Analytics Insight®’s Post

7 steps in the data science lifecycle concisely: 1. *Define the Problem:* Identify business objectives and goals. 2. *Data Collection:* Gather relevant data from various sources. 3. *Data Preparation:* Clean and preprocess the data. 4. *Exploratory Data Analysis (EDA):* Analyze data patterns and insights. 5. *Modeling:* Select and train machine learning models. 6. *Evaluation:* Assess model performance using metrics. 7. *Deployment and Monitoring:* Deploy the model and monitor its performance in production. #datascience #ML #AI

🔍 Mastering the Data Journey: From Cleaning to Reduction in Machine Learning 📊 In the realm of machine learning, the journey from raw data to refined insights involves several crucial steps: data cleaning, integration, transformation, and reduction. Each of these stages plays a pivotal role in ensuring the success of your machine learning projects. 🔑 Key Steps in Data Preparation: 1. Data Cleaning 🧹 ● Handling Missing Values: Use techniques like mean/median imputation or advanced methods such as K-Nearest Neighbors (KNN) to fill in gaps. ● Removing Noise: Filter out irrelevant data points to improve the quality of your dataset. ● Correcting Inconsistencies: Ensure uniformity in data entry and format. 2. Data Integration 🔗 ● Combining Data from Multiple Sources: Merge datasets from various sources to create a unified view. ● Handling Redundancies and Conflicts: Resolve data overlaps and discrepancies to maintain consistency. 3. Data Transformation 🔄 ● Normalization and Scaling: Apply Min-Max Scaling or Standardization to bring all features to a similar range. ● Encoding Categorical Data: Convert categorical variables into numerical values using One-Hot Encoding or Label Encoding. ● Feature Engineering: Create new features from existing ones to capture more information and improve model performance. 4. Data Reduction 📉 ● Dimensionality Reduction: Use techniques like Principal Component Analysis (PCA) or t-SNE to reduce the number of features while preserving essential information. ● Feature Selection: Identify and retain the most relevant features to simplify the model and enhance performance. 📄 Why These Steps Matter ❓: 🔹 Enhanced Model Performance: Clean, integrated, transformed, and reduced data lead to more accurate and efficient models. 🔹 Better Insights: High-quality data allows for more meaningful and actionable insights. 🔹 Resource Efficiency: Reducing data complexity helps in saving computational resources and time. Remember, the foundation of any successful machine learning project lies in meticulous data preparation. Invest time in these steps to unlock the true potential of your data! What's your go-to technique in data preparation? Share your thoughts and experiences below! 🚀 #DataScience #MachineLearning #DataCleaning #DataIntegration #DataTransformation #DataReduction #BigData #AI

In today’s data-driven world, SQL and Prompt Engineering can go hand in hand to unlock new possibilities in data analysis and insights. SQL is the backbone of structured data queries, helping extract, transform, and load data (ETL) for analysis. On the other hand, prompt engineering involves crafting precise instructions for AI models like GPT to generate accurate and meaningful results. The real magic happens when these two are combined. For example, let’s say you’re analyzing customer behavior data. With SQL, you can write complex queries to retrieve specific data points like purchase frequency, product preferences, or geographical distribution. Then, using prompt engineering, you can create a prompt for AI to identify trends, generate insights, or even suggest personalized marketing strategies based on that data. By mastering both, data professionals can streamline data-driven storytelling. SQL retrieves the facts, and AI transforms them into narratives or actionable insights, saving time and offering a deeper understanding. In the evolving landscape of AI and data, combining SQL with prompt engineering can significantly elevate your analytics game. #DataAnalysis #AI #SQL #PromptEngineering #BusinessIntelligence #AIInData #DigitalTransformation

Hi connections, I want to discuss with you this topic.. 🚀Data Preparation and Cleaning Data preparation and cleaning are crucial steps in data analysis and machine learning. They ensure the quality and reliability of your data before you begin modeling or analysis. 🪜#Steps in Data Preparation and Cleaning: 1.🧲 Data Collection: Gather raw data from various sources. 2. 🧰Data Integration: Combine data from multiple sources into a unified dataset. 3. 🧹Data Cleaning: Identify and correct errors, remove duplicates, and handle missing values. 4. 💹Data Transformation: Normalize and scale data, create new features, and convert data types if necessary. 5. 📊Data Reduction: Reduce the volume of data by eliminating redundant or less relevant information. 6. 🔒Data Validation: Ensure the data is accurate and consistent after cleaning. #data_analysis #powerbi #data_science #ai

Fostering data quality is paramount across the entire analytics spectrum , from the traditional BI analytics to analytics fueled by Machine Learning models , be it Generative or Predictive Models. However, more than 2 years into the data science consulting services industry I have come to realize the palpable disproportionality between the data quality requirements and their complexity for Business Intelligence solutions and those of AI/ML powered solutions or use cases.   This disproportionality is owed to the fact that training AI models that can yield accurate predictions or recommend suitable actions is a challenging endeavor . Since, the predictive accuracy and validity of the recommendations provided by the models is going to be as good as the quality of the data ingested by them. So, below are the key data quality checks (by the order of importance) that I consider to be of non-negligible significance for AI/ML :   1) Context - all the data collection and transformation efforts shall be tailored to the context of the ML use case or solution whether it’s for a particular problem area of a business unit or a general solution for the enterprise. 2) Completeness - Once the business problem context is defined, the attributes of the actual collected data shall commensurate with the context requirements , example - we might have data for variables X1,X2,X3,X4 but there could be some latent variables relative to the dataset XL1 and XL2 which if present would not only have improved the accuracy of the model but also lend itself to be more explainable. 3) Sparsity - This check is a determinant of the data cleaning and preprocessing efforts, for example if significant transaction details and history for particular product are missing or contain zeroes, it can adversely affect the model accuracy , therefore these data points would have to be dropped from the training hull. 4) Validity - This emphasizes that the values in each metric(column) of the dataset shall conform to the defined business rules or logic for updating them. Its very critical for prescriptive analytics use cases where every recommendation has to be justified. 5) Precision - The degree to which a particular numerical variable is or supposed to be rounded off (decimal places) or aggregated based on some business rules, this will help in selection of the correct feature scaling technique for ensuring consistency in the range of each numerical variable. #dataquality #datamanagement #machinelearning #generativeAI

5 Steps to Data Pipeline! 1️⃣ Data Ingestion: We start by collecting data from various sources like databases, apps, or social media. 2️⃣ Data Processing: Next, we clean and transform the data to make it useful. Think of it as tidying up and organizing before the real work begins. 3️⃣ Data Storage: Now, the processed data finds a home in a storage solution like a data lake or warehouse. 4️⃣ Data Analysis: Time to dig deep! Analysts use this structured data to find insights and patterns that drive decision-making. 5️⃣ Data Visualization: Finally, these insights are presented in easy-to-understand charts and graphs, making it simple for everyone to understand. #data #webdeveloper #ai #ml #softwaredeveloper #softeareengeener #linkedin

𝗥𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗼𝗳 𝗱𝗮𝘁𝗮 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 | 𝗡𝗲𝘅𝘁-𝗟𝗲𝘃𝗲𝗹 𝘃𝘀 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗱𝗮𝘁𝗮 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 I was shocked when I read that (considering the importance of real time data and up-to-date technologies), previously analysts used to work on the existing historical data for analysis. Tech world has changed enormously after #ai that only descriptive analysis is of no use for businesses and organizations. Here comes the #nextlevel data analysis! 𝙃𝙚𝙧𝙚 𝙖𝙧𝙚 𝙩𝙝𝙚 𝙛𝙤𝙡𝙡𝙤𝙬𝙞𝙣𝙜 𝙠𝙚𝙮 𝙝𝙞𝙜𝙝𝙡𝙞𝙜𝙝𝙩𝙨: 🔍 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀: 📈 Descriptive Insights 🔄 Monthly/Quarterly Analysis and Static data 📊 Past Product Performance, Profitability, Inventory ❌ Is it enough for your business/company's evolving needs? 🔍 𝗡𝗲𝘅𝘁-𝗟𝗲𝘃𝗲𝗹 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀: 🚀 Unstructured & Big Data ⏰ Real-time Insights 🔮 Predictive & Prescriptive Analysis 🤖 AI & Machine Learning ☁️ Cloud-Powered Scalability 🌟 𝗪𝗵𝘆 𝗡𝗲𝘅𝘁-𝗚𝗲𝗻 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀? 💡 React to Current Conditions 🚀 Reliable Predictions 🌍 Informed Decision-Making 📈 𝗧𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀: 👩💼 Lead Transformative Change 📈 Real-Time Decision Support 🚀 Be at the Forefront of the Revolution! #dataanalytics #datascience #ai #datarevolution

🚀 Unlock the Power of Clean Data! 🧹📊 Data preprocessing is the foundation of any data science project. Before diving into analysis or machine learning, ensure your data is clean, structured, and ready to roll! 🔑 Key Steps in Data Preprocessing: 1️⃣ Collect data from multiple sources. 2️⃣ Clean missing or incorrect values. 3️⃣ Integrate datasets seamlessly. 4️⃣ Transform data (normalize, encode). 5️⃣ Engineer new features to boost models. 6️⃣ Select the right features & reduce noise. 7️⃣ Handle class imbalances with care. 8️⃣ Split data for training, validation, and testing. 9️⃣ Scale data for consistency across features. 💻 Pro Tip: Good preprocessing = Great Model Performance! #DataScience #MachineLearning #DataEngineering #AI #DataPreprocessing #BigData #DataCleaning #FeatureEngineering

Why would you want #GenAI to model data? 🤔 Not because AI is better at #data modeling than a professional. It's not about human vs machine. But GenAI combines two skills to produce a model that crunches the time it takes to gather initial requirements and produce the first draft of a model. 🧠 + 🤖 Which skills? #GenAI works just as well for a doctor or an engineer as it does for the head of logistics or the VP of sales. A domain expert can "talk" to the chatbot. It "understands" a variety of domains and the common language and logic used in most domains. The expert can maintain the quality of the process, challenging the AI on possible hallucinations. When combined with Ellie's data modeling expertise, our AI now has the skill to create a data model that a domain expert understands. This is a model that can then be taken to a data modeler or a data architect. A model that can be improved by data professionals with the foundation in real-world business requirements. ✨ Watch the video to see Ellie's AI-assisted modeling in action. Or you can try it right now. Get your free trial: https://lnkd.in/dy-5bCgF #datamodeling #datacollaboration #dataarchitecture #dataproducts

Artificial Intelligence and Machine Learning in Data Analytics #machinelearning #ml #datascience