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Gundega Dekena
and
Shannon Bradshaw
Data Scientists spend most of their time cleaning data. Take Udacity's Data Wrangling with MongoDB course and learn to convert and manipulate messy data to extract what you need.
Register for this course and see more details by visiting:
OpenCourser.com/course/svutf6/udacity
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What's inside
Syllabus
Review the fundamentals of tabular data formats CSV and Excel and learn about the JSON format.
Gain experience working with .csv files and wrangling JSON.
Read more
Syllabus
Review the fundamentals of tabular data formats CSV and Excel and learn about the JSON format.
Gain experience working with .csv files and wrangling JSON.
Read more
Start working with XML and learn how to use BeautifulSoup to scrape web pages.
Practice working with XML and parsing HTML with BeautifulSoup.
Learn about what can make data "dirty" and find out how you can audit your data for quality.
Practice auditing and cleaning dirty data.
Find out how to use MongoDB to store and query your data.
Practice wrangling data and inserting data into MongoDB.
Learn how to create more sophisticated MongoDB queries using pipelines and operators.
Gain experience with MongoDB pipelines and operators.
Go through a case study showing how to audit, clean and prepare OpenStreetMap data for insertion into a database.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores various data formats like CSV, Excel, JSON, XML, and HTML, equipping learners with versatility in data handling
Emphasizes data cleaning and auditing techniques, which are crucial for ensuring data integrity
Utilizes MongoDB, a popular database for storing and querying data
Provides practical experience through hands-on exercises, enhancing learners' data manipulation skills
Case study demonstrates real-world application of data wrangling techniques
Taught by experienced instructors, Gundega Dekena and Shannon Bradshaw, who bring industry knowledge and expertise
Save this course
Save Data Wrangling with MongoDB to your list so you can find it easily later:
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Reviews summary
Mongodb data wrangling basics
MongoDB Data Wrangling is an introductory level course that teaches learners how to extract, clean, and assess data using MongoDB. This self-paced course is appropriate for beginners who seek to build upon existing SQL knowledge and/or to add big data to their analytical skillset.
Final course project is helpful.
"...project at the end was very helpful. Getting to clean data, convert it from XML to JSON, load it into MongoDB and analyze it took effort."
Course is great for beginners.
"This is a great course for those interested in entry-level data science positions as well as..."
Requires existing Python knowledge.
"it's the worst course so far from data analyst nanodegree."
Final Project Instructions are missing.
"I didn't find any "Final Project Instructions" in the course though."
Skipped detailed explanations and codes.
"skipping lot of details and not well explanation for the codes"
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 Wrangling with MongoDB with these
activities:
Organize and review course notes and assignments
Show steps
Regularly review and organize course materials, including notes, assignments, and quizzes, to improve retention and understanding.
Browse courses on
Data Wrangling
Show steps
-
Create a system for organizing course materials, such as folders or a digital notebook.
-
Review notes after each class to reinforce concepts.
-
Complete assignments on time and review them afterwards to identify areas for improvement.
-
Take practice quizzes to test your understanding of the material.
Practice reading files with pandas
Show steps
Practice using pandas to read data from various supported file formats, such as CSV, JSON, and Excel.
Browse courses on
Python
Show steps
-
Load a CSV file into a Pandas dataframe.
-
Load a JSON file into a Pandas dataframe.
-
Load an Excel file into a Pandas dataframe.
Follow a MongoDB tutorial
Show steps
Work through a MongoDB tutorial to gain hands-on experience with MongoDB, including creating and structuring databases and collections, performing CRUD (Create, Read, Update, Delete) operations, and querying data.
Browse courses on
MongoDB
Show steps
-
Find a MongoDB tutorial suitable for your skill level.
-
Follow the steps in the tutorial to create a MongoDB database and collection.
-
Insert, update, and delete documents in the collection.
-
Query the collection using basic and advanced queries.
-
Practice connecting to MongoDB using a programming language.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Participate in a data cleaning discussion group
Show steps
Join a discussion group with peers to share knowledge, discuss challenges, and exchange best practices in data cleaning.
Browse courses on
Data Cleaning
Show steps
-
Find or create a data cleaning discussion group.
-
Participate in discussions by sharing your experiences and insights.
-
Ask questions and seek advice from other participants.
-
Collaborate on solving data cleaning problems.
Attend a data cleaning workshop
Show steps
Attend a workshop led by industry experts to learn advanced data cleaning techniques, tools, and best practices.
Browse courses on
Data Cleaning
Show steps
-
Research and find a data cleaning workshop that aligns with your interests.
-
Register for the workshop and attend all sessions.
-
Take notes and actively participate in discussions.
-
Apply the knowledge and skills gained from the workshop to your own data cleaning projects.
Create a data cleaning cheat sheet
Show steps
Summarize the techniques and best practices for data cleaning in a cheat sheet format, including steps to identify and handle missing values, outliers, duplicates, and data inconsistencies.
Browse courses on
Data Cleaning
Show steps
-
Research and gather information about common data cleaning techniques.
-
Organize the information into categories, such as missing values, outliers, duplicates, and data inconsistencies.
-
Create a summary of each technique, including its purpose, advantages, and limitations.
-
Format the information into a cheat sheet for easy reference.
Develop a data cleaning pipeline
Show steps
Design and implement a data cleaning pipeline using appropriate tools and techniques to automate the data cleaning process.
Browse courses on
Data Cleaning
Show steps
-
Identify the data cleaning tasks that need to be performed.
-
Research and select suitable data cleaning tools and techniques.
-
Design the data cleaning pipeline, including data sources, cleaning steps, and output destination.
-
Implement the data cleaning pipeline using code or a graphical user interface.
-
Test and evaluate the performance of the data cleaning pipeline.
Organize and review course notes and assignments
Show steps
Regularly review and organize course materials, including notes, assignments, and quizzes, to improve retention and understanding.
Browse courses on
Data Wrangling
Show steps
Show steps
Show steps
- Create a system for organizing course materials, such as folders or a digital notebook.
- Review notes after each class to reinforce concepts.
- Complete assignments on time and review them afterwards to identify areas for improvement.
- Take practice quizzes to test your understanding of the material.
Practice reading files with pandas
Show steps
Practice using pandas to read data from various supported file formats, such as CSV, JSON, and Excel.
Browse courses on
Python
Show steps
Show steps
Show steps
- Load a CSV file into a Pandas dataframe.
- Load a JSON file into a Pandas dataframe.
- Load an Excel file into a Pandas dataframe.
Follow a MongoDB tutorial
Show steps
Work through a MongoDB tutorial to gain hands-on experience with MongoDB, including creating and structuring databases and collections, performing CRUD (Create, Read, Update, Delete) operations, and querying data.
Browse courses on
MongoDB
Show steps
Show steps
Show steps
- Find a MongoDB tutorial suitable for your skill level.
- Follow the steps in the tutorial to create a MongoDB database and collection.
- Insert, update, and delete documents in the collection.
- Query the collection using basic and advanced queries.
- Practice connecting to MongoDB using a programming language.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Participate in a data cleaning discussion group
Show steps
Join a discussion group with peers to share knowledge, discuss challenges, and exchange best practices in data cleaning.
Browse courses on
Data Cleaning
Show steps
Show steps
Show steps
- Find or create a data cleaning discussion group.
- Participate in discussions by sharing your experiences and insights.
- Ask questions and seek advice from other participants.
- Collaborate on solving data cleaning problems.
Attend a data cleaning workshop
Show steps
Attend a workshop led by industry experts to learn advanced data cleaning techniques, tools, and best practices.
Browse courses on
Data Cleaning
Show steps
Show steps
Show steps
- Research and find a data cleaning workshop that aligns with your interests.
- Register for the workshop and attend all sessions.
- Take notes and actively participate in discussions.
- Apply the knowledge and skills gained from the workshop to your own data cleaning projects.
Create a data cleaning cheat sheet
Show steps
Summarize the techniques and best practices for data cleaning in a cheat sheet format, including steps to identify and handle missing values, outliers, duplicates, and data inconsistencies.
Browse courses on
Data Cleaning
Show steps
Show steps
Show steps
- Research and gather information about common data cleaning techniques.
- Organize the information into categories, such as missing values, outliers, duplicates, and data inconsistencies.
- Create a summary of each technique, including its purpose, advantages, and limitations.
- Format the information into a cheat sheet for easy reference.
Develop a data cleaning pipeline
Show steps
Design and implement a data cleaning pipeline using appropriate tools and techniques to automate the data cleaning process.
Browse courses on
Data Cleaning
Show steps
Show steps
Show steps
- Identify the data cleaning tasks that need to be performed.
- Research and select suitable data cleaning tools and techniques.
- Design the data cleaning pipeline, including data sources, cleaning steps, and output destination.
- Implement the data cleaning pipeline using code or a graphical user interface.
- Test and evaluate the performance of the data cleaning pipeline.
Career center
Learners who complete Data Wrangling with MongoDB will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts are responsible for gathering, cleaning, and analyzing data to help businesses make informed decisions. This course provides a solid foundation in data wrangling techniques, including how to clean and prepare data for analysis. This skillset is essential for Data Analysts, as they often need to work with large and messy datasets.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems and make predictions. This course provides a foundation in data wrangling techniques, which are essential for Data Scientists. The course also covers MongoDB, a database that is often used by Data Scientists to store and query data.
Data Engineer
Data Engineer
Data Engineers are responsible for building and maintaining the infrastructure that stores and processes data. This course provides a foundation in data wrangling techniques, which are essential for Data Engineers. The course also covers MongoDB, a database that is often used by Data Engineers to store and query data.
Business Analyst
Business Analyst
Business Analysts use data to understand business needs and make recommendations. This course provides a foundation in data wrangling techniques, which are essential for Business Analysts. The course also covers MongoDB, a database that is often used by Business Analysts to store and query data.
Database Administrator
Database Administrator
Database Administrators are responsible for managing and maintaining databases. This course provides a foundation in data wrangling techniques, which are essential for Database Administrators. The course also covers MongoDB, a database that is often used by Database Administrators to store and query data.
Statistician
Statistician
Statisticians use data to make predictions and draw conclusions. This course provides a foundation in data wrangling techniques, which are essential for Statisticians. The course also covers MongoDB, a database that is often used by Statisticians to store and query data.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course provides a foundation in data wrangling techniques, which can be useful for Software Engineers who work with data-intensive applications. The course also covers MongoDB, a database that is often used by Software Engineers to store and query data.
Product Manager
Product Manager
Product Managers are responsible for planning and managing the development of products. This course provides a foundation in data wrangling techniques, which can be useful for Product Managers who need to understand and analyze data about their products. The course also covers MongoDB, a database that is often used by Product Managers to store and query data.
Marketing Analyst
Marketing Analyst
Marketing Analysts use data to understand customer behavior and make marketing decisions. This course provides a foundation in data wrangling techniques, which can be useful for Marketing Analysts who need to clean and prepare data for analysis. The course also covers MongoDB, a database that is often used by Marketing Analysts to store and query data.
Financial Analyst
Financial Analyst
Financial Analysts use data to make investment decisions. This course provides a foundation in data wrangling techniques, which can be useful for Financial Analysts who need to clean and prepare data for analysis. The course also covers MongoDB, a database that is often used by Financial Analysts to store and query data.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use data to solve business problems. This course provides a foundation in data wrangling techniques, which can be useful for Operations Research Analysts who need to clean and prepare data for analysis. The course also covers MongoDB, a database that is often used by Operations Research Analysts to store and query data.
Risk Analyst
Risk Analyst
Risk Analysts use data to identify and assess risks. This course provides a foundation in data wrangling techniques, which can be useful for Risk Analysts who need to clean and prepare data for analysis. The course also covers MongoDB, a database that is often used by Risk Analysts to store and query data.
Auditor
Auditor
Auditors use data to ensure that organizations are following laws and regulations. This course provides a foundation in data wrangling techniques, which can be useful for Auditors who need to clean and prepare data for analysis. The course also covers MongoDB, a database that is often used by Auditors to store and query data.
Fraud Investigator
Fraud Investigator
Fraud Investigators use data to investigate fraud. This course provides a foundation in data wrangling techniques, which can be useful for Fraud Investigators who need to clean and prepare data for analysis. The course also covers MongoDB, a database that is often used by Fraud Investigators to store and query data.
Tax Preparer
Tax Preparer
Tax Preparers use data to prepare tax returns. This course provides a foundation in data wrangling techniques, which can be useful for Tax Preparers who need to clean and prepare data for analysis. The course also covers MongoDB, a database that is often used by Tax Preparers to store and query data.
For more career information including salaries, visit:
OpenCourser.com/course/svutf6/udacity
Reading list
We've selected eight 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 Wrangling with MongoDB.
Provides a comprehensive overview of MongoDB, covering topics such as data modeling, indexing, and replication. It valuable resource for anyone looking to learn more about MongoDB.
Focuses on data wrangling with Python, providing techniques and tools for cleaning and transforming data. Useful for gaining practical skills in data wrangling.
Provides a comprehensive introduction to data science, including data wrangling, analysis, and visualization. Useful for gaining a foundation in data science concepts and techniques.
Provides a comprehensive guide to data wrangling with Python, covering topics such as data cleaning, transformation, and aggregation. It valuable resource for anyone looking to learn more about data wrangling with Python.
Save
Provides a comprehensive guide to MongoDB, covering topics such as data modeling, indexing, and replication. It valuable resource for anyone looking to learn more about MongoDB.
Save
Covers XML fundamentals and advanced concepts, providing practical examples and real-world scenarios. Useful for gaining hands-on experience with XML.
Save
Provides comprehensive documentation for BeautifulSoup, a popular Python library for web scraping. Useful for understanding how to use BeautifulSoup for data extraction and parsing.
Save
Provides a comprehensive overview of data quality, including its principles, techniques, and tools. Useful for gaining a deeper understanding of data quality and how to ensure it in data projects.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/svutf6/udacity
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Effort
2 days
Via
Udacity
Instructors
Gundega Dekena
Shannon Bradshaw
Language
English
Good to know
Explores various data formats like CSV, Excel, JSON, XML, and HTML, equipping learners with versatility in data handling
Emphasizes data cleaning and auditing techniques, which are crucial for ensuring data integrity
Utilizes MongoDB, a popular database for storing and querying data
Provides practical experience through hands-on exercises, enhancing learners' data manipulation skills
Case study demonstrates real-world application of data wrangling techniques
Taught by experienced instructors, Gundega Dekena and Shannon Bradshaw, who bring industry knowledge and expertise
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