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Google Cloud Training
This is a self-paced lab that takes place in the Google Cloud console. Google Cloud Dataprep is an intelligent data service for visually exploring, cleaning, and preparing data for analysis. Watch the short video Dataprep: Qwik Start - Qwiklabs Preview.
Register for this course and see more details by visiting:
OpenCourser.com/course/y27p1o/dataprep
What's inside
Syllabus
Dataprep: Qwik Start
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Strengthens an existing foundation for intermediate learners
Develops professional skills or deep expertise in a particular topic or set of topics
Builds a strong foundation for beginners
If this course is multi-modal and includes a mix of media, such as videos, readings, discussions, etc
Explores x, which is standard in industry y
Save this course
Save Dataprep: Qwik Start to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Dataprep: Qwik Start with these
activities:
Review Intro to DataPrep
Show steps
Complete this self-paced tutorial to build a foundation of knowledge for data preparation using Google Cloud Dataprep.
Browse courses on
Google Cloud Dataprep
Show steps
-
Watch the video
-
Complete the practice exercises
Join a Data Preparation Study Group
Show steps
Enhance your learning through collaboration by joining a study group, discussing course materials, sharing insights, and completing exercises together.
Show steps
-
Find or create a study group with fellow learners
-
Set regular meeting times
-
Divide responsibilities for preparing and leading discussions
Practice Data Cleaning Exercises
Show steps
Find and complete online data cleaning exercises to enhance your skills in this area.
Browse courses on
Data Cleaning
Show steps
-
Find online resources or platforms that offer data cleaning exercises
-
Select exercises that align with your skill level and course topics
-
Complete the exercises and review your results
Two other activities
Expand to see all activities and additional details
Show all five activities
Discuss Dataprep Use Cases
Show steps
Compose a blog post or article about effective use cases of Dataprep for different industries and sectors.
Show steps
-
Research different industries and sectors that use Dataprep
-
Interview experts or case studies to gather insights
-
Write and publish your article
Contribute to the Dataprep Open Source Community
Show steps
Identify an area in Dataprep's open-source codebase and contribute to its development by submitting bug fixes, feature enhancements, or documentation improvements.
Browse courses on
Open-Source Software
Show steps
-
Familiarize yourself with Dataprep's open-source codebase
-
Identify an area where you can contribute
-
Fork the codebase and make your changes
-
Submit a pull request for review
Review Intro to DataPrep
Show steps
Complete this self-paced tutorial to build a foundation of knowledge for data preparation using Google Cloud Dataprep.
Browse courses on
Google Cloud Dataprep
Show steps
Show steps
Show steps
- Watch the video
- Complete the practice exercises
Join a Data Preparation Study Group
Show steps
Enhance your learning through collaboration by joining a study group, discussing course materials, sharing insights, and completing exercises together.
Show steps
Show steps
Show steps
- Find or create a study group with fellow learners
- Set regular meeting times
- Divide responsibilities for preparing and leading discussions
Practice Data Cleaning Exercises
Show steps
Find and complete online data cleaning exercises to enhance your skills in this area.
Browse courses on
Data Cleaning
Show steps
Show steps
Show steps
- Find online resources or platforms that offer data cleaning exercises
- Select exercises that align with your skill level and course topics
- Complete the exercises and review your results
Two other activities
Expand to see all activities and additional details
Show all five activities
Discuss Dataprep Use Cases
Show steps
Compose a blog post or article about effective use cases of Dataprep for different industries and sectors.
Show steps
Show steps
Show steps
- Research different industries and sectors that use Dataprep
- Interview experts or case studies to gather insights
- Write and publish your article
Contribute to the Dataprep Open Source Community
Show steps
Identify an area in Dataprep's open-source codebase and contribute to its development by submitting bug fixes, feature enhancements, or documentation improvements.
Browse courses on
Open-Source Software
Show steps
Show steps
Show steps
- Familiarize yourself with Dataprep's open-source codebase
- Identify an area where you can contribute
- Fork the codebase and make your changes
- Submit a pull request for review
Career center
Learners who complete Dataprep: Qwik Start will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists work with large sets of data to find trends and patterns and help businesses understand how to use them. This course can help build a foundation for understanding data sets that could be useful as you develop a career as a Data Scientist. It may introduce you to tooling, methods, and techniques that will help you perform the work of a data scientist.
Data Engineer
Data Engineer
Data Engineers are responsible for building, maintaining, and managing big data systems. They must be able to transform raw data into formats that are easy to analyze. This course may help you learn how to extract, clean, and transform data - foundational skills for being a Data Engineer. Experience with data preparation and analysis are valuable to those working as a Data Engineer.
Data Analyst
Data Analyst
A Data Analyst will help businesses use their information to turn it into actionable insights. This course may be useful in learning how to spot trends and patterns in data. Familiarity with identifying, cleaning, and then preparing data for analysis are all skills that can help a Data Analyst succeed. The course also may introduce you to ways to make sense of large data sets, which is a key part of being an effective Data Analyst.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts take large data sets and use them to create statistical models, using these models to assess risk or predict outcomes. This course can provide a foundation for Quantitative Analysts, by introducing them to data cleaning, transformation, and analysis. Understanding how to prepare and analyze data for your models can be valuable for success in this career.
Data Visualization Analyst
Data Visualization Analyst
Data Visualization Analysts help to make sense of data by visually representing it. This course may help you with the preparation and exploration of data, which are foundational skills for being an effective Data Visualization Analyst. Understanding how to find patterns and trends, and communicate them to others, is important for a Data Visualization Analyst to succeed.
Data Architect
Data Architect
Data Architects design data management solutions to make data accessible to different business units within an organization. This course may introduce you to foundational topics in data management, including data preparation. Data Architects should also have a good understanding of data management best practices, data quality, data security, and data governance.
Statistician
Statistician
Statisticians use mathematical and statistical theory to collect, analyze, interpret, and present data. This course may help you develop foundational skills necessary to be a Statistician, such as cleaning and preparing data for analysis. This skill will help you to find trends and patterns in data, which will help in interpreting and presenting data as part of your work.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers turn machine learning models into actual products. They work with big data to inform decisions, predict outcomes, and make recommendations. This course will not directly teach machine learning, but its focus on getting data ready for analysis can be useful for those who want to work as a Machine Learning Engineer. Understanding how to clean and prepare data for analysis is foundational for machine learning.
Business Analyst
Business Analyst
Business Analysts offer solutions to business problems by using data analysis and visualization. They must be able to interpret data and communicate insights to improve business processes. This course may help Business Analysts understand how to translate raw data into insights that can transform a business.
Actuary
Actuary
Actuaries use statistical and mathematical models to assess risk. They may work with insurance companies, banks, or other financial institutions. This course can be useful for Actuaries by introducing them to working with large data sets, identifying trends, and understanding how to prepare and analyze data.
Market Researcher
Market Researcher
Market Researchers conduct qualitative and quantitative research to help businesses understand their customers and make strategic decisions. This course will not provide direct training in those areas, but its focus on working with data, preparing it, and analyzing it, will help Market Researchers understand how to use data to better understand their customers.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to understand trends in the economy and make investment recommendations. While this course does not provide specific training in finance or economics, it may help Financial Analysts develop skills in data analysis, data cleaning, and working with large data sets. These are all skills Financial Analysts use on a daily basis.
Data Management Analyst
Data Management Analyst
Data Management Analysts are responsible for developing, implementing, and managing data policies and procedures. They may also be responsible for ensuring that data is accurate, consistent, and accessible. This course will not provide thorough training for all aspects of data management, but its focus on data analysis and cleaning can provide a foundation for understanding data management.
Health Data Analyst
Health Data Analyst
Health Data Analysts use their statistical skills to work with data related to health and medicine. They may work with large data sets to analyze trends and patterns, or they may help develop new ways to prevent and treat illnesses. This course may provide a foundation in data analysis and data cleaning that can be used in a career as a Health Data Analyst. This course may aid in identifying trends and patterns in health data.
Sports Data Analyst
Sports Data Analyst
Sports Data Analysts use their statistical skills to analyze sports data and trends. They may work with sports teams or athletes to help them improve their performance. This course will not provide specific training in sports analysis, but its focus on data cleaning and analysis may provide a foundation that can be used in sports data analysis.
For more career information including salaries, visit:
OpenCourser.com/course/y27p1o/dataprep
Reading list
We've selected six books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Dataprep: Qwik Start.
Provides a comprehensive overview of data science, from the basics of data manipulation and visualization to more advanced topics such as machine learning and deep learning. It great resource for those who want to learn about data science from scratch.
Provides a comprehensive overview of deep learning techniques using Python. It covers the entire deep learning pipeline, from model design and training to model evaluation and deployment.
Provides a comprehensive overview of machine learning techniques using Python. It covers the entire machine learning pipeline, from data preparation and feature engineering to model training and evaluation.
Provides a comprehensive overview of artificial intelligence techniques using Python. It covers a wide range of topics, from natural language processing and computer vision to machine learning and deep learning.
Provides a comprehensive overview of natural language processing techniques using Python. It covers a wide range of topics, from text preprocessing and feature engineering to machine learning and deep learning for NLP.
Provides a comprehensive guide to data manipulation using Pandas, a popular Python library for data analysis. It covers both the basics of data manipulation and more advanced techniques such as data cleaning, transformation, and aggregation.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/y27p1o/dataprep
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Effort
1 hour
Level
Introductory
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
English
Good to know
Strengthens an existing foundation for intermediate learners
Develops professional skills or deep expertise in a particular topic or set of topics
Builds a strong foundation for beginners
If this course is multi-modal and includes a mix of media, such as videos, readings, discussions, etc
Explores x, which is standard in industry y
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