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.
Data Science Blog

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.
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.
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.
Data Analysis Tools That You Use to Perform
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.
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.
What Is A Good Data Science Project?
A significant number of newcomers in data science tend to spend a huge amount of time to develop theoretical knowledge and earn certifications only. While theoretical knowledge is certainly required to become a good data science professional, recruiters don’t put much emphasis on certifications only. Instead, they tend to evaluate the potential of a candidate by going through his/her work.
Is Being A Data Analyst A Step To Becoming A Data Scientist?
Being a data analyst would mean you’ll have several skill-sets that one needs to work in the domain of data science. However, it doesn’t mean you can easily jump from your data analyst career into the role of a data scientist. Before discussing if being a data analyst could act as a step to becoming a data scientist, let’s take a look at what each of these professionals do.
What Need To Know In A Data Scientist Career?
From businesses and government institutions to non-profit organizations, there is a seemingly-infinite quantity of data that can needs to be sorted and interpreted to get information that can be applied for a wide array of purposes. With “big data” being the buzz word these days, pursuing a data scientist career has become one of the hottest trends of modern times. Whether you take up a full-time data science course or opt for a data science bootcamp in Silicon Valley, you’ll need to known certain trending technologies (as mentioned below) to make a mark in this field: