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Learn Jupyter Notebooks, earn certificates with free online courses from Stanford, University of Pennsylvania, University of Michigan, UC San Diego and other top universities around the world. Read reviews to decide if a class is right for you.
Learn Python fundamentals, data manipulation with pandas, and basic statistical analysis. Gain practical skills in cleaning, processing, and analyzing tabular data for data science applications.
Learn Python-based data analysis using Numpy, Pandas, and visualization libraries. Covers data cleaning, manipulation, and visualization techniques for extracting insights from various data sources.
Using Python, learn statistical and probabilistic approaches to understand and gain insights from data.
Comprehensive tutorial on data analysis using Python libraries like NumPy, Pandas, Matplotlib, and Seaborn. Covers data processing, visualization, and cleaning with practical examples and exercises for beginners.
Master Python fundamentals through hands-on coding, from basic syntax to advanced concepts like OOP, while building real projects and learning essential computer science principles and Git version control.
Master Python programming, data science, and machine learning fundamentals through hands-on projects. From basic syntax to advanced regression analysis, build practical skills in data visualization and predictive modeling.
Master Python data science fundamentals through hands-on projects, from basic programming concepts to advanced machine learning techniques like regression analysis, classification, and K-nearest neighbors.
Master Python programming fundamentals through advanced data science techniques, from data handling to machine learning, using NumPy, Pandas, Matplotlib, and scikit-learn.
Explore linear algebra fundamentals and their applications in machine learning, covering vectors, matrices, eigenvalues, and practical implementations in Python for data-driven tasks.
Explore inferential statistics using Python, covering confidence intervals and hypothesis testing for various population parameters. Apply concepts to real-world case studies using statistical libraries.
Comprehensive introduction to Python programming, covering core concepts, data structures, and file operations with hands-on exercises and practical applications.
Learn Python-based data analysis techniques, including data manipulation with pandas, visualization with matplotlib, and practical skills for loading, querying, and summarizing real-world datasets.
Explore Jupyter notebooks, cloud-hosted environments, Python microservices, and command-line tools for data engineering tasks. Build and deploy scalable solutions for machine learning and data processing.
Learn Python basics for data science: variables, data types, control structures, and functions. Practice in Jupyter Notebook to build a foundation for programming and data analysis.
Explore essential data science tools, languages, and platforms. Gain hands-on experience with Jupyter, RStudio, Git, and more. Develop practical skills for data analysis, visualization, and collaboration.
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