Applies to all roles 👇 This Career Week session with Tomás Tecce Anna Skripnikova Izaskun López-Samaniego & Jyothish Jayaraman i useful to watch no matter the role you're having right now.

The Great Tech Over-The Data Analyst Not even data is safe. AI algorithms can now process, analyze, and find patterns in massive datasets faster and often more accurately than humans. The data analyst's role is evolving from number-cruncher to insight-interpreter. Plan your career accordingly! #TheGreatTechOver #DataScience #AI #BigData"

The Great Tech Over-The Data Analyst Not even data is safe. AI algorithms can now process, analyze, and find patterns in massive datasets faster and often more accurately than humans. The data analyst's role is evolving from number-cruncher to insight-interpreter. Plan your career accordingly! #TheGreatTechOver #DataScience #AI #BigData"

“𝗦𝗶𝗺𝗽𝗹𝘆 𝘀𝗮𝘆 𝗟𝗲𝗮𝗿𝗻 𝗔𝗜” 𝗶𝘀 𝗻𝗼𝘁 𝗮 𝗴𝗼𝗼𝗱 𝗮𝗱𝘃𝗶𝗰𝗲 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀. The problem isn’t learning AI. It’s understanding what actually changes. Your day-to-day work? The skills that are still important (and the ones that aren’t)? When not to build with AI at all? I’ll be speaking with Alejandro Aboy , Senior Data and AI Engineer and writer of The Pipe & The Line, to break this down. We’ll cover: - Using AI vs building AI systems - APIs vs custom solutions - Where AI helps (and where it doesn’t) - How to get started without overcomplicating things. If you're a data engineer trying to figure out AI, Join us on 𝟯𝟭 𝗠𝗮𝗿𝗰𝗵 at 𝟲:𝟯𝟬 𝗣𝗠 (𝗠𝗲𝗹𝗯𝗼𝘂𝗿𝗻𝗲, 𝗔𝘂𝘀𝘁𝗿𝗮𝗹𝗶𝗮 𝘁𝗶𝗺𝗲 – 𝗔𝗘𝗦𝗧) to dive deeper into this. Bring your questions. If you can’t attend, drop them in the comments, and we’ll cover them. link to live session: https://lnkd.in/gAuv-tX5 #pipelinetoinsights #dataengineering #ai #continuouslearning

🤔 Still confused between Data Science, AI & Machine Learning? Let’s clear it up 👇 🔍 Data Science → Turns data into insights 🤖 AI → Makes machines think like humans 📊 ML → Helps machines learn from data ✨ Key points: • Data Science = Analysis • AI = Intelligence • ML = Learning 💥 They’re connected — but not identical! 👉 Which one are you interested in? Comment below! 🌐 www.skillversed.com 📩 support@skillversed.com 👉 Which one are you interested in? Comment below! #AIExplained #MLBasics #DataScienceLife #TechSimplified #Upskill #LearnAI #TechCareers #skillversed #AI #ml #datascience

𝗔𝗜 𝗧𝗵𝗲𝗻 𝘃𝘀 𝗡𝗼𝘄: 𝗧𝗵𝗲 𝗕𝗶𝗴𝗴𝗲𝘀𝘁 𝗦𝗵𝗶𝗳𝘁 𝗶𝗻 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 AI didn’t go mainstream because it got smarter. It went mainstream because more people could use it. Julian Wiffen, Chief of AI and Data Science at Matillion nails the real takeaway: When AI became text-first, it stopped being “a data scientist thing” and became something anyone could try and that accelerated adoption at scale. What changed fast: 1️⃣ AI moved beyond experts 2️⃣ Text made it easy to try 3️⃣ Messy data started giving usable outputs 🎥 𝗗𝗿𝗼𝗽𝗽𝗶𝗻𝗴 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻 𝗹𝗶𝗻𝗸 𝗶𝗻 𝘁𝗵𝗲 𝗰𝗼𝗺𝗺𝗲𝗻𝘁𝘀. #AI #ChatGPT #Short #Technology #ThenVsNow #MessyData #Adoption

Agentic AI is becoming an important part of how we work with data. Nowadays I am exploring Agentic AI and how it is shaping the future of data science and data analytics. Simply Agentic Ai can take actions and make decisions to complete tasks instead of humans, not just giving answers about questions. This means many data tasks like collecting, cleaning and analyzing data can be done more efficiently with AI support. As data professionals this allows us to spend more time in understanding insights and making better decisions. 🙂 !!!! 🚀الى أين ستأخذنا أيها الذكاء الإصطناعي ---------------------------------------- #AgenticAI #DataSience #DataAnalytics #AI #ArtificialIntelligence #DataAutomation

🚀 Day 9 of My AI & Machine Learning Journey at Africa Agility Foundation Today���s learning focused on Exploratory Data Analysis (EDA), an important step before building Machine Learning models. EDA helps us understand the dataset by exploring patterns, relationships, and key characteristics of the data. It allows us to ask important questions such as: 📊 What does the data look like? 📈 Are there patterns or trends? 🔍 Are there missing values or outliers? By exploring the data first, we can make better decisions when preparing it for Machine Learning models. One key lesson I learned is that understanding the data is just as important as building the model. The better we understand our data, the better our models will perform. I’m grateful to continue learning and building my skills in AI and Machine Learning every day. #MachineLearning #AI #ExploratoryDataAnalysis #AfrikaAgility #WomenInTech #LearningJourney 🚀📊

Day 3/20 Why Data Cleaning Matters in Machine Learning One thing I am starting to realize is that machine learning is not just about models. A big part of the work actually happens before the model is even built. This part is called data cleaning. Data cleaning means preparing your dataset so it is accurate, complete, and ready to be used. In real life, data is often messy. You might find missing values, duplicate records, or incorrect entries. If you train a model on bad data, the results will also be bad. There is a common saying in tech. Garbage in, garbage out. Some basic things involved in data cleaning are: ➡️Removing duplicates ➡️Handling missing values ➡️Fixing errors in the data ➡️Making sure everything is in the right format Clean data helps the model learn better patterns and make more reliable predictions. So before thinking about fancy algorithms, I am learning to respect the process of preparing data properly. Still learning step by step in my AI and Machine Learning journey. #AI #MachineLearning #DataScience #LearningJourney #WomenInTech #20daysconsistencychallenge

Confused by the tech buzzwords? Let's break down AI, ML, and Data Science. 🤔 People often use these terms interchangeably, but they mean different things! 🤖 Artificial Intelligence (AI): The overarching goal of creating machines that can mimic human intelligence and behavior. ⚙️ Machine Learning (ML): A specific method within AI. It uses statistical methods to enable machines to learn from data and improve with experience. 📊 Data Science: A broader field that extracts insights from data. A Data Scientist might use ML, but they also use traditional statistics, data visualization, and business analytics. AI is the vision, ML is the engine, and Data Science is the entire vehicle! #ArtificialIntelligence #MachineLearning #DataScience #TechBasics