Data Science Quotes

“Data is a form of capital. And as is the case with all capital - it has to be efficient utilized.”
― Hendrith Vanlon Smith Jr, CEO of Mayflower-Plymouth

“Data is one of the most valuable resources of a bank.”
― Hendrith Vanlon Smith Jr, CEO of Mayflower-Plymouth

“We've passed through the era of data accumulation. We're entering now into the era of data amalgamation - data combined and directed in service of a greater purpose.”
― Hendrith Vanlon Smith Jr, CEO of Mayflower-Plymouth

“Many modern businesses have become proficient at mining data. In fact the mining of data is becoming almost routine. But as we advance further into the 21rst century and the 22nd century, the utilization of data begins to take priority. So it's not just about collecting all this data, but also about getting really creative with generating new ways to utilize that data in the quest to add value.”
― Hendrith Vanlon Smith Jr

“All good decisions are Data dependent. To make good decisions, you need good data. And you need that good data to be organized according to it's applicable use value. So every business should be mining data and organizing data to enable business leaders to make good decisions on behalf of the business.”
― Hendrith Vanlon Smith Jr, CEO of Mayflower-Plymouth

“When working with data, I discover what I really want to say.”
― Damian Mingle

“Data privacy is an illusion because you are not in control. Any information you don’t want out there shouldn’t be shared anywhere on and off the internet.”
― Olawale Daniel

“Protecting data is not just about preventing unauthorized access. It also involves ensuring data integrity and availability.”
― Enamul Haque, AI Horizons: Shaping a Better Future Through Responsible Innovation and Human Collaboration

“Machine learning modeling and testing on sample data and going through the business user acceptance test can surprise you!
And it will definitely make you to rethink on your feature selection and data sampling methods.”
― Shitalkumar R. Sukhdeve, Step up for Leadership in Enterprise Data Science & Artificial Intelligence with Big Data : Illustrations with R & Python

“Hey, I’m Santosh P
I’m determined to make your business successful.
My only question is, will it be yours?”
― Santosh Pandey

“Successful AI initiatives start with the right problems, but the right problems don’t necessarily come from your data scientists. They can come from leaders, domain experts, and innovators who sit close to the daily business challenges in your organization. Still, it takes practice to develop the vision for spotting AI opportunities...”
― Kavita Ganesan, The Business Case for AI: A Leader's Guide to AI Strategies, Best Practices & Real-World Applications

“In the midst of World War II, Quincy Wright, a leader in the quantitative study of war, noted that people view war from contrasting perspectives:

“To some it is a plague to be eliminated; to others, a crime which ought to be punished; to still others, it is an anachronism which no longer serves any purpose. On the other hand, there are some who take a more receptive attitude toward war, and regard it as an adventure which may be interesting, an instrument which may be legitimate and appropriate, or a condition of existence for which one must be prepared”

Despite the millions of people who died in that most deadly war, and despite widespread avowals for peace, war remains as a mechanism of conflict resolution.

Given the prevalence of war, the importance of war, and the enormous costs it entails, one would assume that substantial efforts would have been made to comprehensively study war. However, the systematic study of war is a relatively recent phenomenon. Generally, wars have been studied as historically unique events, which are generally utilized only as analogies or examples of failed or successful policies. There has been resistance to conceptualizing wars as events that can be studied in the aggregate in ways that might reveal patterns in war or its causes. For instance, in the United States there is no governmental department of peace with funding to scientifically study ways to prevent war, unlike the millions of dollars that the government allocates to the scientific study of disease prevention. This reluctance has even been common within the peace community, where it is more common to deplore war than to systematically figure out what to do to prevent it. Consequently, many government officials and citizens have supported decisions to go to war without having done their due diligence in studying war, without fully understanding its causes and consequences.

The COW Project has produced a number of interesting observations about wars. For instance, an important early finding concerned the process of starting wars. A country’s goal in going to war is usually to win. Conventional wisdom was that the probability of success could be increased by striking first. However, a study found that the rate of victory for initiators of inter-state wars (or wars between two countries) was declining: “Until 1910 about 80 percent of all interstate wars were won by the states that had initiated them. . . . In the wars from 1911 through 1965, however, only about 40 percent of the war initiators won.”

A recent update of this analysis found that “pre-1900, war initiators won 73% of wars. Since 1945 the win rate is 33%.”. In civil war the probability of success for the initiators is even lower. Most rebel groups, which are generally the initiators in these wars, lose. The government wins 57 percent of the civil wars that last less than a year and 78 percent of the civil wars lasting one to five years.

So, it would seem that those initiating civil and inter-state wars were not able to consistently anticipate victory. Instead, the decision to go to war frequently appears less than rational. Leaders have brought on great carnage with no guarantee of success, frequently with no clear goals, and often with no real appreciation of the war’s ultimate costs. This conclusion is not new. Studying the outbreak of the first carefully documented war, which occurred some 2,500 years ago in Greece, historian Donald Kagan concluded:

“The Peloponnesian War was not caused by impersonal forces, unless anger, fear, undue optimism, stubbornness, jealousy, bad judgment and lack of foresight are impersonal forces. It was caused by men who made bad decisions in difficult circumstances.”

Of course, wars may also serve leaders’ individual goals, such as gaining or retaining power. Nonetheless, the very government officials who start a war are sometimes not even sure how or why a war started.”
― Frank Wayman, Resort to War: 1816 - 2007

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“What Is Data Science and Information Science?

According to Dr.P.S.Jagadeesh Kumar (Dr.PSJ Kumar);

"The Science Of Learning And Applying Data By Exchanging Methods and Algorithms Between Human And Machine Is Known As Data Science (DS)"

"The Science Of Learning, Applying And Protecting Information By Exchanging Methods and Algorithms Between Human And Machine Is Known As Information Science (IS)”
― P.S. Jagadeesh Kumar

“Hacking described his research interest ‘in classifications of people, in how they affect the people classified, and how the affects on the people in turn change the classifications.’ Hacking labeled the subjects of these studies ‘moving targets’ because researchers’ investigatory efforts change them in ways so ‘they are not quite the same kind of people as before.”
― Kevin Guyan, Queer Data: Using Gender, Sex and Sexuality Data for Action

“The cleaning of data can remove its queerness: paper surveys where respondents score out the response options ‘female’ and ‘male’ and write their own answer, interview recordings were participants flip the focus and ask questions of the researcher, census returns where LGBTQ couples identify themselves as ‘married’ even when governments do not recognize same sex marriage. These examples demonstrate how collection methods can fail to restrict how participants share data about their lives and experiences. … cleaning, which involves the removal of data that breaks established rules”
― Kevin Guyan, Queer Data: Using Gender, Sex and Sexuality Data for Action

“DATA means Determined Action of Transformative Awareness.”
― Abhijit Naskar, Rowdy Scientist: Handbook of Humanitarian Science

“Should an organization find a data scientist to point out its shortcomings, follow them as you would a guide to hidden riches.”
― Damian Mingle

“It doesn't matter how slowly you go as long as you don't stop.✋”
― Ahmed Elkhateeb

“It doesn't matter how slowly you go as long as you don't stop.✋”
― ahmedelkhateeb01

“It doesn't matter how slowly you go as long as you don't stop.✋”
― Ahmed Elkhateeb_01

“It doesn't matter how slowly you go as long as you don't stop.✋”
― Ahmed Elkhateeb1

“Consuming data with no sense of context, gives you, not awareness, but only ulcers.”
― Abhijit Naskar, Brit Actually: Nursery Rhymes of Reparations

“For me, data science is about more than just acquiring skills- it's a true passion.”
― Benjamin Kofi Quansah

“Data is power.”
― Abhijit Naskar, Azad Earth Army: When The World Cries Blood

“You don’t train a neural network. You let it struggle, fail, adapt, and repeat—until its failures look like intelligence.”
― Emmimal P. Alexander, NEURAL NETWORKS AND DEEP LEARNING WITH PYTHON A PRACTICAL APPROACH

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