Today, artificial intelligence is used for a wide range of activities in different fields and industries. From education, healthcare, and entertainment to finance, electronic trading platforms, e-commerce platforms, transportation, and more, you’ll find how artificial intelligence finds many applications in our lives today. No wonder why many aspiring job seekers want to learn artificial intelligence to enter a promising field that’s changing almost every aspect of our lives significantly and is predicted to continue doing so in the future, albeit in a much more extensive way. …
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:
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.
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.
As a data science professional, you may have worked on lots of crucial problems, but if you fail to present them to the recruiters, getting a good job in the field may become even more difficult. And this is exactly…
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.
When it comes to the structure of big data, you can consider it a collection of data values, the relationships between them together with the operations or functions which can be applied to that data.
These days, lots of resources (social…
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.
Probably you already know that natural languages, which are used by humans for communications, are difficult to define with a specific set of rules. By using the combined power of computer science, computational linguistics, and artificial intelligence, NLP or natural language processing helps machines to understand that natural language. The technique uses machine-based algorithms that have the ability to obtain meaning from communication both verbal and written. Natural language processing is widely used in language translation, recognizing human speech, information retrieval etc.
While some may argue that programming language is just a tool to equip an NLP project, it all…
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.
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.
First of all, data science is often described as a multidisciplinary field which uses scientific processes, methods, systems, and algorithms to derive insights and knowledge from data. The emergence of…
Most likely you have already come across the term blockchain which is taking the tech world by storm these days. Put simply, blockchain can be considered as a digital ledger where facts are kept in data blocks. These data blocks are connected to each other through cryptographic validation. The entire system leads to an immutable record of data which is managed by a cluster of computers. Let’s see some most common use cases of blockchain to understand why experts call it one of the biggest disrupting innovations ever.