Having spent the better part of the last decade helping organisations in the U.S. and the U.K. use data to drive profit, efficiency, and performance improvements, I thought it would be helpful to jot down best practices for organizations that are looking to get the most value out of their enterprise-level financial and operational data sets.

Every organisation entering the roaring 20s knows that they must use their data as a strategic differentiator to gain a competitive advantage and…

Data Science methods and techniques allow new approaching the solution of complex tasks in terms of mathematics, and statistics for the various aspects and areas of our life, work, and business. Therefore, this makes it possible to produce the most unobvious…

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…

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:…

25 Experts have compiled this list of Best Python for Machine Learning Course, Tutorial, Training, Class, and Certification available online for 2020. It includes both paid and free resources to help you learn Python for Machine Learning and these courses are suitable for beginners, intermediate learners as well as experts.

1.Machine Learning with Python (Coursera) 

If you are interested in getting started with the field…

As a data scientist in an organization you frequently find yourself in a couple of situations:

In the abundance of…

Today, one of the most popular tasks in Data Science is processing information presented in the text form. Exactly this is text representation in the form of mathematical equations, formulas, paradigms, patterns in order to understand the text semantics (content) for its further…

Production sphere embraces a wide range of processes related to all branches and stages of creating material goods. In addition, these material goods may be of different values and even have rather contrasting goals. 

Under conditions…

Data science and analytics are growing in their popularity and range of applications in the modern world. Data science deals with answering questions and uncovering hidden insights, while analytics is rather concentrated on the processing itself and conducting of statistical…

H2O is a scalable and fast open-source platform for machine learning. We will apply it to perform classification tasks. The dataset we are using is the Bank Marketing Dataset. Here we need to train a model which will be able to predict if the client of the bank opens…

Problem

While training the model, we want to get the best possible result according to the chosen metric. And at the same time we want to keep a similar result on the new data. The cruel truth is that we can’t get 100% accuracy. And even if we did, the result is still not…

A successful business requires new approaches to data management in this age. Modern advances in data science area provide an efficient solutions for numerous use cases.

Data science embraces a broad spectrum of tasks in the sphere of…

Data Scientists help find insights about the market and help make products better. They are responsible for analyzing and handling a massive amount of structured and unstructured data and require various tools to do so. Some of the tools used by Data Scientists to carry out their data operations are mentioned below.

1. SAS-

Designed for statistical operations, SAS is an open source proprietary software that is used to…

Hadoop is an open-source framework that stores and process big data in a distributed environment using simple programming models. It is designed to scale up from single servers to thousands of machines, while each offers local computation and storage. Hadoop divides a file into blocks and stores across a cluster of machines. It achieves fault tolerance by replicating the blocks on a cluster.

Hadoop can be used as a flexible and easy way for the distributed processing of large…

In the times when data is of extreme value, industries cannot afford to ignore it. The only right choice for them is to find new ways to use data for their benefit.

These words have become a motto for the sales industry along with the others. As far as the feature of the…

Businesses generate data from different sources such as text files, multimedia forms, Internet of Things (IoT) devices, social media, and, mobile. Data is being used in various domains such as marketing, social media, healthcare, automation, sales, predictive analysis, etc.

However, the simple Business Intelligence (BI) tools cannot process the humongous data. The advanced analytical tools and algorithms for processing and analyzing can help to draw actionable insights out of…

Actually the unlocking of hidden benefits and true potential of the data is an essential task for business. Also, customers` satisfaction appears to be a pushing force for evolution of services and products. If you have loads of customer data available for analysis the work on the improvement of the customers` satisfaction…

Q1. Is MSc in Data science/Data Analytics same as ML/AI as some universities don’t have AI but Data Science?

Q2. I am interested in MS Data Science and not MS Analytics as the later is not technical in nature. Are MS Data Science and MS Data Analytics the same?

Q3. How to Choose Between a Master’s in Data Analytics vs Business…

Aren’t all good data scientists’ great data scientists? As much as we’d like to think good data scientists to be great data scientists, it isn’t. No doubt good data scientists are a rarity. But great ones are even more difficult to find. It is a bigger challenge like finding a needle in a haystack.

Data science industry is a thriving industry. Multiple organizations have started implementing data science practices for better future predictions. Moving to a data…