PowerBI vs. Tableau: When it comes to data visualization and analytics, both Power BI and Tableau are industry leaders, but each has its unique strengths. Here's a quick comparison to help you decide which one is right: 📍 Power BI 🔹 Self-Service Analytics: Ideal for users with little to no technical expertise, making it easy to create reports and dashboards. 🔹 Data Connectors: Offers a wide range of connectors, allowing seamless integration with various data sources. 🔹 User-Friendly Interface: Known for its intuitive design, making it accessible to beginners. 🔹 Advanced AI/ML Capabilities: Includes features like natural language processing (NLP) for more advanced analytics. 🔹 Strong Visualization: Provides powerful visualization tools, though slightly less sophisticated than Tableau. 🔹 Flexible Deployment: Available as both a cloud-based service and an on-premise solution. 📍 Tableau 🔹 Advanced Analytics: Combines self-service capabilities with powerful tools for data professionals. 🔹 Robust Data Connectors: Extensive set of connectors, including APIs and custom options, for more complex data integration. 🔹 Steeper Learning Curve: Offers greater depth and functionality, but requires more time to master. 🔹 Superior Visualizations: Renowned for creating beautiful and interactive dashboards that captivate audiences. 🔹 Less Advanced AI/ML: Not as strong in AI and machine learning as Power BI. 🔹 Cloud-Focused: Primarily a cloud-based solution with more limited on-premise options compared to Power BI. Choosing the right tool depends on your specific needs and expertise. Whether you prioritize ease of use and advanced AI (Power BI) or superior visualization and robust data integration (Tableau), both tools offer powerful solutions to transform your data into actionable insights. Which one do you prefer for your data projects? Share your thoughts! #DataScience #PowerBI #Tableau #DataVisualization #BusinessIntelligence #Analytics

"What is it that you do?" is a question I am often asked in different get-togethers and reunions (and somehow there have been quite a few of them lately). My standard one sentence reply is, "I am a freelance consultant in Data Science", which either tends to stop the conversation completely or more likely, leads to the inevitable question: "Do you do AI? Chat GPT?". To which I somehow sheepishly reply "Yes, AI", though I, like most Data Scientists, am not really sure of what AI means. I do know though, that Artificial Intelligence in general - and Generative AI in particular - has become the biggest selling point of the Data Science industry. Hence, companies claim to use AI extensively and every new product (or old) is now backed by AI models. Having been in this industry for almost 15 years though, for me AI would be AI only when we are able to build replicants in Blade Runner, and even though Generative AI is a huge, huge step, it will still be some time before Generative AI becomes mainstream for all companies to effectively use. So what is it that I actually do? I experiment with Data - and hence the term Data Scientist. I believe the major limitation with Data Science today is the gap between business requirements of the stakeholders and the technical understanding of the Data Scientists. While the stakeholders know their business very well, they are often unable to grasp the technicalities of the complex Data Science solutions. On the other hand, while many Data Scientists are aware of different Data science solutions, they sometimes falter in understanding the exact business requirements or explaining the complex algorithms which constitute the solution. As such, the solution is either very different from client requirements or is considered a black-box and hence rendered undeployable. I help bridge this gap by: a) proposing the best solution to stakeholders; b) building the complete solution; c) explaining the solution to stakeholders; and d) implementing the solution for long term impact. I bring a wealth of experience working with diverse datasets across various industries, allowing me to adapt to unique business needs and deliver actionable insights. If you're interested in exploring how data science can drive growth and innovation within your organization, I'd be thrilled to connect and discuss potential collaborations. Feel free to reach out to me via LinkedIn messaging.

📢 Call for participation in our companies' survey on assessing the ICT skills gap in the Data Analytics and Machine Learning fields! 📊 ✨ Does your company operate in Greece, Italy, Sweden, or Turkey and utilise Data Analytics and Machine Learning techniques in its operations? ✨ Would you like to play a pivotal role in bridging the skills gap between the demand (ICT labour market) and the supply (candidates) perspectives, in the fields of Data Analytics and Machine Learning? ✨ Would you like to join us in empowering prospective candidates with skills and competencies highly sought after in the ICT labour market, in the fields of Data Analytics and Machine Learning? 🎉 If you answered yes to the above questions, we need your expertise and insights! 📋 We would like to invite you to participate in our survey titled: "Assessing the ICT Skills Gap in the Data Analytics and Machine Learning fields from the companies perspective" 📈 With your input, we seek to: 💡 Gain a deeper understanding of the current state of the ICT labour market in the consortium countries 💡 Identify the most pressing ICT skills shortages in the fields of Data Analytics and Machine Learning 💡 Develop a targeted and relevant curriculum that meets the needs of both learners and the labour market. 📢 Make an Impact! We would like your input on: 🔧 The general (methodologies and techniques, e.g. Regression Analysis, Natural Language Processing (NLP), Agent-Based Modeling, Optimisation, etc.) and the specific (languages and technologies, e.g. Python, R, Scikit-learn, TensorFlow, Tableau, SQL, Hadoop, etc.) skills most commonly used in your company, in the fields of DA/ML. In other words, a listing of the most frequently encountered skills and competencies in your DA/ML job postings. This will help us include the most in-demand skills in our courses. 💼 A listing of some of the most frequently encountered use-case scenarios (e.g. Customer Segmentation and Targeted Marketing, Fraud Detection and Prevention, Personalised Healthcare Recommendations, etc.), within your company, in the fields of DA/ML. This will help us develop hands-on activities that will be more realistic and more relevant to the labour market, to include in our courses. 🎯 Your input will directly shape the development of our courses to make them more targeted, relevant, and effective, thus contributing to bridging the skills gap and widening the pool of prospective candidates for the ICT labour market, in the fields of Data Analytics and Machine Learning. 🌐 Why Participate? 🌱 Shape the development of high-impact educational content 🌱 Connect with a pool of high-performing students 🌱 Join a dynamic community dedicated to driving innovation in Data Analytics and Machine Learning 🎉 Thank you for your commitment to advancing the field of Data Analytics and Machine Learning. For more information please visit https://lnkd.in/dk2PVV8s.

Hi all, One of my connections is looking for freelance projects in #datascience, #machinelearning , and #webdevelopment He has expertise in- Data Science & ML: Cleaning, EDA, Statistical Analysis, Supervised & Unsupervised Learning, Ensemble Methods. Neural Networks & DL: ANN/CNN/RNN, Transfer Learning, TensorFlow, PyTorch, NLP with Transformers. Web Dev & Data Viz: Flask, Streamlit, Interactive Visualization, Power BI, Matplotlib & Seaborn. Version Control & Python: GitHub, Pandas, NumPy, Scikit-Learn. He has previously delivered impactful projects like sentiment analysis (85% accuracy) & medical imaging diagnostics. Please feel free to connect to explore collaboration opportunities. 📞 Ph: 8708623051 (Whatsapp Only) #freelance #datascience #freelancedeveloper #dataanalytics #artificialintelligence #ml #neuralnetworks #datavisualization #programming #github #flask #pytorch #tensorflow #nlp #datacleaning #statisticalanalysis #webdev #datascientists #dataengineering #datadriven #tech #codereview #collaboration #datainsights #dataprojects #webdevelopment #python #pythonprogramming #deeplearning #ai

Join us in Sonoma County in January. Have some food and wine. Hear three exciting talks about developing with AI platforms, tools, unstructured data, and generative AI. Stay the night to explore the bounty that is Sonoma County. Known for its great wine, food, coastal views, and redwoods. This is our inaugural event. If you'd like to speak, please let us know, there is one speaking slot. Registration required, we will be limited to 50 attendees so grab your spot now. Register here: https://lnkd.in/gGAm4P9U 3:30 - 4:30 - Welcome/Networking/Registration 4:35 - 5:00 - Christy Bergman, freelance AI Dev Advocate - data science using latest Gemini models from Google 5:05 - 5:30 - Paco Nathan, Principal DevRel Engineer, Senzing - Catching Bad Guys with AI apps 5:35 - 6:00 - Talk 3 6:05 - 6:30 - Networking Tech Talk 1: Unlock Data Science with the power of AI Speaker: Christy Bergman, freelance Abstract: With a background in Data Science, I’ll guide you through essential data science tasks—like clustering, prediction, and querying—using Python and AI tools in Google AI Studio. Learn how to harness AI-powered techniques for quick data science prototyping. Tech Talk 2: Catching Bad Guys using open data and open models for AI apps Speaker: Paco Nathan, Senzing Abstract: We'll show use cases for investigative graphs and downstream AI applications which leverage them, such as GraphRAG. In efforts such as countering money laundering (AML), ultimate beneficial owner (UBO - catching oligarchs), human trafficking, etc., there are open datasets based on whistleblowers' leaks from banks, law firms, and others involved in the netherworld of Dark Money. If you've seen the Netflix film "The Laundromat" or read Oliver Bullough's "Moneyland" exposé, you've already seen how terrible this hidden landscape is and the impact it has on our daily lives. In this talk we'll explore use of state-of-the-art (SOTA) large models for constructing knowledge graphs from watchlists ("bad guys"), corporate disclosure documents, investigative journalism articles, and other related data sources. In particular, we'll cover use of high-end entity resolution and entity linking to build highly accountable workflows which mitigate AI "hallucinations" while following evidence-handling procedures needed to bring truly terrible people to court. Tech Talk 3: Who Should attend: Anyone interested in talking and learning about developing Generative AI Apps. Where: This is an in-person event. Registration using this form is required to get into the event. Registration in advance will close 2 days before the event. Capacity 50. Sponsored by LUMA OPTICS. Are you interested in speaking or sponsoring a Sonoma AI with wine event? 🍷 Let us know!

🚀 Exciting Update! 🚀 I’m thrilled to share a fresh look at my work and expertise in AI and data science through my updated GitHub profile and README.md file! 🎉 🔍 Why GitHub and README.md Matter: GitHub is more than just a code repository—it’s a showcase of my journey, skills, and projects. An informative README.md file is crucial for effectively communicating the scope and impact of my work, making it easier for collaborators and potential clients to understand what I bring to the table. 👨💻 About Me: I’m a freelance Data Scientist specializing in end-to-end projects across machine learning (ML), deep learning (DL), natural language processing (NLP), and generative AI. My work spans from developing predictive models to crafting AI-driven solutions and applications, delivering impactful results through innovative techniques and frameworks. 🔧 Explore My Skills: Machine Learning & Deep Learning: TensorFlow, PyTorch, Scikit-learn NLP & Generative AI: Hugging Face, LangChain, OpenAI MLOps & Deployment: Docker, AWS, Streamlit Data Management & Visualization: SQL, PostgreSQL, Power BI 💡 Discover More: Check out my GitHub profile to explore detailed project insights and see how I leverage cutting-edge technology to solve real-world problems. Your feedback and connections are always welcome! 🔗 Explore my GitHub profile:[https://lnkd.in/ggQsEG2B] Looking forward to connecting with fellow data enthusiasts and exploring new opportunities! #DataScience #MachineLearning #DeepLearning #NLP #GenerativeAI #Freelancer #GitHub #TechInnovation #AI #MLOps #DataVisualization

🚀𝟔𝟔 𝐃𝐚𝐲𝐬 𝐨𝐟 𝐃𝐚𝐭𝐚🚀 🎓𝑾𝒆𝒆𝒌 𝟐/𝟏𝟎 : 𝑩𝒖𝒊𝒍𝒅𝒊𝒏𝒈 𝑩𝒍𝒐𝒄𝒌𝒔🎓 🧠 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬 🧠 I started with the short course on DeepLearning.AI titled 𝐋𝐚𝐧𝐠𝐂𝐡𝐚𝐢𝐧 : 𝐂𝐡𝐚𝐭 𝐰𝐢𝐭𝐡 𝐘𝐨𝐮𝐫 𝐃𝐚𝐭𝐚 to gain further knowledge about LangChain and its applications. Key Concepts learned so far: 🧩𝐌𝐨𝐝𝐮𝐥𝐚𝐫 𝐂𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭𝐬 𝐢𝐧 𝐋𝐚𝐧𝐠𝐜𝐡𝐚𝐢𝐧 Prompts: Structured texts initiating model interactions. Models: The core engines process inputs to produce outputs. Indexes: Repositories for storing and retrieving model-relevant information. Chains: Frameworks linking multiple components for complex tasks. Agents: Integrations of prompts, models, indexes, and chains into complete systems. 📂𝐃𝐨𝐜𝐮𝐦𝐞𝐧𝐭 𝐋𝐨𝐚𝐝𝐢𝐧𝐠 Involves integrating external documents into a model's workflow using loaders. These loaders handle the access and conversion of data from various sources such as websites, databases, and YouTube. They manage documents in formats like PDF, HTML, JSON, Word, and PowerPoint, where each 'Document' object contains content and related metadata. Common Loaders: PyPDFLoader: Loading PDF files. GenericLoader, OpenAIWhisperParser, YouTubeAudioLoader: Loading YouTube content. WebBaseLoader: Loading content from URLs. NotionDirectoryLoader: Loading data from Notion. 📑𝐃𝐨𝐜𝐮𝐦𝐞𝐧𝐭 𝐒𝐩𝐥𝐢𝐭𝐭𝐢𝐧𝐠 Basically, this involves breaking down documents into smaller sections for easier processing. Here is a concise overview of common methods and tools used, as discussed in the lecture: create_documents(): Converts a list of texts into separate document objects. split_documents(): Segments documents into smaller parts. Commonly Used Splitters: CharacterTextSplitter: Splits text at the character level. MarkdownHeaderTextSplitter: Segments text based on markdown headers. TokenTextSplitter: Divides text into basic tokens. SentenceTransformersTokenTextSplitter: Uses SentenceTransformers to split text into semantically aware tokens. RecursiveCharacterTextSplitter: Splits text into characters recursively. NLTKTextSplitter: Utilizes the Natural Language Toolkit for text splitting. SpacyTextSplitter: Employs Spacy for advanced linguistic text splitting. 🧬𝐕𝐞𝐜𝐭𝐨𝐫 𝐒𝐭𝐨𝐫𝐞𝐬 𝐚𝐧𝐝 𝐄𝐦𝐛𝐞𝐝𝐝𝐢𝐧𝐠𝐬 Vector Stores are specialized databases for storing vector data.  Embeddings are vector representations that encapsulate the content's meaning. 🧠𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬🧠 📊𝐌𝐞𝐚𝐧, 𝐕𝐚𝐫𝐢𝐚𝐧𝐜𝐞 𝐚𝐧𝐝 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝 𝐃𝐞𝐯𝐢𝐚𝐭𝐢𝐨𝐧 🧮𝐌𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐚𝐥 𝐌𝐨𝐝𝐞𝐥 🔢𝐒𝐚𝐦𝐩𝐥𝐢𝐧𝐠 𝐟𝐫𝐨𝐦 𝐚 𝐃𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧 Looking forward to the next week and hoping to expand upon the knowledge gained so far! I would highly appreciate any suggestions on areas where I could improve and what topics to explore next. #66daysofdata #GenerativeAI #MachineLearning #LangChain #Embeddings #DeepLearning

How can #AI help you work more effectively with #spreadsheet, #Excel or #CSV files? A few readers have asked for details based on my story for The Wall Street Journal this week, so here are some practical tips and examples. Sign up for my newsletter ☝ to get more details on my stories for the WSJ and for lots of AI how-tos. I'll put a link to the latest story in comments. Here's the scoop on how to do the kind of thing I wrote about this week. How to get spreadsheet data into an AI-friendly form: ✅ Upload CSVs to ChatGPT or Claude.ai as part of a prompt or custom GPT 📊 Paste data from Excel sheet (or uploaded a csv into Coda) ⬆️ Upload an xlsx file to Google Sheets use a GPT add-on 💻 Try an app designed for working with spreadsheet data (like Akkio or Numerous.ai, though so far I haven't found them useful enough to be worth paying for) 💵 Pro tip: Conserve tokens when asking GPT to work with a CSV by adding an index column to your input file (e.g. IN1, IN2, IN3). Then ask for it to return results (like categorization of your original data) as a 2-column table in the form Index No | Category. That way you don't use up tokens/answer power just returning your original data. Use XLOOKUP to re-unite the categories with your original dataset. (ChatGPT can tell you how to do that.) How I've used AI to work with spreadsheet data 📁 Categorize social media posts by topic (small sets via Coda.io, larger via CSV upload to GPT and then asking for results in table form). 🧹 Clean a messy export of past articles by returning a file as a de-duped table. ⚾️ Power a custom GPT that helps me draft story pitches. I uploaded a CSV of social media analytics for all my past published stories so the AI knows what performs best. 🎓 Digest the stats in an academic article by returning the table in a PDF in CSV form that's easier to read and work with. 🔎 Make my ChatGPT history searchable. I exported, got GPT's help writing a Python script to convert to CSV, uploaded to Coda.io, and used Coda's on-board AI to categorize past chats. 🐍 Learned how to use Python to clean up an Excel file! I had a file with 8 tabs of similar but differently labeled data, so I took all the separate column headings and pasted them into a single CSV. Then I pasted that into GPT, along with the list of what I wanted as my canonical column headings, and asked it to map each tabs column headings onto my canonical headings and return as a table. It got that about 90% correct, but I had to manually correct the mappings. But THEN my mappings were the only input I needed in order to write a Python script that took my many tabs and consolidate them into a single sheet with consistent headings! And I did all that as someone who basically does not know Python; it was just monkey see, monkey do, with me following GPT's instructions. Are you using AI to work with spreadsheets? I'd love to hear how.

The pace of creative disruption has accelerated across these successive waves: ▶ The first wave centered around empowering report developers to create reports without needing SQL coding skills. It took 15 years to reach maturity, with SAP BusinessObjects and IBM Cognos leading the market. ▶The second wave was driven by data analysts' need to visualize data from spreadsheets, cubes, and data warehouses. This wave matured in 10 years, with Tableau, Qlik, and Microsoft Power BI becoming dominant players. ▶The third wave, augmented analytics, introduced Natural Language Processing (NLP) and automated insights to on-premises data stores and centralized data warehouses. It matured in just 3 years, with ThoughtSpot at the forefront. Other companies quickly followed suit through acquisitions, such as Tableau acquiring ClearGraph, launching Ask Data and SAP rebranding into SAP Analytics Cloud after multiple acquisitions. ▶The fourth wave, spurred by the modern data stack, gained momentum during the pandemic as businesses accelerated digital transformation and cloud migration. Leaders in this space included Looker, Mode, and Sigma, which integrated deeply with cloud data platforms and transformation tools like dbt. However, this wave was interrupted before fully maturing due to a global war and economic downturn, now giving way to the fifth wave. ▶The fifth wave, the Gen AI era, is actively transforming every aspect of the data and analytics workflow. This wave began in early 2023 when ThoughtSpot launched the first AI-powered analytics and BI experience, ThoughtSpot Sage. Microsoft Copilot for Power BI and Google Duet AI for Looker are currently in preview, and Tableau Pulse is expected to launch in Spring 2024. #techkors #techbites

Could AI Replace Data Analysts? Over the past few weeks, I’ve been testing workflows that leverage AI to generate SQL queries from natural language prompts using BigQuery—and it works incredibly well. Tools like QueryGPT are revolutionizing how we interact with data. Instead of spending time building queries, data analysts can focus more on real analysis and deriving meaningful insights. This isn’t the end of data analysts; it’s an evolution of their role. By automating tedious tasks, we’re empowered to drive strategic decisions like never before. Exciting times ahead! #DataAnalytics #BigQuery #AI #Productivity #DataScience