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Saravanan Dhandapani
Data Scientists spend most of their time cleaning and processing their data before they can be leveraged for future predictions. This course will teach you the various data cleaning and processing techniques and how to leverage the cloud services and AI tools to accomplish them.
Properly cleaning and processing the data is crucial to ensure that the subsequent data modeling produces accurate, meaningful, and reliable data.
Read more
Data Scientists spend most of their time cleaning and processing their data before they can be leveraged for future predictions. This course will teach you the various data cleaning and processing techniques and how to leverage the cloud services and AI tools to accomplish them.
Properly cleaning and processing the data is crucial to ensure that the subsequent data modeling produces accurate, meaningful, and reliable data.
Read more
Data Scientists spend most of their time cleaning and processing their data before they can be leveraged for future predictions. This course will teach you the various data cleaning and processing techniques and how to leverage the cloud services and AI tools to accomplish them.
Properly cleaning and processing the data is crucial to ensure that the subsequent data modeling produces accurate, meaningful, and reliable data.
In this course, Data Cleaning and Processing for Data Scientists, you’ll gain the ability to learn the various techniques to pre-process the data that can be used to generate accurate analysis, which will lead to effective decision-making.
First, you’ll explore the various data-cleaning techniques and address data with missing values, duplicate data, and outliers.
Next, you’ll discover some of the transformation techniques like min-max scaler, standard scaler, one-hot encoding, and dimensionality reduction.
Finally, you’ll learn how to leverage the cloud services and AI tools and automate these tasks to achieve results quickly.
When you’re finished with this course, you’ll have the skills and knowledge of cleaning and processing the data needed to generate high-quality data for enhanced decision-making.
Register for this course and see more details by visiting:
OpenCourser.com/course/3nkkzn/data
What's inside
Syllabus
Course Overview
Data Cleaning Techniques and Strategies
Data Transformation Techniques and Strategies
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops data cleaning techniques used in industry, which are foundational for real-world application
Leverages cloud services and AI tools, which are in high demand for skilled data scientists
Teaches data transformation techniques used in the industry, which augments the cleaning techniques to make the data ready for analysis
Taught by instructors with a strong reputation, which adds credibility to the material
Focuses heavily on cleaning and processing tasks for data scientists, which aligns with the target audience
Save this course
Save Data Cleaning and Processing for Data Scientists 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 Data Cleaning and Processing for Data Scientists with these
activities:
Review Data Science For Dummies, 2nd Edition
Show steps
Review the fundamentals of data science from a beginner-friendly reference text.
View
Artificial Intelligence For Dummies (For...
on Amazon
Show steps
-
Obtain a copy of Data Science For Dummies, 2nd Edition.
-
Read chapters 1-4 to gain an overview of data science concepts.
-
Review highlighted sections on data cleaning techniques.
-
Take notes or summarize key concepts for better retention.
Organize Course Materials and Resources
Show steps
Create a structured system for storing and accessing course materials to enhance your learning experience.
Show steps
-
Create a dedicated folder or notebook for course materials.
-
Organize materials by topic or type (e.g., lecture notes, assignments, readings).
-
Establish a consistent naming convention for files.
Practice Data Cleaning Exercises from Kaggle
Show steps
Apply data cleaning techniques to real-world datasets, reinforcing your understanding.
Show steps
-
Visit the Kaggle website and select a beginner-friendly data cleaning dataset.
-
Load the dataset into your preferred data analysis tool.
-
Identify and address missing values, outliers, and duplicates.
-
Transform the data using techniques like normalization and one-hot encoding.
Three other activities
Expand to see all activities and additional details
Show all six activities
Discussion Forum Participation
Show steps
Engage with your peers and instructors to clarify concepts and share insights.
Show steps
-
Actively participate in discussion forums.
-
Ask questions to seek clarification on course topics.
-
Share your understanding and insights to help others.
Follow a Tutorial on Advanced Data Transformation Techniques
Show steps
Deepen your understanding of complex data transformation techniques.
Browse courses on
Data Transformation
Show steps
-
Identify a topic related to advanced data transformation, such as dimensionality reduction or feature scaling.
-
Search for tutorials or articles that cover the chosen topic.
-
Follow the tutorial step-by-step, applying the techniques to a sample dataset.
-
Experiment with different parameters and settings to observe the impact on data.
Participate in a Data Science Hackathon
Show steps
Test your skills and gain practical experience in a competitive environment.
Show steps
-
Identify a data science hackathon aligned with your interests.
-
Form a team or work independently.
-
Develop a solution to the hackathon challenge.
-
Present your findings and compete for recognition.
Review Data Science For Dummies, 2nd Edition
Show steps
Review the fundamentals of data science from a beginner-friendly reference text.
View
Artificial Intelligence For Dummies (For...
on Amazon
Show steps
Show steps
Show steps
- Obtain a copy of Data Science For Dummies, 2nd Edition.
- Read chapters 1-4 to gain an overview of data science concepts.
- Review highlighted sections on data cleaning techniques.
- Take notes or summarize key concepts for better retention.
Organize Course Materials and Resources
Show steps
Create a structured system for storing and accessing course materials to enhance your learning experience.
Show steps
Show steps
Show steps
- Create a dedicated folder or notebook for course materials.
- Organize materials by topic or type (e.g., lecture notes, assignments, readings).
- Establish a consistent naming convention for files.
Practice Data Cleaning Exercises from Kaggle
Show steps
Apply data cleaning techniques to real-world datasets, reinforcing your understanding.
Show steps
Show steps
Show steps
- Visit the Kaggle website and select a beginner-friendly data cleaning dataset.
- Load the dataset into your preferred data analysis tool.
- Identify and address missing values, outliers, and duplicates.
- Transform the data using techniques like normalization and one-hot encoding.
Three other activities
Expand to see all activities and additional details
Show all six activities
Discussion Forum Participation
Show steps
Engage with your peers and instructors to clarify concepts and share insights.
Show steps
Show steps
Show steps
- Actively participate in discussion forums.
- Ask questions to seek clarification on course topics.
- Share your understanding and insights to help others.
Follow a Tutorial on Advanced Data Transformation Techniques
Show steps
Deepen your understanding of complex data transformation techniques.
Browse courses on
Data Transformation
Show steps
Show steps
Show steps
- Identify a topic related to advanced data transformation, such as dimensionality reduction or feature scaling.
- Search for tutorials or articles that cover the chosen topic.
- Follow the tutorial step-by-step, applying the techniques to a sample dataset.
- Experiment with different parameters and settings to observe the impact on data.
Participate in a Data Science Hackathon
Show steps
Test your skills and gain practical experience in a competitive environment.
Show steps
Show steps
Show steps
- Identify a data science hackathon aligned with your interests.
- Form a team or work independently.
- Develop a solution to the hackathon challenge.
- Present your findings and compete for recognition.
Career center
Learners who complete Data Cleaning and Processing for Data Scientists will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists are in high demand as businesses increasingly rely on data to make decisions. This course will teach you the skills you need to clean and process data, which is a critical part of the data science process. By taking this course, you'll be well-positioned for a successful career as a Data Scientist.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to help businesses make better decisions. This course will teach you the skills you need to clean and process data, which is a critical part of the data analysis process. By taking this course, you'll be well-positioned for a successful career as a Data Analyst.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models. This course will teach you the skills you need to clean and process data, which is a critical part of the machine learning process. By taking this course, you'll be well-positioned for a successful career as a Machine Learning Engineer.
Business Analyst
Business Analyst
Business Analysts use data to help businesses make better decisions. This course will teach you the skills you need to clean and process data, which is a critical part of the business analysis process. By taking this course, you'll be well-positioned for a successful career as a Business Analyst.
Data Engineer
Data Engineer
Data Engineers build and maintain data pipelines. This course will teach you the skills you need to clean and process data, which is a critical part of the data engineering process. By taking this course, you'll be well-positioned for a successful career as a Data Engineer.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. This course will teach you the skills you need to clean and process data, which is a critical part of the statistical process. By taking this course, you'll be well-positioned for a successful career as a Statistician.
Data Architect
Data Architect
Data Architects design and build data systems. This course will teach you the skills you need to clean and process data, which is a critical part of the data architecture process. By taking this course, you'll be well-positioned for a successful career as a Data Architect.
Database Administrator
Database Administrator
Database Administrators manage and maintain databases. This course will teach you the skills you need to clean and process data, which is a critical part of the database administration process. By taking this course, you'll be well-positioned for a successful career as a Database Administrator.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course will teach you the skills you need to clean and process data, which is a common task for Software Engineers. By taking this course, you'll be well-positioned for a successful career as a Software Engineer.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve business problems. This course will teach you the skills you need to clean and process data, which is a critical part of the operations research process. By taking this course, you'll be well-positioned for a successful career as an Operations Research Analyst.
Financial Analyst
Financial Analyst
Financial Analysts use data to make investment decisions. This course will teach you the skills you need to clean and process data, which is a critical part of the financial analysis process. By taking this course, you'll be well-positioned for a successful career as a Financial Analyst.
Market Researcher
Market Researcher
Market Researchers collect and analyze data to understand consumer behavior. This course will teach you the skills you need to clean and process data, which is a critical part of the market research process. By taking this course, you'll be well-positioned for a successful career as a Market Researcher.
Product Manager
Product Manager
Product Managers develop and manage products. This course will teach you the skills you need to clean and process data, which is a common task for Product Managers. By taking this course, you'll be well-positioned for a successful career as a Product Manager.
Project Manager
Project Manager
Project Managers plan and execute projects. This course will teach you the skills you need to clean and process data, which is a common task for Project Managers. By taking this course, you'll be well-positioned for a successful career as a Project Manager.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use data to help businesses make better decisions. This course will teach you the skills you need to clean and process data, which is a critical part of the business intelligence process. By taking this course, you'll be well-positioned for a successful career as a Business Intelligence Analyst.
For more career information including salaries, visit:
OpenCourser.com/course/3nkkzn/data
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 Cleaning and Processing for Data Scientists.
Save
Offers a practical approach to data cleaning and transformation, focusing on real-world examples and case studies.
Explores advanced data cleaning techniques using machine learning algorithms, providing insights into automated data cleaning approaches.
Save
Focuses on data cleaning in the R programming language, offering a practical guide to data manipulation and transformation.
Provides a comprehensive overview of data cleaning and transformation using the Pandas library in Python.
Delves into advanced data cleaning techniques, including outlier detection, feature engineering, and data integration.
Emphasizes the importance of data quality in data science and provides guidance on data cleaning and validation.
Combines theoretical concepts with practical examples to demonstrate data cleaning techniques for machine learning applications.
Presents a step-by-step guide to data cleaning and transformation using Python, including code snippets and examples.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/3nkkzn/data
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Time
34 hours
Level
Intermediate
Via
Pluralsight
Instructor
Saravanan Dhandapani
Language
English
Good to know
Develops data cleaning techniques used in industry, which are foundational for real-world application
Leverages cloud services and AI tools, which are in high demand for skilled data scientists
Teaches data transformation techniques used in the industry, which augments the cleaning techniques to make the data ready for analysis
Taught by instructors with a strong reputation, which adds credibility to the material
Focuses heavily on cleaning and processing tasks for data scientists, which aligns with the target audience
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