Ecommerce sites generate tons of web server log data which can provide valuable insights through analysis. For example, if we know which users are more likely to buy a product, we can perform targeted marketing, improve relevant product placement on our site and lift conversion rates. However, raw web logs are often enormous and messy so preparing the data to train a predictive model is time consuming for data scientists.…
Added by Ayumi Owada on July 18, 2019 at 2:00pm —
Background: Civis began a data technology partnership with McDonald’s North America Marketing and Data Science teams in late 2017, and — after a year and a half of keeping our heads down — we jointly presented some of our key learnings recently at Advertising Week in New York.…
Added by Civis Analytics on March 6, 2019 at 8:00am —
Summary: True prescriptive analytics requires the use of real optimization techniques that very few applications actually use. Here’s a refresher on optimization with examples of where and how they’re best used.
Predictive analytics and optimization have gone hand in hand since the very beginning. But in… Continue
Added by William Vorhies on February 18, 2019 at 10:00am —
Wondering how the words, fashion, weather and predictive analytics are connected?
Here’s a poser – what is one of the biggest challenges before the global fashion industry today? Weather. You wouldn’t have guessed it, right?
Pick up any fashion magazine, read any fashion portal, white paper…. you name it, unpredictable weather is on the Top-5… Continue
Added by Hemant Warudkar on July 27, 2018 at 4:13am —
A smoothly running sensor data analytics tool may be just as difficult to manage as a symphony orchestra. Because every musician in an orchestra – and every part of an IoT system – needs to work properly and ‘harmonize’ with the others. But how do conductors make their orchestras work so nicely and sound so heavenly instead of creating a mismanaged cacophony? Obviously, there’s a lot of practice involved. But besides that, they definitely know what pitfalls they need to avoid. Which is why,… Continue
Added by imranali on July 7, 2018 at 4:30am —
R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises.
Learn the fundamentals of data analysis in the second edition of Data Analysis with R, authored by data scientist… Continue
Added by Packt Publishing on May 8, 2018 at 10:30pm —
Sales prediction is an important part of modern business intelligence. First approaches one can apply to predict sales time series are such conventional methods of forecasting as ARIMA and Holt-Winters. But there are several challenges while using these methods. They are: multilevel daily/weekly/monthly/yearly seasonality, many exogenous factors which impact sales, complex trends in different time periods. In such cases, it is not easy to apply conventional methods. Of course, there is… Continue
Added by Bohdan Pavlyshenko on March 8, 2018 at 9:00am —
Added by Peter Bruce on January 14, 2018 at 11:00am —
Finding out the difference between data scientists, data engineers, software engineers, and statisticians can be confusing and complicated. While all of them are linked to data in a way, there is an underlying difference between the work they do and manage.
The growth of data and its usage across… Continue
Added by Ronald van Loon on December 19, 2017 at 1:00am —
Summary: Which is more important, the data or the algorithms? This chicken and egg question led me to realize that it’s the data, and specifically the way we store and process the data that has dominated data science over the last 10 years. And it all leads back to Hadoop.
Recently I was challenged to speak on the role of data in data… Continue
Added by William Vorhies on November 28, 2017 at 10:36am —
One of the main goals in the Bitcoin analytics is price forecasting. There are many factors which influence the price dynamics. The most important factors are: the interaction between supply and demand, attractiveness for investors, financial and macroeconomics indicators, technical indicators such as difficulty, how many blocks were created recently, etc. A very important impact on the cryptocurrency price has trends…
Added by Bohdan Pavlyshenko on October 26, 2017 at 11:30pm —
Today, data scientists are generally divided among two languages — some prefer R, some prefer Python. I will not try to explain in this article which one is… Continue
Added by Marija Zoldin on September 26, 2017 at 10:00am —
Here is our list of featured articles and resources posted since Monday.
Added by Vincent Granville on September 21, 2017 at 8:30am —
Data is important. It is not a secret for anybody. We can even paraphrase famous saying mentioning that “who owns the data, owns the world”. And if you are a business person, you should know like no one else. Your activity can be changed for better if you use Big Data sources. Sales growth, clever marketing strategy - you can achieve it using Big Data. Let’s check it out what is Big Data and how you can make use of it.
Big Data - meaning and particularities
In fact, Big Data… Continue
Added by Nataliia Kharchenko on August 14, 2017 at 7:00am —
A long, long time ago (maybe 10 years) the data analytics industry was fairly easy to define and track. Back in that pre-historic era SAS was considered the gold standard of analytics companies with a comprehensive range of solutions addressing the demands of many industries. Given the relative paucity of data, analytics tended to focus on those industries that generated usable data. Companies that were part of the analytics universe back then would have included:
Added by Gregory Thompson on August 8, 2017 at 12:30pm —
Summary: A year ago we wrote about the emergence of fully automated predictive analytic platforms including some with true One-Click Data-In Model-Out capability. We revisited the five contenders from last year with one new addition and found the automation movement continues to move forward. We also observed some players from last year have now gone in different directions. …
Added by William Vorhies on July 17, 2017 at 4:30pm —
The following links describe a set of free SAS tutorials which help you to learn SAS programming online on your own. It includes tutorials for data exploration and manipulation, predictive modeling and some scenario based examples.
SAS (Statistical analysis system) is one of the most popular software for data analysis. It is widely used for various purposes such as data management, data mining, report writing, statistical analysis, business modeling, applications development and data… Continue
Added by Deepanshu Bhalla on June 27, 2017 at 9:00am —
Summary: Quantum computing is already being used in deep learning and promises dramatic reductions in processing time and resource utilization to train even the most complex models. Here are a few things you need to know.
So far in this series of articles on Quantum computing we showed that… Continue
Added by William Vorhies on June 13, 2017 at 8:00am —
R language is the world's most widely used programming language for statistical analysis, predictive modeling and data science. It's popularity is claimed in many recent surveys and studies. R programming language is getting powerful day by day as number of supported packages grows. Some of big IT companies such as Microsoft and IBM have also started developing packages on R and offering enterprise version of R.
Table of… Continue
Added by Deepanshu Bhalla on June 12, 2017 at 12:30am —
This article explains how to select important variables using boruta package in R. Variable Selection is an important step in a predictive modeling project. It is also called 'Feature Selection'. Every private and public agency has started tracking data and collecting information of various attributes. It results to access to too many predictors for a predictive model. But not every variable is important for prediction of a particular task. Hence it is essential to… Continue
Added by Deepanshu Bhalla on June 1, 2017 at 9:00am —