A Gentle Introduction to Data Analytics in SQL Server 2016
Explore data analytics using SQL Server 2016, comparing it to BI and data science. Learn to organize data, use Power BI, R, and Analysis Services for exploration and visualization techniques.
Predictive Analytics Using R and SQL
Explore predictive analytics concepts, applications, and integration with BI environments. Learn data science processes, methodologies, and practical techniques using R and SQL for effective predictions.
Streams, Lakes and Oceans - Working with Big Data with Azure ML
Explore big data architectures with Azure ML Studio, covering integration, customization, and scaling for effective machine learning on large datasets.
Exploring StackOverflow Data
Discover insights from StackOverflow's data using machine learning. Learn how to process large-scale data functionally with R and F#, and see connections to cancer research.
Predicting the Future as a Service with Azure ML and R
Learn to create predictive analytics using Azure Machine Learning Studio. Explore data manipulation, R modules, publishing endpoints, and model retraining for easy, low-cost machine learning implementation.
Shiny - Creating Data-Centric Web Applications in Python
Discover how to build data dashboards and interactive visualizations using Shiny for Python - a framework that eliminates the need for HTML, CSS, and JavaScript while leveraging Transparent Reactive Programming concepts.
AWS 클라우드에서 모던 데이터 플랫폼과 분석환경 구축하기
Discover how SK Telecom built a modern data platform on AWS, transforming their on-premise Datalake to cloud, featuring integrated analytics tools like Datahub, Jupyter, and QuickSight for enhanced data processing capabilities.
Implementing Linear Discriminant Analysis (LDA) with Irish Data Sets in R
Implement LDA using Irish datasets in R, gaining practical experience with Linear Discriminant Analysis techniques.
K-Means Implementation in R
Implement K-means clustering algorithm in R programming language. Learn data preprocessing, algorithm application, and result interpretation for effective data analysis.
K-Nearest Neighbors Implementation in R
Implement K-Nearest Neighbors algorithm in R programming language. Learn practical application of this machine learning technique for classification and regression tasks.
Logistic Regression Implementation in R for Automotive Crash Testing
Implement logistic regression in R for automotive crash testing, from data preparation to model evaluation and prediction.
Interoperability between R and Other Languages
Explore techniques for integrating R with other programming languages, leveraging R's statistical libraries while developing in preferred environments. Learn about various interoperability options and tools.
The Future Roadmap for the Composable Data Stack
Explore advancements in data processing with Wes McKinney. Learn about Parquet, Arrow, and interface libraries like Ibis and tidyverse. Gain insights into current project states and future innovations.
Introduction to R for Data Analysis - 2024
Gain insights into R programming fundamentals for medical and population genetics data analysis through expert-led discussions of essential concepts and practical applications.
OnlineFDR Control with Applications to RNA-Seq Data
Explore onlineFDR control techniques for RNA-Seq data analysis, focusing on adaptive algorithms and their applications in multiple hypothesis testing scenarios.
Comprehensive Guide to Downstream Analysis for Single-Cell ATAC-Seq in R - scATAC-Seq Workflow
Comprehensive guide for downstream analysis of single-cell ATAC-Seq data using R and Signac. Covers integration with RNA-Seq, differential accessibility analysis, and visualization of genomic regions.
50 Ways to Show Your Data
Explore advanced data visualization techniques using R's ggplot2 package. Learn to create compelling diagrams beyond basic plots, including waterfalls, violins, and maps, with minimal code effort.
Multilingual Programming and Project Structure for Collaboration
Explore multilingual programming and project structures for effective collaboration. Learn to choose the right language for each problem and implement best practices in data science and MLOps.
Discrete Distributions in R - Lecture 6
Master discrete distributions through hands-on practice in R, exploring probability concepts and statistical analysis techniques for working with categorical data.
Introduction to Statistics: Independent and Identically Distributed Random Variables - Lecture 12
Explore fundamental statistical concepts including iid random variables, sample realizations, and the Central Limit Theorem while practicing implementations in R programming.
Confidence Intervals and Hypothesis Testing with R Examples - Lecture 16
Master statistical analysis through confidence intervals and hypothesis testing, with practical R programming examples for real-world data interpretation and decision-making.