Category: Data Analysis/Data Science

How To Succeed Without Data, In A Data Driven World – Chaka Booker

1.jpg

There are some words that inspire confidence when you use them. “Data” is one of those words. Throw “data-driven” in front of “decision-making” and you’ll suddenly find yourself more credible. If someone is sharing an idea, ask about “the data” and your IQ shoots up several points. I believe in data. I understand how data can identify trends, minimize risk and lead to better decisions. Data comforts me. But the fixation on data has a drawback. It leads to the belief that decisions made without data – aren’t as strong. Never mind that bad decisions, based on data, get made all the time……

Read more: https://www.forbes.com/sites/chakabooker/2018/10/06/how-to-make-decisions-without-data-in-a-data-driven-world/#5ba0f01e1d6e

 

 

 

Your kindly Donations would be so effective in order to fulfill our future research and endeavors – Thank you

Advertisements

Why Mathematicians Can’t Find the Hay in a Haystack – Vladyslav Danilin

1.jpg

The first time I heard a mathematician use the phrase, I was sure he’d misspoken. We were on the phone, talking about the search for shapes with certain properties, and he said, “It’s like looking for hay in a haystack.” “Don’t you mean a needle?” I almost interjected. Then he said it again. In mathematics, it turns out, conventional modes of thought sometimes get turned on their head. The mathematician I was speaking with, Dave Jensen of the University of Kentucky, really did mean “hay in a haystack.” By it, he was expressing a strange fact about mathematical research: Sometimes the most common things are the hardest to find…….

Read more: https://www.quantamagazine.org/why-mathematicians-cant-find-the-hay-in-a-haystack-20180917/

 

 

Your kindly Donations would be so effective in order to fulfill our future research and endeavors – Thank you

 

 

What is The Difference Between Data Analysis and Data Science?

1.jpg

Following the current technological transformations within the economy, there has been an emergence of enormous career options, wherein, Data Science is the hottest. According to the Glassdoor, Data Science arose as the highest paid area. On the other hand, there is a significant field which has been gazing attention since years, i.e., Data Analysis. Both the Data Science and Data Analysis is often confused by the individuals. However, the terms are incredibly different in accordance with their job roles and the contribution they do to the businesses. But, are these the only factors which make these two distinct from each other? Well, to know more we need to take a look below:

Data Analysis Data Science:

Data Analysis is referred as the process of accumulating the data and then analyzing it to persuade the decision making for the business. The analysis is undertaken with a business goal and impact the strategies. Whereas, Data Science is a much broader concept where a set of tools and techniques are implied upon to extract the insights from the data. It involves several aspects of mathematics, statistics, scientific methods, etc. to drive the essential analysis of data

Skills:

The individuals misinterpret Data Analysis with Data Science, but the methodologies for both are diverse. The skill set for the two are distinct as well. The fundamental skills required for Data Analysis are Data Visualisation, HIVE, and PIG, Communication Skills, Mathematics, In-Depth understanding of R and python and Statistics. On the other hand, the Data Science embed the skills like – Machine Learning, Analytical Skills, Database Coding, SAS/R, understanding of Bayesian Networks and Hive

Techniques:

Though the areas – Data Analysis and Data Science, are often confused about being similar, but the methodology is different for both. The methods used in the two are diverse. The essential techniques used in Data Analysis are – Data Mining, Regression, Network Analysis, Simulation, Time Series Analysis, Genetic Algorithms and so on. While, the Data Science involves – Split Testing, categorizing the issues, cluster analysis and so on

Aim:

Just like the areas are different, so are their goals. The Data analysis is basically about answering the questions generated, for the betterment of the businesses. While the Data Science is concerned with shaping the questions followed by answering The Data science, as illustrated above, is a more profound concept

The era of the Artificial Intelligence and Machine Learning is shaping economy in a much more comprehensive aspect. The organizations are moving towards data-driven decision-making process. The data is becoming imperative in functioning and are not limited to the Information Technology organizations. It is soon taking over the industries like – Sports, Medicine, Hospitality, etc.

Such technological advancements have led to a rise in the job opportunities in the area of Data Science and Analysis. The merely significant facet which needs to be taken into consideration is the understanding of the difference between the two. The Big Data is the future which is expected to lay a considerable impact on the operations of both industries and routine life.

 

 

Your kindly Donations would be so effective in order to fulfill our future research and endeavors – Thank you
https://www.paypal.me/ahamidian