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Learn Applied Analytics, earn certificates with free online courses from Stanford, MIT, University of Pennsylvania, University of Michigan and other top universities around the world. Read reviews to decide if a class is right for you.
Apply deep learning to medicine: explore health data standards, machine learning basics, and deep neural networks through labs and a healthcare modeling project.
Learn how probability, math, and statistics can be used to help baseball, football and basketball teams improve, player and lineup selection as well as in game strategy.
Learn how data informs sports front offices: player valuation and roster management, athlete representation frameworks, facility and event operations, and career paths across the sports industry.
Apply data analysis to sports roster decisions: drafting, scouting, trades and free agency, guided by the collective bargaining agreement and standard player contract across the 12-month calendar.
Apply an intersectional gender lens to a real-world case: analyze quantitative and qualitative evidence, reframe the problem, prototype a solution, and build a change plan.
Follow healthcare data along the Path to Value: compare star schema, hierarchical and relational models, normalize and integrate data across systems, and validate it for quality.
Master Python Programming Fundamentals and Harness the Power of ML to Solve Real-World Practical Applications in Finance
Build data structures for common healthcare problems: apply groupers to map medical codes, run ETL to harmonize multi-source data, and document sources in a data dictionary.
Build Python skills for finance, analyze financial data, apply machine learning to create predictive models, and automate trading strategies in this certificate program.
Intro to Health Data, sources, features, issues: insurance claims, EHR, diagnosis, procedure, drug codes
The course teaches tools such as analytics, text mining, sentiment analysis, and data visualization required to process the data in order to gain insight for financial market applications.
This course prepares you to deal with advanced clinical data science topics and techniques including temporal and research quality analysis.
This course teaches you the fundamentals of transforming clinical practice using predictive models. This course examines specific challenges and methods of clinical implementation, that clinical data scientists must be aware of when developing their pred…
Examine how AI turns healthcare data into decisions: the DIAD pipeline, analytics, machine learning algorithms, data governance, and workflow integration across clinical and administrative functions.
Financial Analysis, Time Series Analysis, Portfolio Optimization, CAPM, Algorithmic Trading, Q-Learning, and MORE!
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