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16
Sep

0

What Is The Role Of Data Science In Everyday Life And Every Situation?

There’re lots of people who closely associate data with privacy issues, data leaks, and profit maximization techniques. Also, terms like data science, analytics, statistics, databases etc are ones that most people associate with the professional world exclusively. However, all these and many of their associated ones aren’t meant for data science professionals only. In reality, most people experience the role of data science in their day-to-day lives and in almost every situation. From new friend suggestions by Facebook to Google’s help to complete a search phrase to television shows predicted by Netflix in accordance to your preferences, and many more – data science is being used by common people in almost every situation.

16
Sep

0

What Are The Differences Between Data Scientist And Data Engineer?

The domain of data science has been at the focal point of discussion for quite a few years now and there are no signs of it slowing down. As more and more businesses, organizations, and companies are waking up to the importance of extracting important insights from the pile of data that they are sitting on, the demand for data scientists, data engineers, and other experts in the field has increased significantly. No wonder that while there’s an increased focus on bringing such data science talent onboard, a whole new set of data science titles and roles too have been created to address the needs of the market.

15
Sep

0

How Can You Improve Yourself In Data Science Area To Be A Good Data Scientist?

The role of data scientist surely involves a lot of great things and those are the reasons professionals from across the globe are striving to step into the field of data science. Businesses, regardless of their field and volume, are looking to recruit ‘effective’ data scientists. We mentioned the term ‘effective’ because there is a huge supply of so-called data scientists that often fail to meet the expectations. The continuing media hype around data science has heavily exploded the volume of junior talents over the past few years.

13
Sep

0

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.

13
Sep

0

What Exactly Does Data Science Mean? Is It Really Going To Revolutionize The Industry?

You’ve probably heard the fact that the role of data scientists has been declared as the hottest job of the 21st century. If you’re not much interested in knowing about the latest advancements or buzzing technologies that are taking the tech world by storm, it’s quite normal to wonder why businesses, regardless of their volume and industry, are emphasizing so much to hire these data science professionals. In this post, we’re going to learn what data science actually stands for and how it can benefit the industry.

10
Aug

0

How Do You Build A Data Science Portfolio?

There’re a lot of people trying to step into the field of data science. Unfortunately, many of them often overlook one of the most critical aspects of landing up a good job in the field – the importance of building a strong data science portfolio. While having enough knowledge about different data science techniques and a good number of certifications are surely critical, unless you have a strong data science portfolio, your chances of coming under the radar of recruiters aren’t extremely high. Here, we’ve jotted down the key aspects of building a solid data science portfolio that would make your journey a tad easier.

10
Aug

0

The Most Powerful Idea In Data Science

In the tech fields these days, there’re a huge number of people trying to embark on different types of new paths that eventually lead to having a career in the field of data science. Undeniably, the goal is a worthy one, but it’s also important to have a clear idea about the key goal of data science. In this post, we’ll be trying to explore it. Let’s start the discussion.

10
Aug

0

What Are The Advantages And Disadvantages Of Big Data?

In today’s business landscape, big data has become the most valuable asset for any business. The more a business can harness big data, the better its position becomes from where it can carry out analysis that helps to develop useful business decisions. Across every industry, big data is being heavily used to predict future trends, recognize patterns, and draw new conclusions. However, like every technological advancement, big data also comes with equal shares of advantages and disadvantages. Let’s have a look at them.

10
Aug

0

What Tools Do You Use To Perform Data Analysis?

Data analysis comes with the goal of deriving useful information from data, suggesting conclusions, and supporting critical business decision making. There’re lots of data analysis tools that can be utilized to help a business to get a competitive edge. If you’re trying to step into the field of data analysis, it’s extremely important to have a good working knowledge of the most commonly used data analysis tools. In this post, we’re going to discuss five such tools by learning which you’d be able to propel your career in data analysis.

10
Aug

0

What Is The Structure Of Big Data?

In the last few years, big data has become central to the tech landscape. You can consider big data as a collection of massive and complex datasets that are difficult to store and process utilizing traditional database management tools and traditional data processing applications. The key challenges include capturing, storing, managing, analyzing, and visualization of that data.

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