Hilarious Jokes & Videos on Statistics and Data Science

If you love data science, you’d find many aspects to it. A month back, I found 10 Best Movies on Machine Learning. A week later, I found 7 Documentaries on Statistics. It’s time to explore the funny side of analytics. I’ve compiled a list of best hilarious jokes (including images, videos) based on numbers, statistics, big data, machine learning. Hope you’d enjoy reading them. During this process, I found many jokes. In fact too many. Hence, I’ve listed the best ones I liked.


Exploring US Mass Shootings in R

Mass shootings have become a hot topic recently, so I found some data to explore on the website Shooting Tracker, which has recorded Mass Shootings from 2013 to present. Each incident is cited by a news source, and is defined as having 4 or more victims, either killed or injured.


d3.compose

Compose complex, data-driven visualizations from reusable charts and components with d3.
• Get started quickly with standard charts and components
• Layout charts and components automatically
• Powerful foundation for creating custom charts and components


Learning from Hurricanes: Big Data Analytics, Risk, & Data Visualization

This year, Florida has experienced its 10th consecutive year without a hurricane. It is the longest period without a hurricane strike in modern times and one more remarkable considering that Florida’s more then 1200 miles of coastline account for about 40% of the US landed hurricanes recorded in modern history. Exploring this long stretch without hurricanes is worthy of some examination, as it offers us many lessons in Big Data Analytics, Risk, and Data Visualization. First, the obvious: how frequent are hurricanes and are hurricanes regular in their arrival?


20 Lessons From Building Machine Learning Systems

In nutshell, choose the right metric; be thoughtful about your data. Understand dependencies between data and models and optimize only what matters. Make sure you teach your model what you want it to learn. Ensembles and the combination of supervised/unsupervised techniques are key in many ML applications. It is important to focus on feature engineering. Be thoughtful about your ML infrastructure/tools and organizing your teams.


50 Useful Machine Learning & Prediction APIs

We present a list of 50 APIs selected from areas like machine learning, prediction, text analytics & classification, face recognition, language translation etc. Start consuming APIs!


Deep Learning Transcends the Bag of Words

Generative RNNs are now widely popular, many modeling text at the character level and typically using unsupervised approach. Here we show how to generate contextually relevant sentences and explain recent work that does it successfully.


Free Data Science Curriculum

This free data science curriculum is organized into 5 units and a capstone project spread over 12 weeks, and is made up entirely of freely-available online resources. Units cover probability and statistics, data analysis, intro to R, data visualization, data wrangling, and analytics. Maybe you can benefit from this!


Fun with ddR: Using Distributed Data Structures in R

A few weeks ago, we revealed ddR (Distributed Data-structures in R), an exciting new project started by R-Core, Hewlett Packard Enterprise, and others that provides a fresh new set of computational primitives for distributed and parallel computing in R. The package sets the seed for what may become a standardized and easy way to write parallel algorithms in R, regardless of the computational engine of choice. In designing ddR, we wanted to keep things simple and familiar. We expose only a small number of new user functions that are very close in semantics and API to their R counterparts. You can read the introductory material about the package here. In this post, we show how to use ddR functions.


Document your hike with interactive leaflets and R

Maarten Hermans is a sociologist and researcher at KU Leuven in Belgium and an avid hiker. He uses an Android app to track his location and elevation on his hikes, which means he can download his hike data in GPS Exchange Format. With this data and a few R packages, he was then able to create interactive topological maps including his route and photos along the way.


Building Shiny apps – an interactive tutorial

This tutorial is a hands-on activity complement to a set of presentation slides for learning how to build Shiny apps. In this activity, we’ll walk through all the steps of building a Shiny app using a dataset that lets you explore the products available at the BC Liquor Store. The final version of the app, including a few extra features that are left as exercises for the reader, can be seen here. Any activity deemed as an exercise throughout this tutorial is not mandatory for building our app, but they are good for getting more practice with Shiny.


4 Data Processing Architectures of Web Companies

Have you struggled in your data science function because of underlying data processing issues? Here is the list of 4 data processing architecture of top web companies to help you overcome those issues.


Top 10 Machine Learning Algorithms

1.Linear regression
2.Logistic regression
3.k-means
4.SVMs
5.Random Forests
6.Matrix Factorization/SVD
7.Gradient Boosted Decision Trees/Machines
8.Naive Bayes
9.Artificial Neural Networks
10.For the last one I’d let you pick one of the following:
11.Bayesian Networks
12.Elastic Nets
13.Any other clustering algo besides k-means
14.LDA
15.Conditional Random Fields
16.HDPs or other Bayesian non-parametric model