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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.

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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.

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

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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
Refresh your knowledge of linear algebra concepts to strengthen your mathematical foundation for data science
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  • Review matrix operations and vector spaces
  • Practice solving linear equations and systems
Review Python basics
Review fundamental Python concepts to strengthen your programming foundation
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  • Read through Python documentation
  • Practice writing basic Python scripts
Data cleaning exercises
Practice data cleaning techniques to improve your data manipulation skills
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  • Use pandas to handle missing values and outliers
  • Apply data transformation techniques to normalize your data
Four other activities
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Data analysis peer review
Engage in peer review sessions to enhance your data analysis and communication skills
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  • 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
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  • 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
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  • 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
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  • 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:

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