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A Cloud Guru
Python can be a powerful tool for data preparation. In this course, we will quickly cover how to connect to various database types. Then, we will jump into using the pandas Python package for data preparation. We will look at examples of cleansing missing and outlying data as well as data visualizations and exploration. In addition to the pandas package, we will also look at preprocessing data for machine learning using the scikit-learn Python package. Before beginning this course, you should have a strong knowledge of Python and data approaches. Check out the **Prerequisite and Related Courses** lesson in the Introduction section for a starting point.
Register for this course and see more details by visiting:
OpenCourser.com/course/9q3dck/data
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what to watch for , and
possible dealbreakers
Teaches data preparation using Pandas for data cleansing, data visualization, and data exploration
Provides instruction for preprocessing data for machine learning techniques with scikit-learn
Designed for students with a solid understanding of Python and data concepts
Led by the respected A Cloud Guru instructors who have extensive knowledge and expertise in cloud technologies
Utilizes a mix of video lectures and hands-on exercises to enhance learning
Covers data preparation best practices and industry-standard techniques
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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 Data Preparation (Import and Cleaning) for Python with these
activities:
Read 'Python for Data Analysis' by Wes McKinney
Show steps
Gain a deeper understanding of Python's role in data analysis and data preparation by reviewing a comprehensive book on the subject.
View
Python for Data Analysis: Data Wrangling with...
on Amazon
Show steps
-
Purchase or borrow a copy of 'Python for Data Analysis'.
-
Read through the chapters covering data manipulation, cleaning, and preparation.
Compile a collection of useful data preparation resources
Show steps
Build a valuable repository of data preparation resources, including tools, techniques, and best practices.
Browse courses on
Compilation
Show steps
-
Conduct research to find useful data preparation resources.
-
Organize and categorize the resources in a central location.
Review pandas library basics
Show steps
Review the basics of the pandas library to ensure a strong foundation for data preparation tasks in the course.
Browse courses on
Pandas
Show steps
-
Visit the pandas documentation website and read the tutorial.
-
Practice creating and manipulating DataFrames using pandas.
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Review Python basics
Show steps
Review the basics of Python syntax, data structures, and algorithms to prepare for this course.
Browse courses on
Python
Show steps
-
Read through a Python tutorial or textbook.
-
Complete a few practice problems or exercises.
Form a study group and discuss data preparation challenges
Show steps
Enhance understanding of data preparation concepts and techniques by discussing challenges and solutions with peers.
Show steps
-
Identify other students taking the course and form a study group.
-
Meet regularly to discuss data preparation challenges, share resources, and provide support.
Follow a pandas tutorial
Show steps
Follow a tutorial or online course on pandas to learn its core features and how to use it for data preparation.
Browse courses on
Pandas
Show steps
-
Choose a pandas tutorial or online course.
-
Work through the tutorial or course step-by-step.
Follow tutorials on data preprocessing with scikit-learn
Show steps
Enhance understanding of data preprocessing techniques for machine learning tasks by following guided tutorials on scikit-learn.
Browse courses on
scikit-learn
Show steps
-
Search for tutorials on scikit-learn data preprocessing.
-
Complete several tutorials, practicing data scaling, feature selection, and other preprocessing methods.
Attend a workshop on advanced data preparation techniques
Show steps
Expand knowledge and skills in data preparation by attending a workshop led by industry experts.
Browse courses on
Advanced Data Analysis
Show steps
-
Research and find workshops on advanced data preparation techniques.
-
Register and attend the workshop.
Practice data cleaning with pandas
Show steps
Complete practice exercises or coding challenges that focus on cleaning and preparing data with pandas.
Browse courses on
Data Cleaning
Show steps
-
Find a set of practice problems or exercises on data cleaning.
-
Work through the exercises, focusing on applying pandas techniques.
Solve coding exercises on data preparation
Show steps
Reinforce understanding of data preparation techniques by solving coding exercises that simulate real-world scenarios.
Browse courses on
Data Preparation
Show steps
-
Find online coding platforms or resources that provide data preparation exercises.
-
Attempt to solve several exercises, focusing on applying pandas and scikit-learn for data cleaning and preprocessing tasks.
Project: Build a simple data pipeline
Show steps
Develop a small project that involves connecting to a database, cleaning and preparing data with pandas, and visualizing it.
Browse courses on
Data Pipeline
Show steps
-
Choose a simple dataset and database to work with.
-
Develop a data pipeline that connects to the database, cleans the data, and prepares it for analysis.
-
Create visualizations to explore and present the prepared data.
Develop a data preparation pipeline for a specific dataset
Show steps
Apply the concepts learned in the course to a practical project by creating a data preparation pipeline for a dataset of your choice.
Show steps
-
Select a dataset that requires data preparation.
-
Design and implement a data preparation pipeline using pandas and scikit-learn.
-
Document the pipeline and share it with others.
Read 'Python for Data Analysis' by Wes McKinney
Show steps
Gain a deeper understanding of Python's role in data analysis and data preparation by reviewing a comprehensive book on the subject.
View
Python for Data Analysis: Data Wrangling with...
on Amazon
Show steps
Show steps
Show steps
- Purchase or borrow a copy of 'Python for Data Analysis'.
- Read through the chapters covering data manipulation, cleaning, and preparation.
Compile a collection of useful data preparation resources
Show steps
Build a valuable repository of data preparation resources, including tools, techniques, and best practices.
Browse courses on
Compilation
Show steps
Show steps
Show steps
- Conduct research to find useful data preparation resources.
- Organize and categorize the resources in a central location.
Review pandas library basics
Show steps
Review the basics of the pandas library to ensure a strong foundation for data preparation tasks in the course.
Browse courses on
Pandas
Show steps
Show steps
Show steps
- Visit the pandas documentation website and read the tutorial.
- Practice creating and manipulating DataFrames using pandas.
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Review Python basics
Show steps
Review the basics of Python syntax, data structures, and algorithms to prepare for this course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Read through a Python tutorial or textbook.
- Complete a few practice problems or exercises.
Form a study group and discuss data preparation challenges
Show steps
Enhance understanding of data preparation concepts and techniques by discussing challenges and solutions with peers.
Show steps
Show steps
Show steps
- Identify other students taking the course and form a study group.
- Meet regularly to discuss data preparation challenges, share resources, and provide support.
Follow a pandas tutorial
Show steps
Follow a tutorial or online course on pandas to learn its core features and how to use it for data preparation.
Browse courses on
Pandas
Show steps
Show steps
Show steps
- Choose a pandas tutorial or online course.
- Work through the tutorial or course step-by-step.
Follow tutorials on data preprocessing with scikit-learn
Show steps
Enhance understanding of data preprocessing techniques for machine learning tasks by following guided tutorials on scikit-learn.
Browse courses on
scikit-learn
Show steps
Show steps
Show steps
- Search for tutorials on scikit-learn data preprocessing.
- Complete several tutorials, practicing data scaling, feature selection, and other preprocessing methods.
Attend a workshop on advanced data preparation techniques
Show steps
Expand knowledge and skills in data preparation by attending a workshop led by industry experts.
Browse courses on
Advanced Data Analysis
Show steps
Show steps
Show steps
- Research and find workshops on advanced data preparation techniques.
- Register and attend the workshop.
Practice data cleaning with pandas
Show steps
Complete practice exercises or coding challenges that focus on cleaning and preparing data with pandas.
Browse courses on
Data Cleaning
Show steps
Show steps
Show steps
- Find a set of practice problems or exercises on data cleaning.
- Work through the exercises, focusing on applying pandas techniques.
Solve coding exercises on data preparation
Show steps
Reinforce understanding of data preparation techniques by solving coding exercises that simulate real-world scenarios.
Browse courses on
Data Preparation
Show steps
Show steps
Show steps
- Find online coding platforms or resources that provide data preparation exercises.
- Attempt to solve several exercises, focusing on applying pandas and scikit-learn for data cleaning and preprocessing tasks.
Project: Build a simple data pipeline
Show steps
Develop a small project that involves connecting to a database, cleaning and preparing data with pandas, and visualizing it.
Browse courses on
Data Pipeline
Show steps
Show steps
Show steps
- Choose a simple dataset and database to work with.
- Develop a data pipeline that connects to the database, cleans the data, and prepares it for analysis.
- Create visualizations to explore and present the prepared data.
Develop a data preparation pipeline for a specific dataset
Show steps
Apply the concepts learned in the course to a practical project by creating a data preparation pipeline for a dataset of your choice.
Show steps
Show steps
Show steps
- Select a dataset that requires data preparation.
- Design and implement a data preparation pipeline using pandas and scikit-learn.
- Document the pipeline and share it with others.
Career center
Learners who complete Data Preparation (Import and Cleaning) for Python will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts explore and analyze raw data, helping organizations understand their operations more clearly. They work closely with management to advise them about how to optimize performance and increase revenue. The Data Preparation (Import and Cleaning) for Python course will provide you with the skills needed to effectively organize and clean data before analyzing it, which is an important step in the data analysis process. This course may help you build a foundation for a successful career as a Data Analyst.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data infrastructure. They work closely with Data Analysts and other stakeholders to collect, transform, and store data. The Data Preparation (Import and Cleaning) for Python course can teach you how to import and clean data, which are essential tasks for a Data Engineer. This course may help you build a foundation for a successful career as a Data Engineer.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models. They work closely with Data Scientists and other stakeholders to collect, transform, and store data. The Data Preparation (Import and Cleaning) for Python course can teach you how to import and clean data, which are essential tasks for a Machine Learning Engineer. This course may help you build a foundation for a successful career as a Machine Learning Engineer.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematics, statistics, and computer science to develop financial models. They work closely with portfolio managers and other stakeholders to make investment decisions. The Data Preparation (Import and Cleaning) for Python course will provide you with the skills needed to effectively organize and clean data before analyzing it, which is an important step in the quantitative analysis process. This course may help you build a foundation for a successful career as a Quantitative Analyst.
Data Scientist
Data Scientist
Data Scientists use their knowledge of mathematics, statistics, and computer science to extract insights from data. They work closely with management to advise them about how to optimize performance and increase revenue. The Data Preparation (Import and Cleaning) for Python course will provide you with the skills needed to effectively organize and clean data before analyzing it, which is an important step in the data science process. This course may help you build a foundation for a successful career as a Data Scientist.
Database Administrator
Database Administrator
Database Administrators design, build, and maintain databases. They work closely with stakeholders such as developers, analysts, and end users to gather requirements. The Data Preparation (Import and Cleaning) for Python course can teach you how to import and clean data, which are essential skills for a Database Administrator. This course may help you get started in a career as a Database Administrator.
Financial Analyst
Financial Analyst
Financial Analysts use data to help businesses make better financial decisions. They work closely with stakeholders such as executives, managers, and investors to gather requirements. The Data Preparation (Import and Cleaning) for Python course can teach you how to import and clean data, which are essential skills for a Financial Analyst. This course may help you get started in a career as a Financial Analyst.
Marketing Analyst
Marketing Analyst
Marketing Analysts use data to help businesses make better marketing decisions. They work closely with stakeholders such as executives, managers, and customers to gather requirements. The Data Preparation (Import and Cleaning) for Python course can teach you how to import and clean data, which are essential skills for a Marketing Analyst. This course may help you get started in a career as a Marketing Analyst.
Risk Analyst
Risk Analyst
Risk Analysts use data to help businesses identify and manage risks. They work closely with stakeholders such as executives, managers, and regulators to gather requirements. The Data Preparation (Import and Cleaning) for Python course can teach you how to import and clean data, which are essential skills for a Risk Analyst. This course may help you get started in a career as a Risk Analyst.
Data Visualization Specialist
Data Visualization Specialist
Data Visualization Specialists create visual representations of data. They work closely with stakeholders such as executives, managers, and end users to gather requirements. The Data Preparation (Import and Cleaning) for Python course can teach you how to import and clean data, which are essential skills for a Data Visualization Specialist. This course may help you get started in a career as a Data Visualization Specialist.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use data to help businesses make better decisions. They work closely with stakeholders such as executives, managers, and end users to gather requirements. The Data Preparation (Import and Cleaning) for Python course can teach you how to import and clean data, which are essential skills for a Business Intelligence Analyst. This course may help you get started in a career as a Business Intelligence Analyst.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use data to help businesses make better operational decisions. They work closely with stakeholders such as executives, managers, and end users to gather requirements. The Data Preparation (Import and Cleaning) for Python course can teach you how to import and clean data, which are essential skills for an Operations Research Analyst. This course may help you get started in a career as an Operations Research Analyst.
Statistician
Statistician
Statisticians use data to help businesses make better decisions. They work closely with stakeholders such as executives, managers, and end users to gather requirements. The Data Preparation (Import and Cleaning) for Python course can teach you how to import and clean data, which are essential skills for a Statistician. This course may help you get started in a career as a Statistician.
Data Architect
Data Architect
Data Architects design and manage data architectures. They work closely with stakeholders such as business analysts, developers, and end users to gather requirements. The Data Preparation (Import and Cleaning) for Python course can teach you how to import and clean data, which are essential skills for a Data Architect. This course may help you get started in a career as a Data Architect.
Software Engineer
Software Engineer
Software Engineers design, build, and maintain software systems. They work closely with stakeholders such as product managers, designers, and customers to gather requirements. The Data Preparation (Import and Cleaning) for Python course can teach you how to import and clean data, which are essential skills for a Software Engineer. This course may help you get started in a career as a Software Engineer.
For more career information including salaries, visit:
OpenCourser.com/course/9q3dck/data
Reading list
We've selected 11 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 Preparation (Import and Cleaning) for Python.
Provides a comprehensive guide to data science using Python, covering essential tools and techniques for data preparation, analysis, and visualization. It offers a solid foundation for understanding the concepts and methods used in this course.
Provides a comprehensive guide to machine learning using Python, covering a wide range of topics. It offers a valuable resource for those seeking a more in-depth understanding of the field.
As a primary reference for the pandas Python package, this book delves into data manipulation techniques, making it an invaluable resource for understanding how to efficiently clean, transform, and analyze data in this course.
Provides a comprehensive guide to machine learning and deep learning using Python, covering a wide range of topics. It offers a valuable resource for those seeking a more in-depth understanding of the field.
Offers a comprehensive introduction to data science from first principles, providing a strong foundation for understanding the concepts and approaches covered in this course.
Provides a comprehensive introduction to Bayesian statistics, covering a wide range of topics. It offers a valuable resource for those seeking a deeper theoretical understanding of the field.
Provides a comprehensive introduction to statistical learning methods, covering data preparation, model selection, and evaluation techniques. It offers a theoretical foundation for the concepts and approaches used in this course.
Explores feature engineering techniques for machine learning, providing practical guidance on creating informative and predictive features from raw data. It complements the course's coverage of data preparation by providing advanced techniques for feature selection and transformation.
Provides a comprehensive introduction to machine learning from a probabilistic perspective, covering a wide range of topics. It offers a valuable resource for those seeking a deeper theoretical understanding of the field.
Offers an advanced treatment of statistical learning methods, providing in-depth coverage of data preparation, model selection, and evaluation techniques. It serves as a valuable reference for deeper understanding of the concepts covered in this course.
Provides a gentle introduction to machine learning using Python, covering essential concepts and algorithms. It offers a good starting point for those new to the field.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/9q3dck/data
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Time
390 hours
Level
Advanced
Via
Pluralsight
Instructor
A Cloud Guru
Language
English
Good to know
Teaches data preparation using Pandas for data cleansing, data visualization, and data exploration
Provides instruction for preprocessing data for machine learning techniques with scikit-learn
Designed for students with a solid understanding of Python and data concepts
Led by the respected A Cloud Guru instructors who have extensive knowledge and expertise in cloud technologies
Utilizes a mix of video lectures and hands-on exercises to enhance learning
Covers data preparation best practices and industry-standard techniques
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