7 AWESOME DIFFERENCE BETWEEN DATA SCIENCE VS DATA MINING

As organizations and businesses have started to realize that there’s a huge value hiding in the massive amount of data they capture on a regular basis, they’ve been trying to employ different techniques to realize that value. While the ultimate goal is to produce actionable insights from that data, the tech world is getting filled with a significant number of technical terms. And among all these terms, probably the most talked-about terms are data science and data mining. Though some people use them interchangeably, they come with significant differences. Here’re seven most prominent differences between data science and data mining.

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As organizations and businesses have started to realize that there’s a huge value hiding in the massive amount of data they capture on a regular basis, they’ve been trying to employ different techniques to realize that value. While the ultimate goal is to produce actionable insights from that data, the tech world is getting filled with a significant number of technical terms. And among all these terms, probably the most talked-about terms are data science and data mining. Though some people use them interchangeably, they come with significant differences. Here’re seven most prominent differences between data science and data mining.

Kerry…


WHAT ARE REAL-LIFE EXAMPLES OF THE APPLICATION OF BIG DATA ANALYTICS?

While the concept of big data has been around for a significant number of years, everything has started to change with the emergence of big data analytics. This process allows businesses to perform analytical procedures efficiently and quickly, giving them a competitive advantage over competitors. Here’re some of the most prominent real-world examples of how big data analytics is being used.

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These days, as the world is getting more and more connected through different types of digital devices, a massive volume of data is getting emanated from a huge number of digital sources. Businesses and organizations from across the globe are leveraging the power of this data and putting it to their advantages.

Big data analytics is performed to identify correlations, hidden patterns, and to derive actionable insights that can help businesses make informed decisions.

While the concept of big data has been around for a significant number of years, everything has started to change with the emergence of big data analytics. This process allows businesses to perform analytical procedures efficiently and quickly, giving them a competitive advantage over competitors. …


WHAT IS THE BEST NEURAL NETWORK MODEL FOR TEMPORAL DATA IN DEEP LEARNING?

If you’re interested in learning artificial intelligence or machine learning or deep learning to be specific and doing some research on the subject, probably you’ve come across the term “neural network” in various resources. In this post, we’re going to explore which neural network model should be the best for temporal data.

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If you’re interested in learning artificial intelligence or machine learning or deep learning to be specific and doing some research on the subject, probably you’ve come across the term “neural network” in various resources. In this post, we’re going to explore which neural network model should be the best for temporal data.

You can consider an artificial neural network as a computational model which is based on the human brain’s neural structure. Neural networks are capable of learning to perform tasks such as prediction, decision-making, classification, visualization, just to name a few.

An artificial neural network contains processing elements or artificial neurons and is organized in different interconnected layers namely input, hidden, and output. In deep learning, different types of neural networks are used. Since the emergence of big data, the field of deep learning has been gaining steady popularity as the performance of neural networks has improved by working with more amounts of data than ever before. …


WHY PYTHON IS ESSENTIAL FOR DATA ANALYSIS?

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In the U.S., over 36,000 weather forecasts are issued every day that cover 800 different areas and cities. Though some people may complain about the inaccuracy of such forecasts when a sudden spell of rain messes with their picnic or outdoor sports plan, not many spare a thought about how accurate such forecasts often are. That’s exactly what the people at Forecastwatch.com (a leader in climate intelligence and business-critical weather) did. They assembled all 36,000 forecasts, placed them in a database, and compared them to the actual conditions that existed on that particular day in that specific location. …


HOW WE USE BIG DATA ANALYTICS TOOLS?

Businesses are increasingly looking for professionals who’re familiar with various big data analytics tools to get help in attaining their goals. Here’s an overview of some popular big data analytics tools.

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While the concept of big data isn’t new, most businesses have recently realized that if they can capture all the data which streams into their operations, analytics can be applied and significant value can be derived from that. Now, the massive amounts of data only become useful when big data analytics is performed to identify patterns and insights that would be left undiscovered otherwise. As a result, businesses are increasingly looking for professionals who’re familiar with various big data analytics tools to get help in attaining their goals. Here’s an overview of some popular big data analytics tools.

1- Hadoop


IS IT EASY FOR DATA ENGINEER TO BECOME DATA SCIENTIST?

If you want to step into the data science field, it’s crucial to understand the differences between a data scientist and data engineer to identify whether it’d be possible for you switch positions without investing much effort and time.

In this post, we’ve tried to outline the key differences between these two positions to help you make an informed decision. Let’s start the discussion.

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These days, the business world runs entirely on data and none of the companies can survive without data-driven strategic plans and decision making. The field of data science is quite broad and contains a significant number of job positions including data scientist and data engineer. If you want to step into the data science field, it’s crucial to understand the differences between a data scientist and data engineer to identify whether it’d be possible for you switch positions without investing much effort and time.

Claire D. Costa

In this post, we’ve tried to outline the key differences between these two positions to help you make an informed decision. …


WHAT ARE THE BASIC REQUIREMENTS FOR A DATA ANALYST OR A DATA SCIENTIST?

If you’re trying to step into the data science field and have gone through any job portal, most likely you’ve observed that both data scientist and data analyst job positions are in high demand with impressive salaries. In this post, we’ve outlined the fundamental requirements for both data scientist and data analyst positions to help you make an informed decision.

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If you’re trying to step into the data science field and have gone through any job portal, most likely you’ve observed that both data scientist and data analyst job positions are in high demand with impressive salaries. While both these positions share some similarities, there’re significant differences in terms of basic requirements understanding which is necessary to select a path to follow. In this post, we’ve outlined the fundamental requirements for both data scientist and data analyst positions to help you make an informed decision.

1- Data scientist


WHAT KIND OF WORK PRODUCT DO DATA SCIENTISTS PRODUCE?

You may already know that the power of data science originates from a robust understanding of a wide range of skills including algorithms and statistics, programming, communication skills, and many other skillsets. Put simply, data science is all about applying the core skills in a systematic and disciplined manner.

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You may already know that the power of data science originates from a robust understanding of a wide range of skills including algorithms and statistics, programming, communication skills, and many other skillsets. Put simply, data science is all about applying the core skills in a systematic and disciplined manner.

If you’re an aspiring or beginner data scientist, you’ve probably gone through data scientist job responsibilities published in job openings in various job boards. They mention a lot of things which may seem a little confusing to a fresh or aspiring data scientist. In this post, we’re going to discuss exactly what kind of things do data scientists produce. …


WHY IS PYTHON A LANGUAGE OF CHOICE FOR DATA SCIENTISTS?

According to various job advertisements for different data science positions, both Python and R belong to the most commonly mentioned and preferred skills. But a lot of studies have revealed that Python programming language is being used more by data scientists. But what exactly makes this language a preferred one for data scientists? In this post, we’ve tried to find out the answer.

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According to various job advertisements for different data science positions, both Python and R belong to the most commonly mentioned and preferred skills. But a lot of studies have revealed that Python programming language is being used more by data scientists. But what exactly makes this language a preferred one for data scientists? In this post, we’ve tried to find out the answer.

What makes Python ideal for data science?


WHAT ARE THE MAIN DIFFERENCES BETWEEN ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING?

Undeniably, both the terms artificial intelligence and machine learning belong to the most-used buzzwords these days. Almost every tech organization is using these terms when talking about their products or services. Unfortunately, there’re still lots of confusion within the common people about what are these two exactly. Let’s go through the key differences between artificial intelligence and machine learning.

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Undeniably, both the terms artificial intelligence and machine learning belong to the most-used buzzwords these days. Almost every tech organization is using these terms when talking about their products or services. Unfortunately, there’re still lots of confusion within the common people about what are these two exactly. Let’s go through the key differences between artificial intelligence and machine learning.

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