This course was created for individuals with data processing experience who want to perform more advanced work, and get into programming with R and the tidyverse. No prior experience required! We’ll guide you step by step in this affordable introduction to R for Data Analysis. Go at your own pace, anytime, anywhere. No rigid schedules!You’re never alone—our instructors are here to help.

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This course is designed to take your R programming expertise to the next level, building on the foundations laid in the Introduction to R for Data Analysis course. Dive into advanced techniques and powerful tools that will transform your data workflows and analytical capabilities. In this course, you will learn how to write your own custom R functions, reduce redundancy in your code by learning iteration techniques with the purrr package, refactor column variables, reshape your data frames, and join multiple data frames together.

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No prior coding experience required! We’ll guide you step by step in this affordable introduction to Python. Learn how to analyze real-world data using the powerful pandas library. Go at your own pace, anytime, anywhere. You’re never alone—our instructors are here to help.

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Making accurate predictions is a powerful skill when working with real-world data. This course teaches you how to frame research questions as prediction problems and apply foundational machine learning techniques. We'll explore a range of algorithms, including linear and logistic regression, decision trees, random forests, XGBoost, and simple neural networks. By the end of the course, you'll be equipped to build, evaluate, and interpret predictive models, empowering you to uncover patterns in data and make informed, data-driven decisions. Learn skills such as Introduction to Machine Learning, Least Squares for Continuous Response Prediction, Logistic Regression for Binary Responses, Decision Tree Algorithms, and Neural Networks.

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Data cleaning is an incredibly important part of the data science process. This course introduces a customizable data cleaning pipeline, focusing on biomedical data from electronic health records and health survey data. In this course, you will learn how to summarize the data collection process and data dictionaries, identify and address missing values using R, handle data quality issues (such as invalid and inconsistent values) using R, reshape your data into a “tidy” format using R, and create a custom, reproducible data cleaning function in R.

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In this course, you’ll learn to apply statistical methods to real data problems, transforming how you analyze and interpret data. Enhance your ability to make data-driven decisions by implementing classical and modern statistical techniques, including hypothesis testing, confidence intervals, and linear regression. Bridge the gap between theoretical and practical applications of statistics in R, gaining hands-on experience. Refine your statistical intuition.

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Data science drives innovation, but ethics are crucial to ensure fairness, transparency, and accountability. This course explores the ethical challenges around data collection, sharing, and analysis, helping you navigate real-world problems with integrity. Learn to understand ethical data collection and share personal data responsibly, including obtaining informed consent and safeguarding privacy. Evaluate automated decision-making systems' fairness and impact on diverse groups. Learn best practices for protecting sensitive data from unauthorized access while balancing the need for insights.Develop practical tools by creating checklists and frameworks to guide ethical decision-making in data collection, sharing, and algorithm development.

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Unsupervised learning and data mining is about identifying the native structure in data through clustering, dimensionality reduction, and nearest neighbor search. Unlike supervised machine learning, unsupervised learning methods cannot rely on labels to aid in predictions. Nevertheless these methods are an essential part of a data scientist's took box. Learners will discover various touchstone methods within each category of technique, including both linear and non-linear methods for dimensionality reduction, and center-based (e.g., k-means) and density-based clusters. They will be able to identify pros and cons for each option to decide which is best for their scenario.

Python will be used as the programming language, and learners will gain familiarity with standard libraries that make applying these methods simple. It is recommended that students have some background in Python, such as completing Introduction to Python for Data Analysis.

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