This is a major contribution of analytics and big data when you talk about the success of mobile app development. Big Data is large data sets that can be analyzed to know more about the user’s interests, demographics, trends and interactions. We rely on mobile apps present on our phone for most of the tasks like a reminder, planner, health and fitness, etc.…

Hello! This is my first post on Data Science Central, so please be patient. I've had a short Q&A session with our CTO Sergey Nemesh.

Q: What problems can Blockchain solve for the FinTech sector? 

A: The main global problem in the FinTech sector is trust providing as for financial operations it plays a crucial role. Blockchain provides trustless transactions which…

Summary: This blog details R data.table programming to handle multi-gigabyte data. It shows how the data can be efficiently loaded, "normalized", and counted. Readers can readily copy and enhance the code below for their own analytic needs. An intermediate level of R coding sophistication is assumed.

In my travels over the holidays, I…

To get a defining advantage over the competitors the banks, as well as other financial institutions must dig deep into technology breakthroughs. Luckily, Artificial Intelligence and Machine Learning are already here to help them to achieve it. Improving data processing will lead to better strategies and fraud prevention levels.…

Software is always not only a set of instructions but also a context that manages, interacts and executes these instructions. At the start of developing, the engineer configures a dev environment. He can continue to change it in all stages of development. The problem appears when…

While many of the programming libraries encapsulate the inner working details of graph and other algorithms, as a data scientist it helps a lot having a reasonably good familiarity of such details.  A solid understanding of the intuition behind such algorithms not only helps in appreciating the logic behind them but also helps in making conscious decisions about their applicability in real life cases.  There are several graph based algorithms and most notable are the shortest path…

Machine Learning is a term heard around the world these days. Industries like Retail, Healthcare, and Manufacturing are taking the best out of it. So does the leaders in Banking and Finance. There is enough time and room before the technology will truly explode. However, we can still talk about some real-world use cases and ways your business can…

Near about 14% of the workforce or 375 million people worldwide could lose their jobs to AI and automation.

Take a step back and look over the year that has gone by. What have you achieved? Are you being monolingual?

For someone in the tech industry, this might render your career obsolete. In times when AI is taking over the world, being monolingual can be a disadvantage.…

Summary:  Looking at the 12 hottest world-changing segments in the VC-funded world shows that AI will play a key role.  Here’s a little more detail.

From the inside of the data science profession looking out it’s easy to imagine that almost everything that is or will be important somehow depends on AI.  Maybe…

Robotic Process Automation has generated a lot of buzz across many different industries. As businesses focus on digital innovation, automation of repetitive tasks to increase efficiency while decreasing human errors is an attractive proposition. Robots will not tire, will not get bored, and will perform tasks accurately to help their human counterparts improve productivity and free them up to focus on higher level tasks. Beyond simple RPA, Intelligent…

Maybe the biggest challenge we face as a society is our ability to unlearn – to let go of – outdated concepts and beliefs in order to adopt new approaches. Our everyday lives are dominated by outdated concepts: change the oil every 3,000 miles, don’t wear white before Memorial Day, only senior management has the best ideas, don’t eat dessert until you’ve cleaned your plate, trade wars are easy to win, leeches work wonders on headaches, etc.

Well, I’m going to throw down the gauntlet…

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.  …

AI-based technologies reached a new level of adoption in 2019 as businesses learned more about what exactly AI could do for them. 2020 promises to be even more exciting, with AI systems continuing to mature and companies extending usage and applications to address highly specialized needs. In the year ahead, organizations will be empowered to allocate resources even more wisely while achieving greater efficiency.

Here are my top predictions for AI in…

One of the main requirements for modern information systems is the high data processing rate.  Among the solutions to solve this problem the popular one is to use high-performance databases. This article will review and compare two popular databases in performance terms:…

By Pranay Agarwal, VP, Sales and Customer Success at Analytics2go 

     The theme of artificial intelligence adoption is becoming the competitive edge for many companies in different verticals—allowing for both disruption and innovation. Accenture’s research shows that two-thirds of organizations surveyed are …

Here is our selection of featured articles and technical resources posted since Monday.

Resources

• Neural Quantum States
• Google Brain’s TensorFlow…

Business Intelligence (BI) and Business Analytics (BA) are both used to interpret business information and create data-based action plans. The two terms are frequently used interchangeably, and many people consider one to be a subset of the other (there's some disagreement about whether BI is a subset of BA, or BA is a subset of BI). However,  it might be more accurate to describe them as two arms of successful business planning: BI tells you the…

We have data-driven decision support systems implemented in Management Information Systems(MIS). Algorithms created by humans coded into MIS chew raw data and spit out decisions. The MIS systems were developed by human with substantial effort in software development. Once created, they allowed very little flexibility in deriving insights from new data sets. Now we have Machine Learning (ML) systems capable of making data-driven decisions or predictions without the need for explicit…

Summary:  The results are in.  There is only one demonstrably successful strategy for creating big wins for AI-first companies.  We’ll briefly summarize the other contenders that have fallen by the wayside and then lift the curtain on the winner.

For the last three years we’ve been close observers of exactly…

Will Artificial Intelligence (AI) create an environment where design thinking skills are more valuable than data science skills? Will AI alter how we define human intelligence? Will AI actually force humans to become more human?

Okay, sounds questions one might expect from an episode of Rod Serling’s TV series “Twilight Zone” (which I preferred over the meaningless college football bowl games on New Year’s…