Database Scientist

  Machine learning algorithms are computational methods that enable computer systems to learn from data, identify patterns, and make predictions or decisions without being explicitly programmed for each task. These algorithms are the foundation of artificial intelligence applications, allowing systems to improve their performance as they are exposed to more data. Machine learning algorithms are broadly categorized into three main types: Supervised Learning Algorithms:   These algorithms learn from labeled data, where the desired output is known for each input. The goal is to learn a mapping function from input to output so that the algorithm can accurately predict outputs for new, unseen data.Examples: Linear Regression, Logistic Regression, Support Vector Machines (SVMs),  Decision Trees, Random Forests, K-Nearest Neighbors (KNN), Neural Networks. Common Tasks: Classification (predicting categories) and Regression (predicting continuous values). Unsupervised Learning Alg...

  Cybersecurity is of the utmost importance in an era that thrives on technology. Cybersecurity protects all internet-connected systems, devices, and services from malicious cybercriminals.  We live in the digital era. Technology has permeated every part of our lives. Anything we need, we can find with a swipe of our finger. From advanced technologies, such as IoT, to the latest technologies, such as ChatGPT, technology has made our lives far simpler and more convenient. However, this convenience comes with its potential threats. Behind every technology we use, there's a potential threat of cyber crimes. The latest cybercrime statistics are terrifying in that aspect. Cybersecurity Ventures estimates that the cost of cybercrime globally can reach $10.5 trillion by 2025. This has made cybersecurity all the more essential.  In this blog, we have discussed everything you need to know about cybersecurity, what it is, its importance, types, myths, the latest challenges, and dom...

  As the world becomes data-driven, data management has only gotten more complex. We started this six-part series by stressing the importance of data quality. In this part, we will explore better ways to store, manage and analyze your data. While traditional data warehousing has been a go-to decision and popular choice for decades, recently two more data storage solutions have emerged: data lakes and data lakehouses. To help your data architecture decision making process, here’s an exploration of these modern data storage solutions and how they differ from traditional data warehousing. Data Warehouse A data warehouse is a centralized repository where wealth management firms can store all their structured and unstructured data in an organized, integrated and historical manner. In such a setup, the traditional data warehouse extracts, cleanses, transforms, and loads (ETL) data from various sources, allowing organizations to analyze the data easily for business intelligence, repo...

  Thanks to generative AI, we’re getting close to the promise of truly “democratizing” data. This means anyone can make decisions that are data-driven, not just highly skilled data scientists. Here ‘s the issue: The amount of data in the world is growing exponentially. And hidden within all of that information are insights that can potentially help us do anything from curing cancer and discovering new sources of clean energy, to improving our health and fitness and growing our businesses. So the problem isn’t that we don’t have enough information do these things. It’s that we have too much, and don’t know the best place to start! Traditionally, extracting insights and value from data required two things – very clever, highly trained people and lots of time. Here, AI has proven very useful. For the past decade, we’ve used deep learning algorithms to crunch numbers far quicker than any human, spotting patterns and pulling out insights. It also allows us to find clues hidden in unstru...

  If right now, you were to look around for the forms of artificial intelligence, you'd be surprised to notice how deeply AI has penetrated every facet of our life. You can command Alexa or Siri right now to play your favourite song. You can ask the AI systems in your phone to set the alarm for you, remind you to run errands, and perform countless other tasks.   As normalized as AI is in our lives right now, it was just a science fiction fantasy up until a couple of decades ago. So what changed in the last decades that brought AI to common people's lives, straight from sci-fi books and movies?   This article will take you on the journey of understanding what is artificial intelligence, its history, beginnings, types, and future. Let's get started. What is Artificial Intelligence The 'intelligence' in Artificial Intelligence refers to machines' ability to make an informed decision or actions based on the given information. Humans are called intelligent beings bec...

  The amount of data transferred between global business networks is very large. The amount of data transferred in a given period of time is the data transfer rate, which specifies whether the network can be used for tasks that require complex data-intensive applications. Network congestion, delays, server operating conditions, and insufficient infrastructure can cause data transmission rates to be lower than standard levels, thereby affecting overall business performance. High-speed data transfer rates are essential for handling complex tasks such as online streaming and large file transfers. The Importance of Content Delivery Networks High-quality delivery of websites and applications to as many locations in the world as possible requires infrastructure and expertise to achieve delivery with low latency, high-performance reliability, and high-speed data transmission. Professional content delivery networks can bring a variety of benefits, including seamless and secure di...