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Data Talk Articles


Setting up a Python Jupyter Notebook Online & Working with Python on the Cloud;

06 April 2020 | Mayank Tripathi

Wow this is really good as now we can do hands-on using the above online juypter notebook.


Python for Data Analysis;

06 April 2020 | Mayank Tripathi

Really good post.Nice and easy way to explain the concept.This is very helpful, in understanding what is Data Analysis and how python can be used to perform analysis.Will go through the links as there are many links in the post, but after reading this seems I will definitely be benefitted from those.


Tool differences between data science and analytics?;

06 April 2020 | Abhishek Mishra

I think there any many difference and fundamentally there is a think line among-st these both ...apart from its defination


Two stage process of data exploratory;

06 April 2020 | Balakrishnan Subramanian

Two stage process: – Exploratory: Search for evidence using all tools available – Confirmatory: evaluate strength of evidence using classical data analysis


Advanced Monitoring System Using Wireless Sensor Networks;

06 April 2020 | Sureshkumar Sundaram

ZigBee is a wireless communication standard designed to address the unique needs of low-power, low-cost wireless sensor, and control networks. ZigBee can be used almost anywhere, as it is easy to implement and requires little power to operate.


What are the top credentials required to be AI engineer?;

05 April 2020 | Abhishek Mishra

Thanks its helpful. Let keep updating this section with any latest courses /online/offline in AI field


How does R shiny works?;

04 April 2020 | Abhishek Mishra

The Shiny web framework is fundamentally about making it easy to wire up input values from a web page, making them easily available to you in R, and have the results of your R code be written as output values back out to the web page.


What are the common successful methods to do smoothing?;

03 April 2020 | Abhishek Mishra

Exponential smoothingExponential smoothing is a weighted moving average technique which is especially effective when frequent re-forecasting is required, and when the forecasts must be achieved quickly. It is a short-term forecasting technique that is frequently used in the production and inventory environment, where only the next period’s value is required to be forecast.


NLP and NLG how different are they?;

01 April 2020 | Abhishek Mishra

NLGOnce a chatbot, smart device, or search function understands the language it’s “hearing,” it has to talk back to you in a way that you, in turn, will understand. That’s where NLG comes in. It takes data from a search result, for example, and turns it into understandable language. So whenever you ask your smart device, “What’s it like on I-93 right now?” it can answer almost exactly as another human would. It may say something like, “There is an accident at exit 36 that has created a 15-minute delay,” or “The road is clear.” NLG is used in chatbot technology, as well. In fact, chatbots have become so advanced; you may not even know you’re talking to a machine. Using NLP, NLG, and machine learning in chatbots frees up resources and allows companies to offer 24/7 customer service without having to staff a large department.


Which of these measures are used to analyze the central tend;

31 March 2020 | Abhishek Mishra

Which is Best—the Mean, Median, or Mode? When you have a symmetrical distribution for continuous data, the mean, median, and mode are equal. In this case, analysts tend to use the mean because it includes all of the data in the calculations. However, if you have a skewed distribution, the median is often the best measure of central tendency. When you have ordinal data, the median or mode is usually the best choice. For categorical data, you have to use the mode.


What does COTS represent?;

22 March 2020 | Balakrishnan Subramanian

Commercial Off The Shelf.


What is Coverage measurement?;

22 March 2020 | Balakrishnan Subramanian

It is a partial measure of test thoroughness.


The later in the development life cycle a fault is discovered, the more expensive it is to fix. Why?;

22 March 2020 | Balakrishnan Subramanian

The fault has been built into more documentation, code, tests, etc


In which order should tests be run?;

22 March 2020 | Balakrishnan Subramanian

The most important one must be tested first

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Understanding the COVID-19 Pandemic as a Big Data Analytics Issue

06 April 2020

Big data analytics techniques are well-suited for tracking and controlling the spread of COVID-19 around the world.


Data Digest Predictive Analytics and AI for Retail, Wireless Networks, and Trucking

01 April 2020

These articles explain some of the latest applications for predictive analytics and machine learning AI


Free COVID-19 online course re-launches to learners worldwide

30 March 2020

Imperial researchers and Coursera have partnered to offer a free Massively Open Online Course explaining the science behind coronavirus.


COVID-19 Data Research

28 March 2020

Find out about the ways in which Health Data Research UK, as the national institute for health data science for England, Wales, Scotland and Northern Ireland, is championing the use of health data to respond to COVID-19.


DataIQ Top 100 List

26 March 2020

The first and only fully-curated power list of the most influential data and analytics practitioners in UK organisations.


5 AI policy questions our presidential candidates must address

22 March 2020

An over-regulation of AI could hand technical superiority to countries like China and Russia, leading to a ripple effect on America’s GDP and even threatening national security.


Three Tricks to Amplify Small Data for Deep Learning

14 March 2020

One of the drawbacks of deep learning is it typically requires huge data sets (not to mention big clusters). But with a little skill, practitioners with smaller data sets can still partake of deep learning riches.


Amid coronavirus setbacks, sustainability is key to the future of China’s BRI

04 March 2020

Sustainability must be at the heart of China's Belt and Road Initiative (BRI) if it is to remain a major force in global infrastructure development, according to an influential new report, produced by the Economist Corporate Network in cooperation with Baker McKenzie.


Why hasn’t AI changed the world yet?

03 March 2020

When Kursat Ceylan, who is blind, was trying to find his way to a hotel, he used an app on his phone for directions, but also had to hold his cane and pull his luggage.


AI ethics backed by Pope and tech giants in new plan

29 February 2020

The Roman Catholic Church has joined up with IBM and Microsoft to work on the ethics of artificial intelligence.


The Age of Big Data

20 February 2020

GOOD with numbers? Fascinated by data? The sound you hear is opportunity knocking. Mo Zhou was snapped


White House Earmarks New Money for A.I. and Quantum Computing

11 February 2020

The technologies are expected to become an important part of national security, and some worry the United States is behind China in their development.


5 critical issues solved by DataOps

08 February 2020

This article examines the practical uses of DataOps, its advantages, and how it can solve critical issues.


How Blackstone uses data scientists to win deals

05 February 2020

Investment banks and hedge funds aren't alone in incorporating data science into their business models. Private equity funds are also turning to data science, both to win deals in the first place and to help them manage portfolio companies after a purchase


Coronavirus: Can AI (Artificial Intelligence) Make A Difference?

02 February 2020

The mysterious coronavirus is spreading at an alarming rate. There have been at least 305 deaths as more than 14,300 persons have been infected.

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The Data Science Foundation is committed to improving decision making. We believe that the use of data can improve decision making, but only if the social and environmental impact of decisions are evaluated alongside the knowledge and insight created though analytics. We work hard to help our members improve their decision making. To make good decisions today and to make better decisions tomorrow.


We help organisations new to evidence-based decision making; collect, process and analyse data. We help them appreciate the meaning hidden in their data so that they can understand more about the landscape in which they operate. We then help organisations who already have the expertise to collect data and create insight, to consider the moral implications of their actions. To think about the social and environmental impact of what the data might suggest is the best possible course of action.