Statistical Computing Courses and Certifications

Harvard CS50's Introduction to R Programming - Full University Course

1 rating

Master R programming fundamentals through Harvard's comprehensive introduction covering data representation, transformation, visualization, and package creation for statistical computing.

• freeCodeCamp
• 8 hours 49 minutes
• Self-Paced
• Free Video

R

Learn statistical computing with R, an open-source language designed for ease of use and powerful analysis. Covers basics to advanced topics like data manipulation, visualization, and statistical modeling.

• Exercism
• Self-Paced
• Free Online Course

Statistical Data Analysis using R

Master statistical data analysis fundamentals using R programming, from data organization and visualization to probability models, regression analysis, hypothesis testing, and time series documentation.

• Swayam
• 12 weeks
• Self-Paced
• Free Online Course

Discover how to create powerful R visualizations within SAP Analytics Cloud through hands-on demonstrations and interactive activities covering R code integration and advanced charting.

• SAP Learning
• 2 hours 17 minutes
• Self-Paced
• Free Online Course

Deep Generative Models - Maximum Likelihood Learning - Lecture 4

Explore maximum likelihood learning in deep generative models with Stanford's Stefano Ermon. Gain insights into advanced AI techniques and their applications in this comprehensive lecture.

• YouTube
• 1 hour 24 minutes
• Self-Paced
• Free Video

Great Learning

R Programming for Beginners - Learn R in 3 Hours

2 ratings

Comprehensive introduction to R programming, covering data types, manipulation, visualization, and neural networks. Includes hands-on examples and a Pokemon case study for practical application.

• YouTube
• 3 hours 29 minutes
• Self-Paced
• Free Video

The Royal Institution

The Future of Crime Detection and Prevention

Expert panel explores AI's role in predicting and preventing crime, discussing technological advancements, ethical concerns, and the future of law enforcement and security.

• YouTube
• 44 minutes
• Self-Paced
• Free Video

NPTEL-NOC IITM

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.

• YouTube
• 29 minutes
• Self-Paced
• Free Video

NPTEL-NOC IITM

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.

• YouTube
• 21 minutes
• Self-Paced
• Free Video

Julia Silge

Modeling Hotel Bookings in R Using Tidymodels and Recipes

Learn to predict hotel bookings using R, tidymodels, and recipes. Explore data, create visualizations, and build decision tree models to gain insights into booking patterns and improve predictions.

• YouTube
• 52 minutes
• Self-Paced
• Free Video

The Julia Programming Language

Probability via Computation - Week 4

Explore probability concepts through computational methods, covering random sampling, Monte Carlo simulations, Bernoulli trials, and the Central Limit Theorem with practical examples and visualizations.

• YouTube
• 26 minutes
• Self-Paced
• Free Video

Simons Institute

Optimal Iterative Algorithms for Problems With Random Data

Explore optimal iterative algorithms for statistical problems with random data, focusing on computational complexity and efficient solutions in statistical inference.

• YouTube
• 53 minutes
• Self-Paced
• Free Video

Alan Turing Institute

The Mother of All Representer Theorems for Inverse Problems and Machine Learning - Michael Unser

Explore the mathematical foundations of inverse problems and machine learning, focusing on representer theorems, Banach spaces, and kernel methods for advanced data science applications.

• YouTube
• 48 minutes
• Self-Paced
• Free Video

Alan Turing Institute

Affine Spline Insights into Deep Learning - Richard Baraniuk, Rice University

Explore deep learning through affine splines, understanding neural networks as matched filterbanks and partition-based signal distances. Gain insights into data augmentation and future research directions.

• YouTube
• 38 minutes
• Self-Paced
• Free Video

NPTEL-NOC IITM

Why Julia is the Future for Data Science Projects

Explore Julia's advantages for data science, including speed, economic viability, and reduced carbon footprint compared to R and Python.

• YouTube
• 34 minutes
• Self-Paced
• Free Video