The advancements in systems deploying large language models (LLMs), as well as improvements in their ability to act as agents with predefined templates, provide an opportunity to conduct qualitative, individualized assessments, creating a bridge between qualitative and quantitative methods for candidates seeking career progression. In this paper, we develop a platform that allows candidates to run AI-led interviews to assess their current career stage and curate coursework to enable progression to the next level. Our approach incorporates predefined career trajectories, associated skills, and a method to recommend the best resources for gaining the necessary skills for advancement. We employ OpenAI API calls along with expertly compiled chat templates to assess candidate competence. Our platform is highly configurable due to the modularity of the development, is easy to deploy and use, and available as a web interface where the only requirement is candidate resumes in PDF format. We demonstrate a use-case centered on software engineering and intend to extend this platform to be domain-agnostic, requiring only regular updates to chat templates as industries evolve.

The Meteorology is a field where huge amounts of data are generated, mainly collected by sensors at weather stations, where different variables can be measured. Those data have some particularities such as high volume and dimensionality, the frequent existence of missing values in some stations, and the high correlation between collected variables. In this regard, it is crucial to make use of Big Data and Data Mining techniques to deal with those data and extract useful knowledge from them that can be used, for instance, to predict weather phenomena. In this paper, we propose a visual big data system that is designed to deal with high amounts of weather-related data and lets the user analyze those data to perform predictive tasks over the considered variables (temperature and rainfall). The proposed system collects open data and loads them onto a local NoSQL database fusing them at different levels of temporal and spatial aggregation in order to perform a predictive analysis using univariate and multivariate approaches as well as forecasting based on training data from neighbor stations in cases with high rates of missing values. The system has been assessed in terms of usability and predictive performance, obtaining an overall normalized mean squared error value of 0.00013, and an overall directional symmetry value of nearly 0.84. Our system has been rated positively by a group of experts in the area (all aspects of the system except graphic desing were rated 3 or above in a 1-5 scale). The promising preliminary results obtained demonstrate the validity of our system and invite us to keep working on this area.

Computer programming represents a rapidly evolving and sought-after career path in the 21st century. Nevertheless, novice learners may find the process intimidating for several reasons, such as limited and highly competitive career opportunities, peer and parental pressure for academic success, and course difficulties. These factors frequently contribute to anxiety and eventual dropout as a result of fear. Furthermore, research has demonstrated that beginners are significantly deterred by the fear of failure, which results in programming anxiety and and a sense of being overwhelmed by intricate topics, ultimately leading to dropping out. This project undertakes an exploration beyond the scope of conventional code learning platforms by identifying and utilising effective and personalised strategies of learning. The proposed solution incorporates features such as AI-generated challenging questions, mindfulness quotes, and tips to motivate users, along with an AI chatbot that functions as a motivational aid. In addition, the suggested solution integrates personalized roadmaps and gamification elements to maintain user involvement. The project aims to systematically monitor the progress of novice programmers and enhance their knowledge of coding with a personalised, revised curriculum to help mitigate the fear of coding and boost confidence.

Numberly is recognized as one of the world's leading data marketing specialists with nearly 500 employees and 8 offices worldwide serving more than 500 blue-chip clients (L'Oréal, P&G, Groupe Seb, HSBC...). By putting technology to work for brands and consumers, Numberly is at the heart of business growth and everyone's desire for more responsible and relevant marketing. Numberly is looking for a Data Engineer to join its dedicated team Data. Create and maintain pipeline jobs that transfer client data to/from our database diverse infrastructure (Hive, MS SQL Server, MongoDB, ScyllaDB). Nurture our large Hadoop cluster, optimize distributed Data Operations and Storage.

This article was published as a part of the Data Science Blogathon. You must have seen streaming services such as Spotify, Deezer, and Apple Music. So, what better way to flex our backend skills than to work with MongoDB to create our own Spotify backend clone, all with NodeJS? In this article, I will show you how to handle uploading songs to the database, streaming music, user authentication, the ability to choose your favorite songs, and a recommendation engine using machine learning. First up, here is how to set up MongoDB Atlas for NodeJS. You can either register with your email address or use a GitHub or Google account to log in. Enter the new project name and click Next.

The database market is massive (IDC estimates it to be $121B by 2025!) and MongoDB is at the head of its disruption. At MongoDB we are transforming industries and empowering developers to build amazing apps that people use every day. We are the leading modern data platform and the first database provider to IPO in over 20 years. Join our team and be at the forefront of innovation and creativity. MongoDB is growing rapidly and seeking a Data Engineer to be a key contributor to the company's Internal Data Platform.

Databases are fundamental to training all sorts of machine learning and artificial intelligence (AI) models. Over the last two decades, there has been an explosion of datasets available on the market, making it far more challenging to choose the right one for your tasks. At the same time, the larger number of datasets means you can find the perfect fit for whichever application you're aiming towards. Powered by Oracle, MySQL is one of the most popular databases on the market. Created in 1995, it has consistently been one of the top open-source relational database management systems (RDBMS) used by major companies like Facebook, Twitter, Uber, and Youtube. What led to its rise in popularity?

Before directly jumping into which database to use in Machine Learning, it is very important to know and understand the uses of different types of databases. In Machine Learning, we can use any of the databases either SQL-based or NoSQL-based. But then also, there are various reasons because of which various NoSQL databases are extensively used in the industry. Some of the reasons Why NoSQL databases are chosen over MySQL in Machine Learning, Computer Vision and, Natural Language Processing for large-scale projects? SQL databases can store a large amount of data, but only in one machine that is the biggest flaw in SQL databases.

In the first part of this series, we introduced Play framework, a web development framework for Java and Scala developers, and we showed how it enabled one to expose a basic REST API with minimal effort. In this second part, we explore some of the features of Play and use real code to illustrate its capabilities. Play supports both synchronous and asynchronous REST services, and it's particularly powerful when dealing with asynchronous requests. Whatever the request, an Action always returns an instance of play.mvc.Result. This can be directly returned to the consumer in the case of a synchronous service, or it can be served up as a Future.

You can now learn Python coding with RESTful API's using the Flask framework. In this Python Flask training course, you will have a deeper knowledge and understanding of core elements of web development using Python, along with the understanding of how to use MongoDB, Docker, and Tensor flow. With this course's classes, you will learn and develop the knowledge on how to plan, build, set up and deploy a RESTful API to an Amazon EC2 instance. You will also discover how to build a machine-learning API using Tensorflow for image recognition. Get to know and make use of a NoSQL (MongoDB) database.