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Mohamed Touiti
in 2006, the British mathematician Clive Humby coined the phrase "Data is the new Oil". This analogy has been proven correct as data powers entire industries nowadays but if left unrefined, is effectively worthless.
Read more
in 2006, the British mathematician Clive Humby coined the phrase "Data is the new Oil". This analogy has been proven correct as data powers entire industries nowadays but if left unrefined, is effectively worthless.
Read more
in 2006, the British mathematician Clive Humby coined the phrase "Data is the new Oil". This analogy has been proven correct as data powers entire industries nowadays but if left unrefined, is effectively worthless.
This 2.5 hours-long guided project is designed for business analysts & data engineers eager to learn how to Clean Messy Data in Snowflake Data Platform. By the end of the project, you will
-Be able to identify common data quality issues then use SQL String functions to remove unwanted characters and split rows into multiple columns.
-Extract dates from Text fields then use SQL date functions for comparisons and calculations.
-Identify and correct missing and duplicated data then answer business questions using SQL statements.
To achieve these objectives, we will work on a real example from the field, you will play the role of a Data Analyst in the marketing department, who has been tasked with answering a business question, but the customer data they have received presents several data quality challenges.
Note: To be successful in this project you need to have Snowflake beginner knowledge such as Creating a trial account, Databases, Tables, and Virtual Warehouses. If you are not familiar with Snowflake and want to learn the basics, start with my previous Guided Project: Snowflake for Beginners: Make your First Snowsight Dashboard which will give you basic knowledge about Snowflake and will teach you how to create your trial account.
Register for this course and see more details by visiting:
OpenCourser.com/course/jwtgrz/data
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What's inside
Syllabus
Project Overview
in 2006, the British mathematician Clive Humby coined the phrase "Data is the new Oil". This analogy has been proven correct as data powers entire industries nowadays but if left unrefined, is effectively worthless. This 2.4 hours-long guided project is designed for business analysts & data engineers eager to learn how to Clean Messy Data in Snowflake Data Platform. During the project, we will work on a real example from the field, you will play the role of a Data Analyst in the marketing department, who has been tasked with answering a business question, but the customer data they have received presents several data quality challenges. By the end of the project, you will Achieve the below Learning Objectives :
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches how to refine data, which is essential for business analysis
Uses SQL, which is widely used in the industry
Involves a hands-on approach to data cleaning
Provides a practical example from the marketing field
Assumes Snowflake beginner knowledge, which may limit accessibility for complete beginners
Save this course
Save Data Cleaning in Snowflake: Techniques to Clean Messy Data to your list so you can find it easily later:
Save
Save
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 Data Cleaning in Snowflake: Techniques to Clean Messy Data with these
activities:
Snowflake Data Cleaning Tutorial
Show steps
Follow a guided tutorial to learn best practices for data cleaning in Snowflake.
Show steps
-
Set up a Snowflake trial account
-
Follow the tutorial steps to clean a sample dataset
Review SQL
Show steps
Brush up on basic SQL concepts to strengthen your foundation for data cleaning.
Browse courses on
SQL
Show steps
-
Review data types, operators, and functions
-
Practice writing queries to select, filter, and sort data
Participate in peer-led data cleaning workshops
Show steps
Engage with fellow learners by participating in peer-led workshops focused on data cleaning, fostering collaboration and knowledge exchange.
Show steps
-
Join or organize a peer-led data cleaning workshop
-
Contribute your knowledge and experience to group discussions
-
Collaborate on data cleaning challenges and share solutions
-
Provide feedback and support to fellow learners
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Data Cleaning Exercises
Show steps
Engage in hands-on exercises to refine your data cleaning skills.
Show steps
-
Download and import a messy dataset
-
Identify and correct common data quality issues, such as missing values, duplicate rows, and inconsistent formats
-
Apply SQL string functions to remove unwanted characters and split rows
-
Extract dates from text fields and use SQL date functions for comparisons and calculations
Identify and correct missing and duplicated data
Show steps
Develop skills in identifying and resolving missing and duplicated data issues, crucial for ensuring data integrity.
Browse courses on
Data Quality
Show steps
-
Review data for missing values and identify patterns
-
Explore techniques for imputing missing values, such as mean, median, or mode
-
Identify and remove duplicate records based on specific criteria
-
Validate your results to ensure data accuracy and consistency
Data Cleaning Study Group
Show steps
Engage with peers to discuss data cleaning challenges and share best practices.
Show steps
-
Join or start a study group
-
Bring data cleaning questions and problems for discussion
-
Collaborate on finding solutions and refining your skills
Explore advanced SQL techniques for data cleaning
Show steps
Expand your knowledge of SQL by exploring advanced techniques specifically designed for data cleaning, enhancing your ability to handle complex datasets.
Browse courses on
Advanced SQL
Show steps
-
Review tutorials on window functions, user-defined functions, and regular expressions
-
Practice applying these techniques to real-world data cleaning scenarios
-
Experiment with different approaches to optimize your data cleaning process
Solve LeetCode problems related to data cleaning
Show steps
Enhance your problem-solving skills by applying data cleaning techniques to LeetCode problems, sharpening your proficiency in both areas.
Show steps
-
Identify LeetCode problems that involve data cleaning challenges
-
Analyze the problem statement and identify the data cleaning tasks required
-
Apply appropriate data cleaning techniques to solve the problem
-
Validate your solutions and optimize your code for efficiency
Data Cleaning Project
Show steps
Apply your skills to a real-world data cleaning project.
Show steps
-
Gather a messy dataset from a public source
-
Clean the data using the techniques learned in the course
-
Create a presentation or report to showcase your findings and insights
Create a data cleaning pipeline using SQL
Show steps
Apply your skills by designing and implementing a data cleaning pipeline using SQL, demonstrating your ability to handle real-world data challenges.
Browse courses on
SQL
Show steps
-
Define the data cleaning requirements and scope
-
Develop SQL queries to perform data cleaning tasks, such as removing duplicates, handling missing values, and transforming data
-
Create a data cleaning pipeline that automates the process
-
Test and validate the pipeline to ensure accuracy and efficiency
-
Document the pipeline and its implementation
Create a blog post or article on data cleaning best practices
Show steps
Share your knowledge and insights by creating a blog post or article outlining best practices for data cleaning, establishing yourself as an expert in the field.
Browse courses on
Data Cleaning
Show steps
-
Research and gather information on data cleaning best practices
-
Organize your content into a logical structure with an introduction, body, and conclusion
-
Write clear and concise content that is accessible to a target audience
-
Include examples and case studies to illustrate key points
-
Proofread and edit your content for clarity and accuracy
Contribute to open-source data cleaning tools
Show steps
Gain practical experience and contribute to the community by participating in open-source data cleaning projects, deepening your understanding and expanding your skillset.
Browse courses on
Open Source
Show steps
-
Identify open-source data cleaning tools and select one to contribute to
-
Review the project documentation and codebase
-
Identify areas where you can contribute, such as bug fixes, feature enhancements, or documentation improvements
-
Submit your contributions and participate in discussions
-
Collaborate with other contributors to enhance the project
Snowflake Data Cleaning Tutorial
Show steps
Follow a guided tutorial to learn best practices for data cleaning in Snowflake.
Show steps
Show steps
Show steps
- Set up a Snowflake trial account
- Follow the tutorial steps to clean a sample dataset
Review SQL
Show steps
Brush up on basic SQL concepts to strengthen your foundation for data cleaning.
Browse courses on
SQL
Show steps
Show steps
Show steps
- Review data types, operators, and functions
- Practice writing queries to select, filter, and sort data
Participate in peer-led data cleaning workshops
Show steps
Engage with fellow learners by participating in peer-led workshops focused on data cleaning, fostering collaboration and knowledge exchange.
Show steps
Show steps
Show steps
- Join or organize a peer-led data cleaning workshop
- Contribute your knowledge and experience to group discussions
- Collaborate on data cleaning challenges and share solutions
- Provide feedback and support to fellow learners
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Data Cleaning Exercises
Show steps
Engage in hands-on exercises to refine your data cleaning skills.
Show steps
Show steps
Show steps
- Download and import a messy dataset
- Identify and correct common data quality issues, such as missing values, duplicate rows, and inconsistent formats
- Apply SQL string functions to remove unwanted characters and split rows
- Extract dates from text fields and use SQL date functions for comparisons and calculations
Identify and correct missing and duplicated data
Show steps
Develop skills in identifying and resolving missing and duplicated data issues, crucial for ensuring data integrity.
Browse courses on
Data Quality
Show steps
Show steps
Show steps
- Review data for missing values and identify patterns
- Explore techniques for imputing missing values, such as mean, median, or mode
- Identify and remove duplicate records based on specific criteria
- Validate your results to ensure data accuracy and consistency
Data Cleaning Study Group
Show steps
Engage with peers to discuss data cleaning challenges and share best practices.
Show steps
Show steps
Show steps
- Join or start a study group
- Bring data cleaning questions and problems for discussion
- Collaborate on finding solutions and refining your skills
Explore advanced SQL techniques for data cleaning
Show steps
Expand your knowledge of SQL by exploring advanced techniques specifically designed for data cleaning, enhancing your ability to handle complex datasets.
Browse courses on
Advanced SQL
Show steps
Show steps
Show steps
- Review tutorials on window functions, user-defined functions, and regular expressions
- Practice applying these techniques to real-world data cleaning scenarios
- Experiment with different approaches to optimize your data cleaning process
Solve LeetCode problems related to data cleaning
Show steps
Enhance your problem-solving skills by applying data cleaning techniques to LeetCode problems, sharpening your proficiency in both areas.
Show steps
Show steps
Show steps
- Identify LeetCode problems that involve data cleaning challenges
- Analyze the problem statement and identify the data cleaning tasks required
- Apply appropriate data cleaning techniques to solve the problem
- Validate your solutions and optimize your code for efficiency
Data Cleaning Project
Show steps
Apply your skills to a real-world data cleaning project.
Show steps
Show steps
Show steps
- Gather a messy dataset from a public source
- Clean the data using the techniques learned in the course
- Create a presentation or report to showcase your findings and insights
Create a data cleaning pipeline using SQL
Show steps
Apply your skills by designing and implementing a data cleaning pipeline using SQL, demonstrating your ability to handle real-world data challenges.
Browse courses on
SQL
Show steps
Show steps
Show steps
- Define the data cleaning requirements and scope
- Develop SQL queries to perform data cleaning tasks, such as removing duplicates, handling missing values, and transforming data
- Create a data cleaning pipeline that automates the process
- Test and validate the pipeline to ensure accuracy and efficiency
- Document the pipeline and its implementation
Create a blog post or article on data cleaning best practices
Show steps
Share your knowledge and insights by creating a blog post or article outlining best practices for data cleaning, establishing yourself as an expert in the field.
Browse courses on
Data Cleaning
Show steps
Show steps
Show steps
- Research and gather information on data cleaning best practices
- Organize your content into a logical structure with an introduction, body, and conclusion
- Write clear and concise content that is accessible to a target audience
- Include examples and case studies to illustrate key points
- Proofread and edit your content for clarity and accuracy
Contribute to open-source data cleaning tools
Show steps
Gain practical experience and contribute to the community by participating in open-source data cleaning projects, deepening your understanding and expanding your skillset.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Identify open-source data cleaning tools and select one to contribute to
- Review the project documentation and codebase
- Identify areas where you can contribute, such as bug fixes, feature enhancements, or documentation improvements
- Submit your contributions and participate in discussions
- Collaborate with other contributors to enhance the project
Career center
Learners who complete Data Cleaning in Snowflake: Techniques to Clean Messy Data will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
As a Data Analyst, you will be responsible for cleaning and analyzing data to help businesses make better decisions. This course will provide you with the skills you need to identify common data quality issues and use SQL to clean and transform data. You will also learn how to extract dates from text fields, identify and correct missing and duplicated data, and answer business questions using SQL statements.
Data Engineer
Data Engineer
As a Data Engineer, you will be responsible for designing, building, and maintaining data pipelines. This course will provide you with the skills you need to clean and transform data, and load it into a data warehouse. You will also learn how to use SQL to analyze data and create reports.
Business Analyst
Business Analyst
As a Business Analyst, you will be responsible for understanding business requirements and translating them into technical specifications. This course will provide you with the skills you need to clean and analyze data, and use SQL to create reports and dashboards. You will also learn how to identify and solve business problems.
Data Scientist
Data Scientist
As a Data Scientist, you will be responsible for using data to solve business problems. This course will provide you with the skills you need to clean and analyze data, and use SQL to create models and predictions. You will also learn how to communicate your findings to business stakeholders.
Database Administrator
Database Administrator
As a Database Administrator, you will be responsible for managing and maintaining databases. This course will provide you with the skills you need to clean and optimize data, and use SQL to create and manage databases. You will also learn how to troubleshoot and resolve database issues.
Machine Learning Engineer
Machine Learning Engineer
As a Machine Learning Engineer, you will be responsible for building and deploying machine learning models. This course will provide you with the skills you need to clean and prepare data, and use SQL to create features and train models. You will also learn how to deploy models to production and monitor their performance.
Financial Analyst
Financial Analyst
As a Financial Analyst, you will be responsible for analyzing financial data to make investment recommendations. This course will provide you with the skills you need to clean and analyze financial data, and use SQL to create reports and dashboards. You will also learn how to use SQL to identify and evaluate investment opportunities.
Software Engineer
Software Engineer
As a Software Engineer, you will be responsible for designing, developing, and maintaining software applications. This course will provide you with the skills you need to clean and transform data, and use SQL to integrate data into software applications. You will also learn how to use SQL to create reports and dashboards.
Marketing Analyst
Marketing Analyst
As a Marketing Analyst, you will be responsible for analyzing marketing data to improve marketing campaigns. This course will provide you with the skills you need to clean and analyze marketing data, and use SQL to create reports and dashboards. You will also learn how to use SQL to identify and evaluate marketing opportunities.
Customer Success Manager
Customer Success Manager
As a Customer Success Manager, you will be responsible for ensuring that customers are satisfied with a company's products and services. This course will provide you with the skills you need to clean and analyze customer data to identify customer needs and improve customer satisfaction. You will also learn how to use SQL to create reports and dashboards to track customer satisfaction.
Sales Manager
Sales Manager
As a Sales Manager, you will be responsible for leading and managing a sales team. This course will provide you with the skills you need to clean and analyze sales data to identify opportunities and improve sales performance. You will also learn how to use SQL to create reports and dashboards to track sales performance.
Product Manager
Product Manager
As a Product Manager, you will be responsible for managing the development and launch of new products. This course will provide you with the skills you need to clean and analyze data to identify customer needs and develop new products. You will also learn how to use SQL to track product performance and make data-driven decisions.
Operations Manager
Operations Manager
As an Operations Manager, you will be responsible for managing the day-to-day operations of a business. This course will provide you with the skills you need to clean and analyze data to improve operational efficiency. You will also learn how to use SQL to create reports and dashboards to track operational performance.
Consultant
Consultant
As a Consultant, you will be responsible for providing advice and guidance to clients on a variety of business issues. This course will provide you with the skills you need to clean and analyze data to identify opportunities and solve business problems. You will also learn how to use SQL to create reports and dashboards to communicate your findings to clients.
Project Manager
Project Manager
As a Project Manager, you will be responsible for planning, executing, and closing projects. This course will provide you with the skills you need to clean and analyze project data to identify risks and opportunities and improve project performance. You will also learn how to use SQL to create reports and dashboards to track project progress.
For more career information including salaries, visit:
OpenCourser.com/course/jwtgrz/data
Reading list
We've selected 12 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
Data Cleaning in Snowflake: Techniques to Clean Messy Data.
Comprehensive guide to data science with Python. It covers a wide range of topics, such as data cleaning, data analysis, and data visualization. The book is well-written and easy to follow, and it provides numerous examples and exercises.
Comprehensive guide to data analysis with R. It covers a wide range of topics, such as data cleaning, data analysis, and data visualization. The book is well-written and easy to follow, and it provides numerous examples and exercises.
Comprehensive guide to statistical methods for data analysis. It covers a wide range of topics, such as data cleaning, data analysis, and data visualization. The book is well-written and easy to follow, and it provides numerous examples and exercises.
Comprehensive guide to deep learning with Python. It covers a wide range of topics, such as data cleaning, data analysis, and data visualization. The book is well-written and easy to follow, and it provides numerous examples and exercises.
Comprehensive guide to data cleaning with Python. It covers all the major data cleaning tasks, such as data profiling, data transformation, and data validation. The book is well-written and easy to follow, and it provides numerous examples and exercises.
Comprehensive guide to natural language processing with Python. It covers a wide range of topics, such as data cleaning, data analysis, and data visualization. The book is well-written and easy to follow, and it provides numerous examples and exercises.
Save
Practical guide to data cleaning with R. It covers all the major data cleaning tasks, such as data profiling, data transformation, and data validation. The book is well-written and easy to follow, and it provides numerous examples and exercises.
Practical guide to data wrangling with Python. It covers all the major data wrangling tasks, such as data loading, data cleaning, and data transformation. The book is well-written and easy to follow, and it provides numerous examples and exercises.
Comprehensive guide to regression modeling with R. It covers a wide range of topics, such as data cleaning, data analysis, and data visualization. The book is well-written and easy to follow, and it provides numerous examples and exercises.
Comprehensive guide to machine learning with R. It covers a wide range of topics, such as data cleaning, data analysis, and data visualization. The book is well-written and easy to follow, and it provides numerous examples and exercises.
Provides a non-technical overview of data science and its applications in business. It covers topics such as data collection, data analysis, and data visualization, and it provides practical advice on how to use data to make better decisions.
Practical guide to data cleaning with Pandas. It covers all the major data cleaning tasks, such as data profiling, data transformation, and data validation. The book is well-written and easy to follow, and it provides numerous examples and exercises.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/jwtgrz/data
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Effort
2.5
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Mohamed Touiti
Language
English
Good to know
Teaches how to refine data, which is essential for business analysis
Uses SQL, which is widely used in the industry
Involves a hands-on approach to data cleaning
Provides a practical example from the marketing field
Assumes Snowflake beginner knowledge, which may limit accessibility for complete beginners
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