5 strategies for converting Big Data into actionable insights

5 strategies for converting Big Data into actionable insights

The strategy to turn the raw data into actionable insights is to integrate and analyze data from all data sources to reach better and optimized business decisions. The word “big” in big data refers to the huge volume of data involved. Big data technologies aim at storing, analyzing, querying, and updating large chunks of data coming from ...

Data Science x Project Planning

Data Science x Project Planning

A non-technical guide to k-NN algorithm and its application on forecasting, from a project planning point of view, and for beginners. Introduction The intended audience for this short blog post are data science practitioners who seek to implement predictive algorithms in a business-project-based setting, with special focus on presenting the work ...

Using Excel for Data Entry

Using Excel for Data Entry

This article shows you how to enter data so that you can easily open in statistics packages such as R, SAS, SPSS, or jamovi (code or GUI steps below). Excel has some statistical analysis capabilities, but they often provide incorrect answers. For a comprehensive list of these limitations, ...

Dask Release 0.17.2

Dask Release 0.17.2

This work is supported by Anaconda Inc. and the Data Driven Discovery Initiative from the Moore Foundation. I’m pleased to announce the release of Dask version 0.17.2. This is a minor release with new features and stability improvements. This blogpost outlines notable changes since the 0.17.0 release on February 12th. You can conda install ...

The Compression of the Hype Cycle

The Compression of the Hype Cycle

I spend a lot of time thinking about hype cycles, across industries (Big Data/AI, IoT) and ecosystems (New York). Whether you use the Carlota Perez surge cycle (see this great Fred Wilson post) or the Gartner version, hype cycles convey the fundamental idea that technology markets don’t develop linearly, but instead go through phases of ...

When shuffling large arrays, how much time can be attributed to random number generation?

When shuffling large arrays, how much time can be attributed to r...

It is well known that contemporary computers don’t like to randomly access data in an unpredictible manner in memory. However, not all forms of random accesses are equally harmful. To randomly shuffle an array, the textbook algorithm, often attributed to Knuth, is simple enough: void swap(int arr, int i, int j) { int tmp = arr; arr = ...

R Tip: Use let() to Re-Map Names

R Tip: Use let() to Re-Map Names

Another Rtip. Need to replace a name in some R code or make R code re-usable? Use . Here is an example involving . Let’s look at some example data: library("dplyr") library("wrapr") starwars %>% select(., name, homeworld, species) %>% head(.) # # A tibble: 6 x 3 # name homeworld species # <chr> ...

Generational changes in support for gun laws

Generational changes in support for gun laws

This is the fourth article in a series about changes in support for gun control laws over the last 50 years. In the first article I looked at data from the General Social Survey and found that young adults are less likely than previous generations to support gun control. In the second article I looked at data from the CIRP Freshman Survey ...

AI Investor Reverse Pitch: Fund Your Startup or Showcase Your Product

AI Investor Reverse Pitch: Fund Your Startup or Showcase Your Pro...

AI Investor Reverse Pitch The areas of data science and artificial intelligence are hot commodities in which organizations, VCs and even individuals actively seek to invest. While start-ups usually do the pitching of their idea, product and vision to investor in order to gain fund, it is a rarer site to see investor groups pitch their position in ...