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.
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.
In recent years, machine learning has been one of the most talked about tech topics and is being applied to businesses widely. Put simply, this application of artificial intelligence allows computers to learn and improve without being programmed directly. The revolutionary technology presently forms a highly crucial aspect of countless established, as well as, burgeoning industries. Let’s have a look at the key reasons why you should start preparing now to become a machine learning professional.
These days, terms like data science, machine learning and artificial intelligence are sometimes mentioned interchangeably, albeit incorrectly. Even an organization offering a new technology powered by any of these may talk about their high-end data science techniques without having much knowledge about them.
Artificial intelligence together with its most talked about subcategory machine learning are probably the biggest two factors impacting the entire business world and transforming it. We may not always realize how these technologies are involved in our day-to-day life, but in reality, they’re present in a lot of aspects. In a business context, almost every industry leverages the power of artificial intelligence and machine learning – from traveling industry to transportation industry to the healthcare industry and many more. In this post, we’re going to explore the impacts of these two technologies on the finance industry.
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.
In the tech domain, there is a huge buzz going around the future abilities of AI and machine learning in terms of how they’ll be impacting our lives. These include high-end things like instant machine translation, self-driving cars, just to name a few. However, AI and machine learning are very much present in these days and they are facilitating human lives in a lot of ways, whether you may realize it or not. In this post, we are going to take a closer look at how these technologies have already started impacting the life of the average people.
You may have already heard that computer coding is one of the most crucial job skills in today’s tech landscape. Coding is infiltrating virtually almost every aspect of work and it’s becoming nearly indistinguishable from common work since tech-related tasks are becoming important parts of even those fields that are less technical. In other words, learning code gives you that much needed competitive edge in today’s job market, no matter whether you do it full-time or part-time. In this post, we’re going to discuss the reasons for which you must concentrate on learning code.
Today, Python has become one of the most favored programming languages among developers across the globe – from process automation to scripting to web development to machine learning – it’s used everywhere. Before we delve deeper to understand why Python is steadily becoming a great choice among machine learning professionals, let’s have a quick look at where actually the study of algorithms helps in.
In today’s world of cutting-edge technologies, implementation of image processing techniques has become a crucial part for many tech organizations, regardless of their volume and field of operation. Acquisition of instant information has become possible because of the advancements taking place in the domain of the internet.