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[title] => Data Science with R: Data Analysis and Visualization
[excerpt] => This course is a 35-hour program designed to provide a comprehensive introduction to R. You’ll learn how to load, save, and transform data as well as how to write functions, generate graphs, and fit basic statistical models with data. In addition to a theoretical framework in which you will learn the process of data analysis, this course focuses on the practical tools needed in data analysis and visualization. By the end of the course, you will have mastered the essential skills of processing, manipulating and analyzing data of various types, creating advanced visualizations, generating reports, and documenting your codes.
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[title] => Data Science with Python: Machine Learning
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[title] => Data Science with Python: Data Analysis and Visualization
[excerpt] => This class is a comprehensive introduction to data science with Python programming language. This class targets people who have some basic knowledge of programming and want to take it to the next level. It introduces how to work with different data structures in Python and covers the most popular data analytics and visualization modules, including numpy, scipy, pandas, matplotlib, and seaborn. We use Ipython notebook to demonstrate the results of codes and change codes interactively throughout the class.
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[title] => Introductory Python
[excerpt] => This is a class for computer-literate people with no programming background who wish to learn basic Python programming. The course is aimed at those who want to learn “data wrangling” – manipulating downloaded files to make them amenable to analysis. We concentrate on language basics such as list and string manipulation, control structures, simple data analysis packages, and introduce modules for downloading data from the web.
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[title] => Designing and Implementing Production Machine Learning Systems (MLOps)
[excerpt] => This course is an introduction to ML systems in production that will demonstrate and give students exposure to how real production ML systems operate. Using Python, Docker, Kubernetes, Google Cloud and various open-source tools, students will bring the different components of an ML system to life and setup real, automated infrastructure.
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[title] => Data Science with R: Data Analysis and Visualization
[excerpt] => This course is a 35-hour program designed to provide a comprehensive introduction to R. You’ll learn how to load, save, and transform data as well as how to write functions, generate graphs, and fit basic statistical models with data. In addition to a theoretical framework in which you will learn the process of data analysis, this course focuses on the practical tools needed in data analysis and visualization. By the end of the course, you will have mastered the essential skills of processing, manipulating and analyzing data of various types, creating advanced visualizations, generating reports, and documenting your codes.
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[title] => Data Science with Python: Machine Learning
[excerpt] => This 20-hour Machine Learning with Python course covers all the basic machine learning methods and Python modules (especially Scikit-Learn) for implementing them. The five sessions cover: simple and multiple Linear regressions; classification methods including logistic regression, discriminant analysis and naive bayes, support vector machines (SVMs) and tree based methods; cross-validation and feature selection; regularization; principal component analysis (PCA) and clustering algorithms. After successfully completing of this course, you will be able to explain the principles of machine learning algorithms and implement these methods to analyze complex datasets and make predictions in Python.
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[title] => Data Science with Python: Data Analysis and Visualization
[excerpt] => This class is a comprehensive introduction to data science with Python programming language. This class targets people who have some basic knowledge of programming and want to take it to the next level. It introduces how to work with different data structures in Python and covers the most popular data analytics and visualization modules, including numpy, scipy, pandas, matplotlib, and seaborn. We use Ipython notebook to demonstrate the results of codes and change codes interactively throughout the class.
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[title] => Introductory Python
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[title] => Data Science with R: Data Analysis and Visualization
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[title] => Data Science with Python: Machine Learning
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[title] => Data Science with Python: Data Analysis and Visualization
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[title] => Introductory Python
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