degrees or previous experience is necessary for enrollment.

You may also be interested in Five core skill areas of a data analyst.

Data analysts prepare, process, and analyze data to help inform business decisions. They create visualizations to share their findings with stakeholders and provide recommendations driven by data.

“Generally, a data analyst will work with a data engineer to turn … raw data into actionable pipelines.” Data analysts provide clean and actionable data. Then data scientists work “to turn it into really cool machine learning models or statistical inferences that are just well beyond anything you could have ever imagined.”

There are 337,400 U.S. job openings in data analytics with a $67,900 average entry-level salary.

Junior data analyst
Associate data analyst
Junior data scientist
Finance analyst
Operations analyst
Data technician
Business performance analyst
Marketing analyst
Business intelligence analyst
Healthcare analyst

Over 8 courses, gain in-demand skills that prepare you for an entry-level job. You’ll learn from Google employees whose foundations in data analytics served as launchpads for their own careers.

Curriculum overview

Start here: https://grow.google/dataanalytics

There are 8 Courses in this Professional Certificate

https://www.coursera.org/professional-certificates/google-data-analytics?#courses

Course 1: Foundations: Data, Data, Everywhere

By the end of this course, you will:

Gain an understanding of the practices and processes used by a junior or associate data analyst in their day-to-day job.
Learn about key analytical skills (data cleaning, data analysis, data visualization) and tools (spreadsheets, SQL, R programming, Tableau) that you can add to your professional toolbox.
Discover a wide variety of terms and concepts relevant to the role of a junior data analyst, such as the data life cycle and the data analysis process.
Evaluate the role of analytics in the data ecosystem.
Conduct an analytical thinking self-assessment.
Explore job opportunities available to you upon program completion, and learn about best practices in the job search.

Course 2: Ask Questions to Make Data-Driven Decisions

By the end of this course, you will:

Learn about effective questioning techniques that can help guide analysis.
Gain an understanding of data-driven decision-making and how data analysts present findings.
Explore a variety of real-world business scenarios to support an understanding of questioning and decision-making.
Discover how and why spreadsheets are an important tool for data analysts.
Examine the key ideas associated with structured thinking and how they can help analysts better understand problems and develop solutions.
Learn strategies for managing the expectations of stakeholders while establishing clear communication with a data analytics team to achieve business objectives.

Course 3: Prepare Data For Exploration

By the end of this course, you will:

Find out how analysts decide which data to collect for analysis.
Learn about structured and unstructured data, data types, and data formats.
Discover how to identify different types of bias in data to help ensure data credibility.
Explore how analysts use spreadsheets and SQL with databases and data sets.
Examine open data and the relationship between and importance of data ethics and data privacy.
Gain an understanding of how to access databases and extract, filter, and sort the data they contain.
Learn the best practices for organizing data and keeping it secure.

Course 4: Process Data from Dirty to Clean

By the end of this course, you will be able to do the following:

Learn how to check for data integrity.
Discover data cleaning techniques using spreadsheets.
Develop basic SQL queries for use on databases.
Apply basic SQL functions for cleaning and transforming data.
Gain an understanding of how to verify the results of cleaning data.
Explore the elements and importance of data cleaning reports.

Course 5: Analyze Data to Answer Questions

<You’ll take what you’ve learned to this point and apply it to your analysis to make sense of the data you’ve collected. You’ll learn how to organize and format your data using spreadsheets and SQL to help you look at and think about your data in different ways. You’ll also find out how to perform complex calculations on your data to complete business objectives. You’ll learn how to use formulas, functions, and SQL queries as you conduct your analysis.>

By the end of this course, you will:

Learn how to organize data for analysis.
Discover the processes for formatting and adjusting data.
Gain an understanding of how to aggregate data in spreadsheets and by using SQL.
Use formulas and functions in spreadsheets for data calculations.
Learn how to complete calculations using SQL queries.

Course 6: Share Data Through the Art of Visualization

By the end of this course, you will:

Examine the importance of data visualization.
Learn how to form a compelling narrative through data stories.
Gain an understanding of how to use Tableau to create dashboards and dashboard filters.
Discover how to use Tableau to create effective visualizations.
Explore the principles and practices involved with effective presentations.
Learn how to consider potential limitations associated with the data in your presentations.
Understand how to apply best practices to a Q&A with your audience.

Course 7: Data Analysis with R Programming

<you’ll learn about the programming language known as R. You’ll find out how to use RStudio, the environment that allows you to work with R. This course will also cover the software applications and tools that are unique to R, such as R packages. You’ll discover how R lets you clean, organize, analyze, visualize, and report data in new and more powerful ways.>

By the end of this course, you will:

Examine the benefits of using the R programming language.
Discover how to use RStudio to apply R to your analysis.
Explore the fundamental concepts associated with programming in R.
Explore the contents and components of R packages including the Tidyverse package.
Gain an understanding of dataframes and their use in R.
Discover the options for generating visualizations in R.
Learn about R Markdown for documenting R programming.

Course 8: Data Analytics Capstone Project: Complete a Case Study

<You’ll have the opportunity to complete an optional case study, which will help prepare you for the data analytics job hunt. Case studies are commonly used by employers to assess analytical skills. For your case study, you’ll choose an analytics-based scenario. You’ll then ask questions, prepare, process, analyze, visualize and act on the data from the scenario. You’ll also learn other useful job hunt skills through videos with common interview questions and responses, helpful materials to build a portfolio online, and more.>

By the end of this course, you will: