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Incarceration: Are Prisons a Suitable Punishment?
Mining Massive Datasets
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Learn Data Wrangling, earn certificates with free online courses from Harvard, Johns Hopkins, Duke, University of Illinois and other top universities around the world. Read reviews to decide if a class is right for you.
Master data analysis skills, Python programming, and practical statistics to launch a successful career in data science with hands-on projects and industry-relevant curriculum.
Learn the data analysis process of questioning, wrangling, exploring, analyzing, and communicating data. You will work with data in Python using libraries like NumPy and pandas.
Learn about the data analysis process, key steps in data wrangling, basic data types, summary statistics, how to design good data visualizations, common types of charts and plots, and how to tell a story with your visuals.
Learn about the big data ecosystem and how to use Spark to work with massive datasets. Learners will also store big data in a data lake and develop Lakehouse architecture on the Azure Databricks platform.
Master advanced data manipulation techniques using Pandas and NumPy, from complex grouping operations to sophisticated preprocessing, scaling, and analysis methods for extracting meaningful insights from datasets.
Master data wrangling techniques with tidyr to reshape, clean, and transform messy datasets into organized structures. Learn to handle missing values, pivot data frames, and prepare data for comprehensive analysis.
Master essential data manipulation and analysis using NumPy for numerical computing and Pandas for data wrangling, from basic array operations to advanced transformations and complex data analysis techniques.
Master data manipulation techniques in R using dplyr to filter, select, summarize, group, mutate, and reshape datasets effectively for advanced data analysis and transformation.
Master data transformation techniques in R to efficiently reshape, manipulate, and analyze datasets using tidyverse packages, enhancing your ability to handle complex data structures and conduct exploratory analyses.
Explore social network structures, dynamics, and analysis techniques. Learn to visualize and predict network evolution using computational methods and software tools.
Explore data visualization techniques for journalism, from chart selection to interactive storytelling. Learn to create compelling visuals using Python, applying design theories and cognitive principles.
Comprehensive R-based data analysis program covering probability, inference, regression, and Bayesian statistics. Develop practical skills for data visualization, wrangling, and reproducible reporting.
Comprehensive R-based data analysis training: from probability and statistics to regression modeling, with emphasis on practical application and effective communication of results.
Master data analysis through R programming, from basic probability to advanced statistical modeling, while learning to create compelling visualizations and reproducible reports for informed decision-making.
Master SQL for data science: clean, analyze, and interpret complex datasets. Apply skills to real-world case studies, including AB testing, for actionable insights.
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