Big Data, Cloud & DevOps

Dataset shift is a topic that is extremely important and yet undervalued by people in the field of data science and machine learning. Given the impact, it can have on the performance of our algorithms, spend some time working out how to handle data properly in order to give you more confidence in your models, and, hopefully, superior performance. The key theme of this article can be summarized in a single sentence: Dataset shift is when the training and test distributions are different.

Bootstrap is a method to extract as much information as possible from a finite size dataset and it makes us calculate the expected value of any observable and it’s precision. It’s really useful when we need to calculate an error estimate for some scientific measure and it can easily be generalized for multivariate observables. Any data scientist should not forget to use this powerful tool, which has shown several useful applications even in machine learning. 

Employee engagement is an important factor contributing to an organization’s success. In order for the organization to be up and running successfully, the officials should focus more on employee engagement along with employee satisfaction. The more engaged employees would be, the more would be the productivity rate. By looking at the big picture, I think it is best to leave the important decisions up to your employees and give them the freedom to work innovatively so that they can be engaged and put their ideas and method on the table.

Prescriptive analytics are powerful, but they won’t be necessary for every company, or every campaign you push out to customers. They also will require a lot of tweaking. No algorithm was crafted perfectly the first time. It takes time, effort, and focus to make prescriptive analytics work effectively. But if you are in a competitive marketplace—managing anything from products to people—prescriptive analytics could mean a huge boost to profit, productivity, and the bottom line. And honestly: it’s still early in the prescriptive analytics game.

Data lakes are a modern take on big data. A data lake contains a vast body of data and is able to handle that data’s volume, velocity, and variety. Data lakes are a natural choice to handle the complexity of such data. So, when you look at creating a data lake, think about what the ecosystem looks like and who your consumers are. Then, embark on a journey to build a lake on your own.

The primary difference between traditional BI and the new, more modern approach to BI lies in the flexibility and accessibility. The more traditional tools were designed for use by analysts or IT staff and, while these tools provided sophisticated features, these features were not accessible or easy to understand for anyone outside the analyst or technical community. The traditional tools were not scalable or flexible enough for mobile, nor did they provide guided, auto-recommendations in a natural language environment.

Technologies are still developing at an unprecedented speed. Value-based consumers, recessionary fears, and global sociopolitical uncertainty will make 2020 the year of adaptability. So, what the future has in stock for us? In Part 1  we have mentioned 20 predictions for 2020 and covered some there. In Part 2 we cover rest of the 20 predictions for 2020.  The year 2020 has much to offer. The expansion of 5G and other prospective wireless technologies is round the corner, which opens a lot of possibilities for the implementation of technological solutions in any sphere.

Multicloud is now on almost everyone’s tip of the tongue. It does allow diversity, although to make use of it you’ll have to pay heavily enough. Or hire a cloud management genius with guts and wits to build you a truly working multicloud system. It will take a while for multicloud to become a democratic and an easy-to-use solution bringing a real value for the clients. For now, one definitely can use multicloud platforms without paying dearly,

Technologies are still developing at an unprecedented speed. Cashless payments, smart wearables, 3D-printed prosthetics, movies downloaded at lightning speeds, etc. What will the Year of the Rat bring for the technological community? Value-based consumers, recessionary fears, and global sociopolitical uncertainty will make 2020 the year of adaptability. So, what the future has in stock for us? Here are 20 predictions for 2020 some of which are covered here and the rest will be covered in Part 2.

Big Data is not going to slow down the pace when transforming the financial services industry. Structured and unstructured data provides customer insights, complex algorithms execute trades, and automation of credit score calculation minimizes human error. These are just a couple of ideas to mark the development of the industry, which was changed due to the Big Data. Consider its possible usage cases in finance, and predict the challenges and disadvantages of its implementation.

Will we serve our technologies or will they serve us? Will we create a new global middle class or pledge fealty to global elite? Technology will not save us. Markets will not save us. We can, as we did in the 1920s and 30s, choose to ignore the challenges before us or, as we did in the 1940s and 50s, choose to build institutions that can help us overcome them and build a new era of peace and prosperity. The ball is in our court.