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Converting String Variables into Numeric Ones You can convert string variables in variable view but a much better option is using a single line of syntax such as ALTER TYPE V01 TO V10 (F8.2). This very simple command converts all variables in one go. Now, whatever method you choose, things get tricky if -values have a period or comma as decimal separator, -empty cells or even -illegal characters in some values. Sadly, none of the other videos deal with these common issues. So in this SPSS beginners video, I'll show how to do this the right way: https://lnkd.in/gxet4wf8 Hope it helps!

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If you are looking for a way to conduct linear regression analysis in Python, the statistics way using statsmodels, and not the machine learning way using scikit-learn, then this article is for you. If you are also new to the concept of regression analysis, this article will give you a brief overview. https://lnkd.in/dnD_N_gc

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New AWIPS Tips: Plotting NEXRAD Data in Python Check out the blog here: https://buff.ly/3WVdyRD

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Statistical NLP Frequency Distributions Analyzes the frequency of terms in a text corpus to identify common words and understand text characteristics. Tools like NLTK's FreqDist class are commonly used. N-grams and Collocations N-grams: Consecutive sequences of n items (words) in a text, e.g., bigrams (2-grams), trigrams (3-grams). Useful for predictive text models and phrase detection. Collocations: Words that frequently occur together, e.g., "strong tea". Statistical measures like PMI (Pointwise Mutual Information) or raw frequency counts are used. Term Frequency-Inverse Document Frequency (TF-IDF) A statistical measure used to evaluate the importance of a word in a document relative to a corpus. Formula: TF: (Number of times term t appears in a document) / (Total number of terms in the document). IDF: log(Total number of documents / Number of documents with term t in it). TF-IDF: TF * IDF. Applications in text mining and information retrieval. Pointwise Mutual Information (PMI) Measures the association between two terms by comparing their joint probability to the product of their individual probabilities. Formula: PMI(x, y) = log(P(x, y) / (P(x) * P(y))). Useful for identifying collocations and semantic similarity. #PMI #Collocations #TextAnalysis #FrequencyDistribution #TextAnalysis #DataScience #Ngrams #Collocations #TextMining #TFIDF #TextMining #MachineLearning #NLP #EncephAI

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Journals with high rates of suspicious papers is an issue, but the attached graph looks to me awkward - What am I missing ? Link to the article in the first comment #research

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🧭 Finding Patterns and Gaps in Health Data through Exploratory Data Analysis (EDA) 🧭 Exploratory Data Analysis (EDA) is a powerful first step in spatial health research, allowing for nuanced data exploration and pattern recognition. Using GeoDa and R, I dive deep into demographic and health datasets to identify correlations between Social Determinants of Health (SDOH) and health outcomes. 🔍EDA Process Highlights: • Generated scatter plots, histograms, and box plots to visually interpret relationships between variables. • Mapped out data points using thematic mapping to locate high-risk and underserved areas. • Applied Moran's I and LISA (Local Indicators of Spatial Association) to assess spatial autocorrelation, helping to pinpoint clusters and hotspots of health disparities. 💡Takeaway: EDA reveals essential patterns and insights, setting a solid groundwork for targeted interventions and evidence-based policies. By identifying underserved regions and at-risk populations, we can design more effective health solutions. #DataScience #HealthGeography #SpatialAnalysis #GeoDa #EDA #PublicHealth #HealthDisparities #RProgramming

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🚀 5 Newer Python Data Science Tools You Should Be Using 🚀 Already using NumPy, Pandas, and Scikit-learn? Here are five more powerful tools to add to your Python data science toolkit: ConnectorX: Speed up data loading from databases like PostgreSQL, MySQL, SQLite, and more. Supports Pandas, PyArrow, Modin, Dask, and Polars. DuckDB: Think SQLite for OLAP. Lightweight, columnar storage, and optimized for long-running queries. Ingests CSV, JSON, and Parquet directly. Optimus: All-in-one tool for loading, exploring, cleansing, and writing data. Compatible with Pandas, Dask, CUDF, Vaex, and Spark. Polars: A faster DataFrame library. Uses Rust for performance. Supports eager and lazy execution, parallel processing, and more. Snakemake: Automate data science workflows. Define steps with rules, multi-threaded execution, and cloud deployment. Upgrade your data science game with these hidden gems! 💎 𝗢𝘂𝗿 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝘀: 𝗦𝘁𝗮𝗳𝗳𝗶𝗻𝗴: We offer contract, contract to hire, direct hire, remote global hiring, SOW projects and managed services. https://lnkd.in/g6bddCHa 𝗥𝗲𝗺𝗼𝘁𝗲 𝗛𝗶𝗿𝗶𝗻𝗴: We offer U.S. companies the opportunity to hire IT professionals from our India-based talent network. https://lnkd.in/gN2A4c-Y 𝗖𝘂𝘀𝘁𝗼𝗺 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁: We offer Web/Mobile Development, UI/UX Design, QA & Automation, API Integration, DevOps services and Product Development. https://lnkd.in/dcKsvxAu 𝗢𝘂𝗿 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝘀: 𝗭𝗲𝗻𝗕𝗮𝘀𝗸𝗲𝘁 :An E-commerce platform to sell your products online to a large user base with custom features. https://getzenbasket.com/ https://zenbasket.in/ 𝗭𝗲𝗻𝘆𝗼 𝗣𝗮𝘆𝗿𝗼𝗹𝗹: An automated payroll application that helps companies in India process their employees' payroll. https://lnkd.in/d8hyNKpM #DataScience #Python #BigData #MachineLearning #AI #TechInnovation #DataAnalytics #Programming #DevelopmentTools #Technology #Centizen #ZenBasket

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🚀 **Your Step-by-Step Guide to Becoming a Data Scientist!** If you're a *B.Tech CSE student* dreaming of a **Data Science** career, here’s a roadmap to help you get there! Data Scientists are in high demand, and with the right skills, you can shape your future in this exciting field. ### 1️⃣ **Master Programming Languages** Focus on **Python** or **R**, and learn data manipulation with **Pandas** and **Numpy**. Don’t forget visualization libraries like **Matplotlib** and **Seaborn**. ### 2️⃣ **Solidify Your Math and Statistics Skills** Master **statistics**, **probability**, and **linear algebra**—they’re key for building models and analyzing data. ### 3️⃣ **Learn Data Wrangling & Data Visualization** Hone your skills in **cleaning** and organizing data, and visualizing it with **Tableau** or **Power BI** to uncover insights. ### 4️⃣ **Dive into Machine Learning** Start with **supervised** and **unsupervised learning**, then move on to **deep learning** using frameworks like **TensorFlow** or **PyTorch**. ### 5️⃣ **Hands-On Practice with Real Projects** Work on real-world projects in **predictive modeling**, **NLP**, and **Computer Vision** to build your portfolio. ### 6️⃣ **Build Your SQL and Big Data Skills** Learn **SQL** for database interaction, and explore **Big Data** tools like **Hadoop** and **Spark** for large-scale data processing. ### 7️⃣ **Explore Data Engineering Basics** Understand **ETL pipelines** and data storage systems like **HDFS** and **NoSQL**. ### 8️⃣ **Stay Updated with Advanced Techniques** Explore **Reinforcement Learning**, **AI ethics**, and **time-series forecasting** to stay on the cutting edge. ### 9️⃣ **Network and Engage with the Data Science Community** Join online communities, participate in **hackathons**, and network with industry professionals on **LinkedIn**. ### 🔟 **Get Industry-Ready with Internships and Certifications** Look for **internships** and earn **certifications** from platforms like **Coursera** or **Udacity** to boost your skills and resume. #DataScienceJourney #ShardaInformatics #DataWrangling #MachineLearning #BTechCSE #CareerInDataScience #ShardaUniversity #Python #AI

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Implementing Anthropics Contextual Retrieval for Powerful RAG Performance This article will show you how to implement the contextual retrieval idea proposed by AnthropicContinue reading on Towards Data Science »... https://lnkd.in/e-kkhwYv #AI #ML #Automation

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Hi everyone! 👋 Today, let's dive into something exciting—Data Analysis with Python! 🐍💻 Python has a rich ecosystem of libraries that make data analysis efficient and fun. Let's break it down: 🔄 Data Manipulation: Handling large datasets is effortless with tools like Datatable, Vaex, and Modin, which allow for fast and scalable data manipulation. 📊 Statistical Analysis: Perform advanced statistical modeling using PyMC3, PyStan, and Lifelines, ideal for probabilistic reasoning and survival analysis. 🤖 Machine Learning: Build machine learning models with powerful libraries like Scikit-learn, Keras, and TensorFlow, which are essential for creating predictive algorithms. 🗣️ Natural Language Processing (NLP): Dive into text analysis with tools like Gensim, Polyglot, and SpaCy, perfect for language modeling, text summarization, and more. 🗄️ Database Handling: Efficiently manage and analyze large databases with Koalas, Dask, and PySpark, bridging the gap between dataframes and distributed computing. 📈 Data Visualization: Bring your data to life using visualization libraries such as Altair, Bokeh, and Pygal, to create interactive and insightful charts and graphs. #DataAnalysis #PythonDataAnalysis #DataManipulation #MachineLearning #NLP #DataScience #PythonLibraries #DataVisualization #StatisticalAnalysis #BigData #PythonForData #AI #DeepLearning #DataModeling #PyMC3 #ScikitLearn #Keras #TensorFlow #Vaex #Modin #Gensim #Polyglot #SpaCy #PyStan #Koalas #Dask #PySpark #Altair #Bokeh #Pygal #PythonProgramming #DataHandling #PredictiveAnalytics #TextAnalysis #AIWithPython #MLLibraries #PythonTools #DataAnalytics #DataProcessing #PythonDataScience #PythonForML #DataEngineering #InteractiveVisuals #DataVisualizationTools #BusinessIntelligence #DataDriven #DataInsights #TechInData #DataScienceTools #DistributedComputing #USITRecruitment #ITStaffing #TechRecruitment #USStaffing #RecruitmentLife #StaffingSolutions #ITHiring #TalentAcquisition #RecruitmentConsultant #JobPlacement #TechJobs #ITConsultants #USITJobs #HiringNow #RecruitmentAgency #StaffingAgency #TechCareers #JobOpenings #CareerOpportunities #RecruitmentStrategies #ITRecruiters #USITStaffing #TalentSourcing #RecruitmentGoals #JobSearchUSA #ITJobMarket #RecruitmentDrive #StaffingIndustry #TalentAcquisitionExpert #TechnicalRecruitment #HiringExperts #USITMarket #TechStaffing #CareerDevelopment #ITStaffingSolutions #JobSeekersUSA #TechRecruiters #RecruitmentSpecialists #ITStaffingAgency #JobVacancies #TechTalent #StaffingServices #WorkInTech #RecruitmentJourney #StaffingExperts #TechRecruitmentUSA #ConsultantPlacement #ITStaffingIndustry #TalentSolutions #JobOpportunities Ravindranath Konda Saini Vinod Shiva Kumar ✪ Nagarjuna G Sairaj Bandari Riya Khan Leenath Mandava Sreenath Kota Varsha Katkam Sri Ranga

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Want to learn more about REDCap options to support your research study teams? Check out OCTRI's #REDCap wiki for how-to guides, helpful tips, use cases, and more. https://bit.ly/3T7tsFL #CTSAProgram

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