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The Data Preprocessing for Data Science course is a comprehensive introduction to the essential steps in preparing data for analysis and machine learning. This course covers key techniques and tools used to clean, transform, and reduce data, ensuring it is in the best possible shape for creating accurate and reliable models. This course will provide you with practical experience using Python and popular libraries like NumPy and scikit-learn.
Register for this course and see more details by visiting:
OpenCourser.com/course/qu8q3u/data
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What's inside
Learning objectives
- Understand how to import datasets from various sources, focusing on csv files and how to manage different file structures.
- The concepts of domain and range in data science.
- To split data into training and testing sets.
- Determine the accuracy of your machine learning models.
- Apply min-max scaling and z-score standardization.
- Using domain reduction to reduce the size of your data's domain.
- Use pca for dimensionality reduction.
- Find hidden patterns in your data using factor analysis.
- Visualize high-dimensional data using t-sne.
- Understand how to import datasets from various sources, focusing on csv files and how to manage different file structures.
- The concepts of domain and range in data science.
- To split data into training and testing sets.
- Determine the accuracy of your machine learning models.
- Apply min-max scaling and z-score standardization.
- Using domain reduction to reduce the size of your data's domain.
- Use pca for dimensionality reduction.
- Find hidden patterns in your data using factor analysis.
- Visualize high-dimensional data using t-sne.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Builds a foundation for analyzing and manipulating data in a comprehensive and easy-to-understand fashion
Covers key data science concepts such as data cleaning, transformation, and reduction
Suitable as a companion to machine learning and deep learning courses, helping students prepare their data for more advanced analysis
Provides practical skills and experience using Python and popular libraries
Delves into advanced data reduction techniques like domain reduction, PCA, and factor analysis, which are essential in high-dimensional data analysis
Requires prerequisite knowledge in Python and data science concepts
Save this course
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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 Preprocessing for Data Science with these
activities:
Review linear algebra
Show steps
Refresh your knowledge of linear algebra concepts to strengthen your mathematical foundation for data science
Browse courses on
Linear Algebra
Show steps
-
Review matrix operations and vector spaces
-
Practice solving linear equations and systems
Review Python basics
Show steps
Review fundamental Python concepts to strengthen your programming foundation
Browse courses on
Python Basics
Show steps
-
Read through Python documentation
-
Practice writing basic Python scripts
Data cleaning exercises
Show steps
Practice data cleaning techniques to improve your data manipulation skills
Browse courses on
Data Cleaning
Show steps
-
Use pandas to handle missing values and outliers
-
Apply data transformation techniques to normalize your data
Four other activities
Expand to see all activities and additional details
Show all seven activities
Data analysis peer review
Show steps
Engage in peer review sessions to enhance your data analysis and communication skills
Browse courses on
Data Analysis
Show steps
-
Form a study group with fellow learners
-
Present your data analysis findings to the group
-
Provide feedback and engage in discussions
Data visualization project
Show steps
Create data visualizations to explore and present your findings effectively
Browse courses on
Visualization Techniques
Show steps
-
Choose a dataset and explore it
-
Select appropriate visualization techniques
-
Implement visualizations using Python libraries
Machine learning algorithm exercises
Show steps
Practice implementing and evaluating different machine learning algorithms to enhance your modeling skills
Browse courses on
Machine Learning Algorithms
Show steps
-
Use scikit-learn to train and test supervised learning models
-
Experiment with unsupervised learning techniques like clustering and dimensionality reduction
TensorFlow tutorials
Show steps
Follow guided tutorials to develop skills in using TensorFlow for deep learning
Browse courses on
TensorFlow
Show steps
-
Set up your development environment
-
Follow TensorFlow tutorials on image classification or natural language processing
-
Experiment with different hyperparameters and architectures
Review linear algebra
Show steps
Refresh your knowledge of linear algebra concepts to strengthen your mathematical foundation for data science
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review matrix operations and vector spaces
- Practice solving linear equations and systems
Review Python basics
Show steps
Review fundamental Python concepts to strengthen your programming foundation
Browse courses on
Python Basics
Show steps
Show steps
Show steps
- Read through Python documentation
- Practice writing basic Python scripts
Data cleaning exercises
Show steps
Practice data cleaning techniques to improve your data manipulation skills
Browse courses on
Data Cleaning
Show steps
Show steps
Show steps
- Use pandas to handle missing values and outliers
- Apply data transformation techniques to normalize your data
Four other activities
Expand to see all activities and additional details
Show all seven activities
Data analysis peer review
Show steps
Engage in peer review sessions to enhance your data analysis and communication skills
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Form a study group with fellow learners
- Present your data analysis findings to the group
- Provide feedback and engage in discussions
Data visualization project
Show steps
Create data visualizations to explore and present your findings effectively
Browse courses on
Visualization Techniques
Show steps
Show steps
Show steps
- Choose a dataset and explore it
- Select appropriate visualization techniques
- Implement visualizations using Python libraries
Machine learning algorithm exercises
Show steps
Practice implementing and evaluating different machine learning algorithms to enhance your modeling skills
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Use scikit-learn to train and test supervised learning models
- Experiment with unsupervised learning techniques like clustering and dimensionality reduction
TensorFlow tutorials
Show steps
Follow guided tutorials to develop skills in using TensorFlow for deep learning
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Set up your development environment
- Follow TensorFlow tutorials on image classification or natural language processing
- Experiment with different hyperparameters and architectures
Career center
Learners who complete Data Preprocessing for Data Science will develop knowledge and skills
that may be useful to these careers:
For more career information including salaries, visit:
OpenCourser.com/course/qu8q3u/data
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Effort
5 to 7 hours per week for 4 weeks
Level
Introductory
Via
edX
Institutions
University System of Maryland
University of Maryland Baltimore County
Session
June 15, 2024
- December 31, 2024
Language
English
Transcript
English
Good to know
Builds a foundation for analyzing and manipulating data in a comprehensive and easy-to-understand fashion
Covers key data science concepts such as data cleaning, transformation, and reduction
Suitable as a companion to machine learning and deep learning courses, helping students prepare their data for more advanced analysis
Provides practical skills and experience using Python and popular libraries
Delves into advanced data reduction techniques like domain reduction, PCA, and factor analysis, which are essential in high-dimensional data analysis
Requires prerequisite knowledge in Python and data science concepts
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