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Data Preprocessing for Data Science

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.

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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.

Traffic lights

Read about what's good
what should give you pause
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

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Reviews summary

Practical data preprocessing for data science

According to learners, this course provides a solid and practical foundation for data preprocessing, highly emphasizing hands-on experience with Python, NumPy, and scikit-learn. Many commend the instructor's clear explanations, especially for complex topics like PCA and t-SNE, making them accessible. While it excels at building core skills for those new to the field or looking to solidify basics, some advanced learners found the depth of coverage on advanced topics or mathematical intuition to be a bit superficial. A few also noted that some prior basic Python and statistical knowledge would be beneficial for a smoother learning experience.
Well-paced for beginners, slow for advanced learners.
"A perfect course for understanding data preprocessing from scratch."
"I found this course somewhat basic for my needs... The pacing was also a bit slow for me in the initial modules."
"It feels more suited for absolute beginners."
Strong foundation, but may lack advanced depth.
"A solid course covering essential data preprocessing techniques."
"The course has good concepts but sometimes the explanations weren't deep enough, especially when it came to the mathematical intuition behind methods like PCA."
"It's a good starting point, but I wouldn't expect to master everything without additional practice."
Assignments are challenging and reinforce learning.
"The assignments were challenging yet rewarding."
"The quizzes helped reinforce learning."
"The coding exercises were helpful but could have used more challenging scenarios."
Instructor clarifies complex topics effectively.
"The instructor explained complex topics like PCA and t-SNE in a very clear and understandable manner."
"Fantastic instructor! Really made data cleaning and transformation approachable."
"Every topic was broken down wonderfully."
Emphasizes practical application with coding.
"The hands-on exercises using Python, NumPy, and scikit-learn were incredibly practical and helped solidify my understanding."
"The emphasis on practical application using scikit-learn was a major benefit."
"I learned how to use practical tools and strategies that I could apply immediately to my work."
Some prior Python/statistical knowledge helps.
"I might need prior basic Python knowledge for smoother sailing."
"The instructor occasionally assumed too much prior knowledge, particularly in statistical concepts."
"I sometimes found myself needing to look up additional documentation."

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
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
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
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
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
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
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
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

Career center

Learners who complete Data Preprocessing for Data Science will develop knowledge and skills that may be useful to these careers:

Reading list

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Course image
Effort
5 to 7 hours per week for 4 weeks
Level
Introductory
Via
edX
Institutions
University System of Maryland
University of Maryland Baltimore County
Language
English
Transcript
English
Traffic lights
Read about what's good
what should give you pause
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
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