Ankit Abhishek’s Post

The 5 Best Data Science Blogs To Follow: There are countless excellent resources on data science, and it can be a little overwhelming to know where to start. Here are some options: 1. Data Science Central Run By: Vincent Granville  Website link: DataScienceCentral.com Data Science Central does exactly what its name suggests and acts as an online resource hub for just about everything related to data science and big data. 2. SmartData Collective Run By: Social Media Today  Websitelink: SmartDataCollective.com SmartData Collective is a community site focused on trends in business intelligence and data management. 3. What's The Big Data? Run By: Gil Press Website link: WhatsTheBigData.com What's The Big Data? takes a different approach to data science and focuses on the impact of big data’s growth into the digital behemoth it is today. T 4. No Free Hunch Run By: Kaggle  Website link: Blog.Kaggle.com This blog is slightly different than the others, offering a look directly into the minds of data scientists, as well as tutorials and news. This is the blog of the data science website Kaggle, which hosts data science projects and competitions that challenges data scientists to produce the best models for featured data sets. 5. InsideBIGDATA Run By: Rich Brueckner  Website link: InsideBIGDATA.com InsideBIGDATA focuses on the machine learning side of data science. It covers big data in IT and business, machine learning, deep learning, and artificial intelligence. Guest features offer insight into industry perspectives, while news and Editor’s Choice articles highlight important goings-on in the field. Happy reading Karl Ramsaran - Data AI Growth Talent Partner

As a Gen AI product owner, I completely resonate with the points made in this article in Forbes by Gil Press regarding the frustrations surrounding data preparation in data science. It’s alarming to see just how much time and energy data scientists spend on cleaning and organizing data rather than working directly with insights. This not only drains their creativity but can also lead to burnout. In our own projects, we’ve noticed that streamlining this process is key to enabling our teams to focus on what truly matters: analysis and innovation. Moreover, I see a tremendous opportunity for generative AI to transform how we handle data preparation. By developing intelligent systems that learn from past tasks, we can ease the burden on users and help them work more efficiently. Imagine a future where data preparation is almost seamless, allowing data professionals to spend their time on strategic insights instead of tedious repetitive and mundane tasks. This vision drives us to create solutions that not only meet user needs but also foster a more dynamic and enjoyable work environment. This article resumes how we can unlock the full potential of data science and harness its power for better decision-making. #DataScience #AI #DataPreparation #MachineLearning #Automation #BigData #DataAnalytics #DataDriven #GenerativeAI #TechInnovation

LLMs are reshaping modern data engineering, offering immense potential to revolutionize workflows and deliver enhanced value. In my recent blog post, I tried to provide a Data Engineer's perspective on how LLMs can empower DEs to drive innovation and optimize efficiency. I’d love to hear your feedback or thoughts! #ModernDataEngineering #AI #LLM #MachineLearning #DataScience #Innovation #FutureOfWork

𝑫𝒆𝒍𝒗𝒊𝒏𝒈 𝒊𝒏𝒕𝒐 𝒕𝒉𝒆 𝑾𝒐𝒓𝒍𝒅 𝒐𝒇 𝑫𝒂𝒕𝒂: 𝑨 𝑷𝒆𝒓𝒔𝒐𝒏𝒂𝒍 𝑻𝒂𝒌𝒆 📊 The world of data is a vast and exciting landscape, brimming with possibilities. Here’s a glimpse into some key areas: 𝟭. 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀:   Data analytics involves analyzing data sets to draw conclusions about the information they contain. It focuses on uncovering trends, patterns, and insights to inform decision-making. It's like detective work with data, where you sift through information to find valuable insights. I admire its ability to translate raw data into actionable insights, making it crucial for businesses and organizations to stay competitive. 𝟮. 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴:   Data engineering is the foundation of any data-driven initiative. It's all about designing, constructing, and maintaining the infrastructure or system that collects, stores, and processes the massive amounts of data we generate. Think of it as building the infrastructure for data to flow smoothly from source to analysis. Its role in creating the backbone of data ecosystems, ensuring that data is accessible, organized, and ready for analysis is intriguing. Data engineering is the backbone of any data-driven organization. 𝟯. 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲:   Data science sits at the intersection of statistics, computer science, and domain expertise. This field feels like a fusion of analysis and problem-solving. Data scientists leverage various techniques to extract knowledge and build models from data. It involves extracting knowledge and insights from structured and unstructured data through various techniques, including machine learning, data mining, and visualization. It's like being a modern-day alchemist, turning data into gold by uncovering hidden patterns and predictions. I find its interdisciplinary nature fascinating, as it allows for creative problem-solving across a wide range of domains. 𝟰. 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴:   Ah, here's the one that truly sparks my passion! Machine learning is a subset of artificial intelligence that focuses on building algorithms that enable computers to learn from data and improve over time without being explicitly programmed. By feeding machines data and algorithms, we enable them to learn and adapt, even mimicking human-like behavior. It's like teaching computers to think and learn like humans, although in a specialized way. It powers many of the intelligent systems we interact with daily, from recommendation engines to autonomous vehicles. I'm intrigued by its potential to revolutionize industries and reshape the way we interact with technology. It's fascinating – the ability to create intelligent systems that can learn and improve on their own. That's why Machine Learning is the field that excites me the most. It allows me to witness the intersection of technology and a semblance of human-like intelligence, which I find truly captivating. Thanks to Cowrywise for sparking this exploration!

👀 Understanding Databases, Data Marts, Data Warehouses, and Data Lakes 👀 • Database: - Database: A database is an electronic repository for structured data from a single source where you can store, retrieve, and query it for a specific purpose. Imagine a digital address book for all your contacts. It stores information in an organized way, making it easy to find, edit, or search when needed. • Data Mart: - Databases that hold a limited amount of structured data for one purpose in a single line of business. Picture a mini-library just for one department, like a sales and marketing. If your sales team only needs data about recent sales, the data mart is their go-to space • Data Warehouse: - A relational database that can handle, store, and bring to one place structured data sets coming from multiple sources. Data warehousing supports business decision-making by analyzing varied data sources and reporting them in an informational format. This is the big library that pulls in structured data from multiple sources. It’s where all the company’s information comes together, helping you analyze and make smart business decisions! • Data Lake: - A large repository that houses structured, semi-structured, and unstructureddata from multiple sources. Data lakes are also an excellent feeding ground for big data, artificial intelligence, and machine learning programs. Picture a huge lake that can hold every kind of data -structured, semi-structured, and unstructured from various sources. This is where data scientists and AI/ML programs dive in to explore. #DataManagement #BigData #AI #MachineLearning #TechMadeSimple

Why are ontologies important in data management and AI? 6 key reasons. I think ontologies are very important for the future of data management and AI, but why? 6 things on my mind, feel free to add what's missing: 1. They provide a formal model of concepts and relationships that enable shared understanding. By defining classes, properties, and restrictions, ontologies create a common vocabulary and semantics around a domain. This facilitates interoperability and integration across systems and organizations. 2. They enable automated reasoning and inference. The formal logic-based representations of ontologies allow logical inferences to be made, deriving new knowledge from asserted facts. This kind of automated reasoning allows systems to check consistency, analyze the implications of data, and make recommendations. 3. They structure and organize knowledge for reuse. Ontologies provide an abstract framework for categorizing and relating entities to support explainability and reuse across applications. This semantic structure enables knowledge to be modularized instead of rebuilt from scratch for every use case. 4. They support machine learning transparency and accuracy. Providing context around training data characteristics, relationships, constraints etc. ontologies can improve ML model transparency, fairness, and accuracy. They also support the validation and monitoring of model performance over time. 5. They help ground AI systems and balance the potential for hallucination. Large language models "hallucinate" false information if not properly grounded. Ontologies provide a formal factual framework to map each of our realities and ensure language models align to truth and facts. 6. And last but not least, they help align data engineers and data scientists. Data engineers focus on building data pipelines while data scientists focus more on unlocking potential...this can leave gaps in formal data modeling. Ontologies can provide a unified semantic model spanning the full data lifecycle from integration to analytics and machine learning. This bridges the gap by enabling data engineers to incorporate more meaning and structure upfront, while still supporting flexibility for data scientists downstream. In short, ontologies move from "half messy half organized" data to formally defining a shared map of an organization's reality around data. This additional meaning, which can sit on top of the current data warehouse, data lakes, or traditional database, enables more intelligent systems and more meaningful data integration across platforms and organizations. We need ontologies to create more lean, efficient, and resilient systems. It's not like you are going to tidy up your room with some magic overnight, so let's get to work!

The importance of ontologies in data management. #Ontology #LinkedData #ConnectedData #KnowledgeGraph #KnowledgeRepresentation

Had to reshare this simple and well articulated defense of ontologies.

How GenAI is Transforming the Field? GenAI is not just another buzzword in the world of data analytics; it is a game-changer that is revolutionizing the field. The potential impact of GenAI on data analytics is enormous, and its transformative power cannot be overstated. In this section, we will explore how GenAI is transforming the field of data analytics and revolutionizing the way we analyze and derive insights from data. One of the most significant ways in which GenAI is transforming the field is by tackling the challenges posed by big data. Big data, as we know, refers to the massive volumes of data that are too complex for traditional data processing methods to handle. GenAI's ability to generate its own data and insights makes it uniquely equipped to handle big data. By generating new data and insights, GenAI can uncover patterns and correlations that traditional methods may have missed. This opens up new possibilities for analysis and provides us with a deeper understanding of the data. Moreover, GenAI is also transforming the field by addressing the challenge of analyzing unstructured data. Unstructured data, such as text documents, images, and videos, poses a significant challenge for traditional data analytics methods. However, GenAI has the ability to extract meaning and patterns from unstructured data, enabling a more comprehensive analysis. This not only enhances our ability to gain insights from unstructured data but also opens up new opportunities for data analytics in areas such as natural language processing and computer vision. Another way in which GenAI is transforming the field is by enabling real-time analysis. Traditional data analytics methods often require time-consuming processes such as data cleaning and preprocessing. However, GenAI has the capability to process and analyze data in real-time, providing instantaneous insights and predictions. This allows organizations to make data-driven decisions on the fly and respond to changing market conditions in a timely manner. Furthermore, GenAI is also transforming the field by democratizing data analytics. In the past, data analytics was primarily the domain of data scientists and experts. However, GenAI's ability to automate the analysis process and generate insights without human intervention makes data analytics more accessible to a wider audience. This empowers individuals and organizations to make data-driven decisions and derive insights from data without the need for specialized knowledge or expertise.