Fifty years, ago, the lines between "data analysis" and "statistical analysis" were pretty clear. But as data analysis evolved, those lines became blurred. The differences between the two terms are now very much a grey area, but there are still a few notable differences.
Data scientists and statisticians typically define "data analysis" in different ways.
Added by Stephanie Glen on January 31, 2020 at 3:30am — No Comments
Blockchain technology has taken the IT world by storm. It has the potential to change the digital world completely, including the way businesses operate and make transactions. Initially, it was used to handle cryptocurrencies such as Bitcoin. Today, numerous business applications across domains use Blockchain as their central component.
However, Blockchain is still at an evolving stage, and its adoption is slow in most industries. Toward this, a programming language like Java can help…Continue
The Economic Value Curve is a measure of the relationship between a dependent variable and independent variables to achieve a particular outcome such as retain customers, increase operational uptime, or optimize inventory. The Economic Value Curve measures the impact that increasing or decreasing one of the independent variables has on the dependent variable. In Figure 1, for example, if we want to improve the dependent variable “Uptime %” then we need to spend more on the independent…Continue
Added by Bill Schmarzo on January 30, 2020 at 11:30am — No Comments
When it comes to dealing with development and software, companies primarily have quite similar goals. All of them want the code to be consistent and self-documenting among other things. However, when Angular enters the picture, and it is when the scenario becomes exceptionally challenging. It can be associated with the fact that prominent the organization, more significant will be the number of developers they have working on various apps. For a company, that means they must spell out as…Continue
Added by Ryan Williamson on January 30, 2020 at 1:30am — No Comments
Added by Vincent Granville on January 29, 2020 at 9:30pm — No Comments
Summary: Workforce forecasting and scheduling applications are rapidly upgrading their use of AI. Techniques of time series forecasting ranging from the simple Holt Winters to the complex, DNNs and Multiple Temporal Aggregation are available on some but not all platforms. Increasingly, AI differentiates the usefulness of these apps.
Added by William Vorhies on January 28, 2020 at 2:15pm — No Comments
News from Google.
Across the web, there are millions of datasets about nearly any subject that interests you. If you’re looking to buy a puppy, you could …
Added by Capri Granville on January 28, 2020 at 12:30pm — No Comments
Machine learning (ML) is a hot topic nowadays. Everyone speaks about the new programming paradigm, models are implemented in very different domains, more and more startups are relying mainly on ML.
At the same time, machine…Continue
Even though processing and storage have become cheap and enterprises are adopting high performance analytics infrastructure, still in majority of cases the analytics study is constrained by local system. Multiple scenarios like working on a proof-of-concept in a small enterprise which can’t afford investing on heavy analytics infra, academic projects, hobby projects… the list goes on and the common factor here is system constraints.
Though we can’t escape the scenarios where we are…Continue
Added by saurabh ajmera on January 28, 2020 at 3:44am — No Comments
Hundreds of programming languages dominate the data science and statistics market: Python, R, SAS and SQL are standouts. If you're looking to branch out and add a new programming language to your skill set, which one should you learn? This one picture breaks down the differences between the four languages.
Below are more…Continue
If you ever have some moments…Continue
Added by Vincenzo Parrilla on January 27, 2020 at 11:10am — No Comments
Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a + is our selection for the picture of the week. To subscribe, follow this link. …Continue
Added by Vincent Granville on January 26, 2020 at 6:00pm — No Comments
I often use this quote from Isaac Newton in my teaching.
AI is a vast and a complex subject. No matter how much you know - you realise that there is really a vast amount more to learn. So, my way of learning a subject as complex and dynamic as AI, is to share my insights. This helps me to refine my own thinking.
I also follow…Continue
Added by ajit jaokar on January 26, 2020 at 12:30pm — No Comments
I am always looking for examples to bolster my University of San Francisco “Economic Value of Data” research efforts, and I think I’ve found a good one. The NHO article “Is value creation with data something Norway can live off of?” states that Norway believes that 1) the financial or economic value that Norway will be able to extract from data is on a par with the oil, while 2) the value of data is even more important than oil when considering the broader society…Continue
Added by Bill Schmarzo on January 26, 2020 at 5:00am — No Comments
There are many ways to deal with time-data. Sometimes one can use it as time-series to take possible trends into account. Sometimes this is not possible because time can not be arranged in a sequence. For example, if there are just weekdays (1 to 7) in a dataset over several month. In this case one could use one-hot-encoding. However, considering minutes or seconds of a day one-hot-encoding might lead to high complexity. Another approach is to make time cyclical. This approach leads to a…Continue
Added by Frank Raulf on January 26, 2020 at 4:00am — No Comments
In part 1 of this article series, I provided a quick primer on graph data structure, acknowledged that there are several graph based algorithms with the notable ones being the shortest path/distance algorithms and finally illustrated Dijkstra’s and Bellman-Ford algorithms. Continuing with the shortest path/distance algorithms, I have illustrated Floyd-Warshall and A* (A-Star) algorithms in this part 2 of the article. As was stated in part 1, while the inner workings of these algorithms are…Continue
Added by Murali Kashaboina on January 25, 2020 at 2:29am — No Comments
We can have an intuition of which professions will be more in demand in the future, according to new trends and social changes. It is clear that, as many large organizations mention, everything related to data science and artificial intelligence will be one of the most demanding professions in the future. But can we imagine what the new professions that will emerge around this discipline will be in the future?
21 Jobs: The Road to 2028…Continue
Added by Noelia Gonzalez Rodriguez on January 25, 2020 at 12:30am — No Comments
Data Compliance: An Integral part of Business Analytics
Prashanth Southekal and Santhosh Raju
“It takes 20 years to build a reputation and 5 minutes to ruin it. If you think about that, you will do things differently”
Added by Prashanth Southekal, PhD on January 24, 2020 at 12:30pm — No Comments
All machine intelligence is powered by data. This isn't ground-breaking, or even news – we've known about data's value for decades now. However, not all data are created equal, and we'll be looking at executing machine learning products from a standpoint that prioritizes the quality of the data streaming into them.
Machine Learning (ML) is a specific subset of…
Added by Maha Islomova on January 24, 2020 at 7:30am — No Comments
Demand is the key indicator for every business to consider before taking the first step or expanding in the chosen market segment. It drives economic growth while central banks and governments boost demand to end down-sliding. Demand Prediction, which is part of Predictive Analytics, implies an evaluation of the number of goods and services that consumers will probably buy in the future. The most critical business factors such as turnover, profit margins, cash flow, capital…Continue
Added by Roman Chuprina on January 24, 2020 at 4:00am — No Comments