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Josh Magee,
Ria Cheruvu,
and
Matt Maybeno
Take Udacity's Data Wrangling course and learn how to leverage the power of Python to quickly gather, assess & clean messy data for easy and quick analysis.
Prerequisite details
Read more
Take Udacity's Data Wrangling course and learn how to leverage the power of Python to quickly gather, assess & clean messy data for easy and quick analysis.
Prerequisite details
Read more
Take Udacity's Data Wrangling course and learn how to leverage the power of Python to quickly gather, assess & clean messy data for easy and quick analysis.
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
- Basic Python
- Basic descriptive statistics
- Pandas
You will also need to be able to communicate fluently and professionally in written and spoken English.
Register for this course and see more details by visiting:
OpenCourser.com/course/0c8ccb/advanced
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What's inside
Syllabus
You will learn what data wrangling is and why it matters. And you will see a real-world example of data wrangling and some common misconceptions about data wrangling.
Read more
Syllabus
You will learn what data wrangling is and why it matters. And you will see a real-world example of data wrangling and some common misconceptions about data wrangling.
Read more
You will learn to implement data gathering methods to obtain and extract data from various sources and in several popular data formats.
You will learn to identify different data quality and structural issues and apply visual and programmatic assessments to catch them.
You will learn to remediate the issues you identified in the assessment stage and test that your data cleaning is successful.
You will apply the skills you acquired in the course by gathering, assessing, and cleaning multiple real-world datasets of your choice.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces core data wrangling concepts and best practices used in industry
Provides hands-on experience in gathering, assessing, and cleaning real-world datasets
Covers a range of data formats and sources, making it applicable to various scenarios
Teaches techniques for identifying and resolving common data quality issues
Requires basic Python and Pandas knowledge, making it accessible to learners with some programming experience
Does not cover advanced data wrangling techniques or specialized tools, which may be necessary for complex data manipulation tasks
Save this course
Save Advanced Data Wrangling 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 Advanced Data Wrangling with these
activities:
Review basic Python and statistics concepts
Show steps
Refreshing your knowledge in these areas will help you better understand the concepts covered in the course.
Browse courses on
Python
Show steps
-
Review basic Python syntax and data structures
-
Recall concepts of descriptive statistics such as mean, median, and standard deviation
Watch videos on data wrangling best practices and tools
Show steps
Videos can provide valuable insights and demonstrations of effective data wrangling techniques and tools.
Show steps
-
Identify reputable sources for data wrangling videos
-
Watch videos and take notes on key concepts and techniques
Follow tutorials on data quality assessment techniques
Show steps
Tutorials will provide you with step-by-step instructions on how to assess data quality and identify potential issues.
Show steps
-
Identify common data quality issues such as missing values, duplicates, and outliers
-
Learn how to use statistical techniques to assess data distribution and identify anomalies
-
Practice using visualization tools to explore data and identify patterns and trends
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice manipulating data using Pandas
Show steps
By practicing, you will develop the necessary skills and gain confidence in your data manipulation skills.
Show steps
-
Load a dataset into a Pandas DataFrame
-
Explore the data using the head() and tail() methods
-
Perform basic data manipulation tasks such as filtering, sorting, and grouping data
-
Create new columns and modify existing ones using built-in functions and methods
Participate in a data wrangling workshop
Show steps
Workshops provide a structured environment to learn and practice data wrangling under the guidance of experts.
Show steps
-
Research and identify relevant workshops
-
Enroll in a workshop and actively participate in sessions
-
Apply the skills and techniques learned in the workshop to your own projects
Complete coding exercises on data cleaning techniques
Show steps
Hands-on coding exercises will reinforce your understanding and help you develop the skills necessary for successful data cleaning.
Show steps
-
Work through coding exercises that cover different data cleaning techniques
-
Practice using Python libraries such as NumPy and Pandas to clean and transform data
-
Debug and optimize your code to improve efficiency
Attend data science meetups or conferences
Show steps
Networking with professionals in the field can broaden your knowledge and provide insights into industry practices.
Show steps
-
Identify relevant meetups or conferences in your area
-
Attend events and engage in discussions
-
Connect with professionals and exchange knowledge
Create a case study on a real-world data wrangling project
Show steps
By working on a real-world project, you can apply your skills and gain experience in data wrangling.
Show steps
-
Identify a suitable dataset
-
Gather and clean the data
-
Analyze the data and draw insights
-
Create a report or presentation showcasing your findings
Review basic Python and statistics concepts
Show steps
Refreshing your knowledge in these areas will help you better understand the concepts covered in the course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review basic Python syntax and data structures
- Recall concepts of descriptive statistics such as mean, median, and standard deviation
Watch videos on data wrangling best practices and tools
Show steps
Videos can provide valuable insights and demonstrations of effective data wrangling techniques and tools.
Show steps
Show steps
Show steps
- Identify reputable sources for data wrangling videos
- Watch videos and take notes on key concepts and techniques
Follow tutorials on data quality assessment techniques
Show steps
Tutorials will provide you with step-by-step instructions on how to assess data quality and identify potential issues.
Show steps
Show steps
Show steps
- Identify common data quality issues such as missing values, duplicates, and outliers
- Learn how to use statistical techniques to assess data distribution and identify anomalies
- Practice using visualization tools to explore data and identify patterns and trends
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice manipulating data using Pandas
Show steps
By practicing, you will develop the necessary skills and gain confidence in your data manipulation skills.
Show steps
Show steps
Show steps
- Load a dataset into a Pandas DataFrame
- Explore the data using the head() and tail() methods
- Perform basic data manipulation tasks such as filtering, sorting, and grouping data
- Create new columns and modify existing ones using built-in functions and methods
Participate in a data wrangling workshop
Show steps
Workshops provide a structured environment to learn and practice data wrangling under the guidance of experts.
Show steps
Show steps
Show steps
- Research and identify relevant workshops
- Enroll in a workshop and actively participate in sessions
- Apply the skills and techniques learned in the workshop to your own projects
Complete coding exercises on data cleaning techniques
Show steps
Hands-on coding exercises will reinforce your understanding and help you develop the skills necessary for successful data cleaning.
Show steps
Show steps
Show steps
- Work through coding exercises that cover different data cleaning techniques
- Practice using Python libraries such as NumPy and Pandas to clean and transform data
- Debug and optimize your code to improve efficiency
Attend data science meetups or conferences
Show steps
Networking with professionals in the field can broaden your knowledge and provide insights into industry practices.
Show steps
Show steps
Show steps
- Identify relevant meetups or conferences in your area
- Attend events and engage in discussions
- Connect with professionals and exchange knowledge
Create a case study on a real-world data wrangling project
Show steps
By working on a real-world project, you can apply your skills and gain experience in data wrangling.
Show steps
Show steps
Show steps
- Identify a suitable dataset
- Gather and clean the data
- Analyze the data and draw insights
- Create a report or presentation showcasing your findings
Career center
Learners who complete Advanced Data Wrangling will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data scientists use data to build models and solve problems. This course would be extremely helpful to a data scientist because it provides instruction on how to gather, clean, and analyze data. The skills learned in this course will help data scientists to do their jobs more efficiently and effectively.
Machine Learning Engineer
Machine Learning Engineer
Machine learning engineers build and maintain the models that power artificial intelligence applications. This course would be extremely helpful to a machine learning engineer because it provides instruction on how to gather, clean, and analyze data. The skills learned in this course will help machine learning engineers to do their jobs more efficiently and effectively.
Business Analyst
Business Analyst
Business analysts use data to identify and solve business problems. This course would be extremely helpful to a business analyst because it provides instruction on how to gather, clean, and analyze data. The skills learned in this course will help business analysts to do their jobs more efficiently and effectively.
Statistician
Statistician
Statisticians use data to make inferences about the world. This course would be extremely helpful to a statistician because it provides instruction on how to gather, clean, and analyze data. The skills learned in this course will help statisticians to do their jobs more efficiently and effectively.
Data Architect
Data Architect
Data architects design and build the systems that store and process data. This course would be extremely helpful to a data architect because it provides instruction on how to gather, clean, and analyze data. The skills learned in this course will help data architects to do their jobs more efficiently and effectively.
Data Analyst
Data Analyst
Data analysts collect, clean, and interpret data to solve business problems. This course would be extremely helpful to a data analyst because it provides instruction on how to gather, clean, and analyze data. The skills learned in this course will help data analysts to do their jobs more efficiently and effectively.
Database Administrator
Database Administrator
Database administrators maintain the databases that store data. This course would be extremely helpful to a database administrator because it provides instruction on how to gather, clean, and analyze data. The skills learned in this course will help database administrators to do their jobs more efficiently and effectively.
Information Security Analyst
Information Security Analyst
Information security analysts protect data from unauthorized access and use. This course would be extremely helpful to an information security analyst because it provides instruction on how to gather, clean, and analyze data. The skills learned in this course will help information security analysts to do their jobs more efficiently and effectively.
Financial Analyst
Financial Analyst
Financial analysts use data to evaluate investments. This course would be extremely helpful to a financial analyst because it provides instruction on how to gather, clean, and analyze data. The skills learned in this course will help financial analysts to do their jobs more efficiently and effectively.
Data Engineer
Data Engineer
Data engineers build and maintain the systems that store and process data. This course would be extremely helpful to a data engineer because it provides instruction on how to gather, clean, and analyze data. The skills learned in this course will help data engineers to do their jobs more efficiently and effectively.
Actuary
Actuary
Actuaries use data to assess risk. This course would be extremely helpful to an actuary because it provides instruction on how to gather, clean, and analyze data. The skills learned in this course will help actuaries to do their jobs more efficiently and effectively.
Health Data Analyst
Health Data Analyst
Health data analysts use data to improve the quality of healthcare. This course would be extremely helpful to a health data analyst because it provides instruction on how to gather, clean, and analyze data. The skills learned in this course will help health data analysts to do their jobs more efficiently and effectively.
Data Analytics Consultant
Data Analytics Consultant
Data analytics consultants help organizations to use data to make better decisions. This course would be extremely helpful to a data analytics consultant because it provides instruction on how to gather, clean, and analyze data. The skills learned in this course will help data analytics consultants to do their jobs more efficiently and effectively.
Data Science Research Analyst
Data Science Research Analyst
Data science research analysts use data to conduct research and develop new solutions. This course would be extremely helpful to a data science research analyst because it provides instruction on how to gather, clean, and analyze data. The skills learned in this course will help data science research analysts to do their jobs more efficiently and effectively.
For more career information including salaries, visit:
OpenCourser.com/course/0c8ccb/advanced
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
Advanced Data Wrangling.
Provides a comprehensive introduction to using Pandas, a powerful Python library for data analysis and manipulation. It covers all the essential topics for data wrangling, including data loading, cleaning, transformation, and visualization.
Save
Provides a practical introduction to data visualization. It covers all the essential topics, including data visualization principles, techniques, and tools. It valuable resource for anyone who wants to learn how to visualize data effectively.
Comprehensive guide to data analysis with Python. It covers topics such as data manipulation, data visualization, and machine learning. It valuable resource for anyone who wants to learn more about data analysis with Python.
Practical guide to data analysis with Pandas. It covers a wide range of topics, from data wrangling to data visualization.
Save
Provides a comprehensive guide to deep learning, covering topics such as neural networks, deep learning architectures, and applications. It valuable resource for readers who want to learn how to use deep learning to solve complex problems.
Provides a comprehensive guide to machine learning with Python, covering topics such as supervised learning, unsupervised learning, and natural language processing. It valuable resource for readers who want to learn how to use machine learning to solve real-world problems.
Provides a comprehensive guide to statistical learning, covering topics such as linear regression, logistic regression, and decision trees. It valuable resource for readers who want to learn how to use statistical learning to solve real-world problems.
Provides a hands-on guide to data science from scratch. It good choice for readers who want to learn how to use Python to build data science projects.
Provides a comprehensive guide to data science for business, covering topics such as data collection, analysis, and visualization. It valuable resource for readers who want to learn how to use data science to improve their business.
Save
Provides a collection of recipes for solving common problems in data analysis with Pandas. It useful resource for readers who want to learn how to use Pandas to solve specific data analysis problems.
Provides a hands-on guide to machine learning with Scikit-Learn, Keras, and TensorFlow. It good choice for readers who want to learn how to use machine learning to solve real-world problems.
Provides a guide to effective data visualization, covering topics such as choosing the right chart for the right data, and designing charts that are clear and easy to understand. It useful resource for readers who want to learn how to visualize data effectively.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/0c8ccb/advanced
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Effort
4 weeks
Level
Intermediate
Via
Udacity
Instructors
Josh Magee
Ria Cheruvu
Matt Maybeno
Language
English
Good to know
Introduces core data wrangling concepts and best practices used in industry
Provides hands-on experience in gathering, assessing, and cleaning real-world datasets
Covers a range of data formats and sources, making it applicable to various scenarios
Teaches techniques for identifying and resolving common data quality issues
Requires basic Python and Pandas knowledge, making it accessible to learners with some programming experience
Does not cover advanced data wrangling techniques or specialized tools, which may be necessary for complex data manipulation tasks
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