Full Stack Big Data

With humans and machines joining forces more than ever before, AI is no longer restricted to innovation labs and is being hailed for its immense transformational possibilities. Though, organizations need to overcome particular challenges before they can realize the real potential of this evolving technology. The key lies in leveraging the right prospects in AI. Provability Companies involved in AI cannot validate clearly why it does and what it does. No matter AI is a "black box." People are uncertain about it, as they fail to comprehend how it makes decisions. Provability – the level of mathematical cert behind AI predictions – remains a grey area for enterprises. There’s no way they can guarantee that the intellect behind the AI system’s decision-making is clear. The solution lies in making AI explicable, provable, and transparent. Businesses must embrace Explainable AI as a best practice. Data privacy and security Most AI applications depend on massive volum

The newfound passion for data science in the present computing world isn’t baseless. Rated as the top job on offer by Harvard Business Review along with lucrative paychecks, the lacunae in the current skills of experts compared to the industry standard skill-set necessary for the position of a data scientist means there is very much already that comes with learning data science. In such a setting, what gives you a competitive edge? Here are a few steps to follow on your path to becoming a data scientist! 1. Develop Skills in Statistics, Mathematics, and ML A data scientist is someone who is better at mathematics and statistics than any software engineer and also better at software engineering than any statistician. You just need to have the right balance of all these, to ensure you’re set up well to develop your data science skills. 2. Learn to Love (Big) Data Data Scientists have to handle a massive amount of structured and unstructured data on which computations

Artificial intelligence (AI) has now become an integral part of our lives. It’s how Amazon suggests products, Google answers our searches, and Pandora plays another song. AI allows scalable growth, and personalizes customer experiences for marketers. AI is a powerful marketing strategy as it allows marketers to outdo themselves in their roles by interacting with their customers through targeted messaging — all at scale. Here are the five ways AI can assist sales and marketing in finding leads. 1. Support Sales with Appropriate Customer Experiences Customers now expect personalized experiences and interactions through their favorite channel. AI-driven predictive content tools are allowing marketers to be more tactical, while reducing the workload.  These AI-driven marketing programs can examine your website for case studies, white papers, blogs, articles, videos, eBooks, etc. Once the content gathered, AI foresees what will appeal and ultimately convert each audience

As business organizations are building more diverse and user-centric data products and services, the demand for machine learning is growing rapidly for predictive insights, personalization, and recommendations. Earlier, data scientists were able to solve these problems using popular tools such as Python and R. But as companies are producing and amassing a large amount of data, data scientists are spending a major portion of their time supporting their data infrastructure rather than creating the models to solve data problems.   To help in solving this problem, Apache Spark offers a general machine learning library known as MLib, which is exclusively designed for simplicity, scalability, and quick integration with other tools. With the scalability, speed and language compatibility of Apache Spark, data scientists can solve and iterate through their data problems easier and faster. Undoubtedly, MLlib’s adoption is growing very quickly as can be seen through the large number of

If the title of this post interests you, the chances are high that you are planning to make a career in the field of big data. As the big data world is growing very fast, where you end up in it mainly depends upon your first step. Fortunately, we are going to start from scratch and give you a complete guide to pursuing a career as a big data architect. Before diving deep into the subject of big data architect, let’s take a sneak peek at what’ll get covered in this post. ·          Who is a big data architect? ·          The job description of a big data architect ·          The necessary qualifications for a big data architect Who is a big data architect? In the computer science world, ‘architecture’ means building computer systems. The “systems” can be large and complex, and to keep them running, you should strictly follow a list of rules and methods.  In other words, a big data architect is a person who comes up with certain rules and methods and meticulously implies them

In this digital era, a single click of the mouse on the “Agree” button can provide enterprises complete access to almost all your information. For many business organizations, collecting user information is vital to be able to send the most personalized messages to its customers.   Today, big data has become a fundamental part of communication and marketing efforts to augment value for businesses – as shown by Netflix and Spotify, which have utilized their huge user data to create engaging messages for their targeted audience.   However, this has caused fury amongst those who think that if you are using personal data without adding any value, it’s annoying, not amusing. Owing to the frequent misuse of information and data leakage, there is a growing fear for privacy and security of the data. The advancements in technology are bringing myriad opportunities and possibilities for businesses, but how well are they prepared for the potential risks associated with data use?   Sm