Jagruti C.’s Post

🚀 Excited to share a sneak peek of my latest SQL project! 📊 Here's a glimpse of what I've been working on: 🔍 Total Vaccinations by Location: Leveraging SQL to examine the distribution of Covid-19 vaccinations across different regions in 2019. 🌍 Total Deaths by Location: Using SQL queries to investigate the total number of Covid-19 related deaths in various locations during that timeframe. ⚰️ Daily Deaths Trend: Tracking the daily number of Covid-19 deaths in 2019 with SQL to understand the progression of the pandemic over time. 💉 Vaccination Progress: Analyzing the percentage of population vaccinated and monitoring the vaccination progress during that period, and more. This project has been an incredible opportunity to sharpen my SQL skills while delving into vital insights about the pandemic's impact. 🔗 Please check out my GitHub repository for more details: https://lnkd.in/eVHTaJDa #SQL #DataAnalysis #DataExploration #Covid19 #DataScience #Insights #Analytics #DataVisualization #InternshipSearch #DataScienceInternship #SQLInternship #AnalyticsInternship #DataAnalysisInternship #JobSearch #CareerDevelopment #DataJobs #Opportunity I'm currently seeking a data analyst internship to apply and further develop my skills. If you know of any opportunities or have any leads, I'd greatly appreciate your assistance! Thank you for your support.

🚀 Excited to Share My Latest SQL Project: Covid-19 Data Exploration! 📊 During these challenging times, data has played a crucial role in understanding and combating the Covid-19 pandemic. I'm thrilled to present my latest project, where I delve into COVID-19 data, uncovering insights and trends through comprehensive SQL queries. 🔧 Skills Applied: - Joins - Common Table Expressions (CTEs) - Temporary Tables - Window Functions - Aggregate Functions - Creating Views - Data Type Conversion 📈 Highlights of the Project: 1. Total Cases vs Total Deaths: Analyzing the likelihood of dying if you contract Covid-19 in different countries. 2. Total Cases vs Population: Understanding what percentage of the population was infected. 3. Highest Infection Rates: Identifying countries with the highest infection rates compared to their population. 4. Highest Death Counts: Breaking down the highest death counts per population by countries and continents. 5. Global Statistics: Summarizing global numbers to get an overarching view of the pandemic's impact. 6. Vaccination Analysis: Using CTEs and temp tables to calculate the percentage of the population that received at least one vaccine dose. 🔍 Exploring Data by Continent: Highlighting continents with the highest death counts and infection rates to understand regional impacts. 🌐 Creating Views for Visualization: Streamlining data for later visualizations to provide clear, insightful presentations of the findings. 📊 Interactive Insights: Leveraging rolling calculations and partitioning data to track vaccination progress over time. Data: https://lnkd.in/ec9ZyaBp GitHub Link: https://lnkd.in/e9wRdgYc #SQL #DataScience #Covid19 #DataAnalysis #DataVisualization #Vaccination #LinkedInCommunity Day 6/10

📊 Unveiling Insights from COVID-19 Data Analysis using R! 🚀 Hey LinkedIn Community! 👋 I'm excited to share a fascinating project where I delved into COVID-19 data using R programming language to validate two common claims surrounding the pandemic: the age disparity between survivors and deceased individuals, and the impact of gender on mortality rates. 🦠💻 Here's a brief overview of my findings: 1️⃣ Age Disparity Analysis: Through rigorous statistical analysis, I examined whether there's a significant age difference between individuals who survived COVID-19 and those who unfortunately succumbed to the virus. By comparing the age distributions of survivors and deceased individuals, I aimed to shed light on this claim. 2️⃣ Gender-Based Mortality Analysis: Another claim under scrutiny was the notion that gender has no effect on COVID-19 mortality rates. Leveraging the power of R, I explored the relationship between gender and mortality, seeking to validate or refute this hypothesis through data-driven insights. Here are some key takeaways from the analysis: 🔍 Age Disparity: Contrary to the claim, the analysis revealed a notable trend: individuals who succumbed to COVID-19 tended to be older on average compared to survivors. This insight underscores the disproportionate impact of the virus on older populations, highlighting the importance of targeted interventions and protective measures for vulnerable age groups. 🚹🚺 Gender-Based Mortality: Interestingly, the data analysis indicated that gender does play a role in COVID-19 mortality rates. While both males and females are susceptible to the virus, there was a discernible difference in mortality rates between genders. Further exploration into the underlying factors driving this disparity could provide valuable insights for public health interventions and policies. By leveraging data analytics and statistical methods, we can gain deeper insights into the dynamics of the pandemic and inform evidence-based decision-making. I'm passionate about leveraging technology and data-driven approaches to address pressing challenges like COVID-19, and I'm eager to continue exploring innovative solutions in this space. Check out the code and details of this project on my GitHub: https://lnkd.in/diNZCUu5 Let's keep the conversation going! What other claims or hypotheses would you like to see explored using COVID-19 data? Share your thoughts and let's collaborate to uncover actionable insights! 💡💬 #DataAnalytics #COVID19 #RProgramming #StatisticalAnalysis #PublicHealth #TechInnovation

I am excited to share insights from my recent Global Population Growth Analysis project. This in-depth analysis leverages the comprehensive international dataset provided by the United States Census Bureau, which spans estimated country populations from 1950 with projections through 2050. This dataset, accessible via Google Cloud Platform's BigQuery, includes midyear population figures by age and gender assignment at birth, alongside time-series data on fertility, birth, death, and migration rates. This comprehensive project was an incredible journey through data exploration, analysis, and visualization, showcasing the power of integrating tools like Google Cloud Platform, BigQuery, Python, and PowerBI. Key Insights: Utilizing BigQuery, I extracted public data to analyze global demographic trends. Through Python, data cleaning and exploratory data analysis (EDA), uncovering patterns. PowerBI enabled me to create dynamic visualizations. One of the most striking findings was the significant shift in fertility rates between 20-24 and 30-34, highlighting the changing dynamics of global population growth. #DataAnalytics #BigQuery #Python #PowerBI #GlobalDemographics #PopulationGrowth

🚀 Excited to share my latest SQL project on analysing Covid-19 cases and vaccination data using SSMS! In this project, I have: Aggregated and cleaned data. Executed complex queries to extract meaningful insights. This project not only honed my SQL skills but also provided valuable insights into the pandemic's trajectory and the effectiveness of vaccination campaigns. Check out the full project on GitHub: https://lnkd.in/gjBmZDeX #SQL #DataAnalysis #Covid19 #Vaccination #DataScience #Analytics #GitHubProjects

Greetings, Connections !! Excited to share that I've completed Task 1 of #ProdigyInfoTech 🎉 Task 1:- Create a bar chart or histogram to visualize the distribution of a categorical or continuous variable, such as the distribution of ages or genders in a population📈✨ https://lnkd.in/dkYbx6vf #ProdigyInfoTech #datascienceintern #datavisualization

🌟 **Project Showcase: Instagram User Analytics** 🌟 I'm excited to share my latest project where I analyzed user engagement and behavior on Instagram using SQL! 📊 ** Project Overview: ** This project aims to analyze user interactions and engagement with the Instagram app using SQL queries. The goal is to provide valuable insights to the product team, aiding in decision-making processes such as rewarding loyal users, encouraging inactive users to post, determining contest winners, researching popular hashtags, and optimizing ad campaign schedules. The project also involves tasks related to marketing analysis and investor metrics. ** Key Objectives: ** 1. Analyze User Engagement: Measure likes, comments, and shares; identify active users. 2. Encourage Activity: Re-engage inactive users; promote consistent posting. 3. Reward Loyalty: Recognize and reward long-term users. 4. Determine Contest Winners: Identify winners by highest likes and engagement. 5. Optimize Ad Campaigns: Find optimal times for ad launches. 6. Conduct Hashtag Research: Identify effective hashtags for engagement. Measure Engagement: Calculate average posts per user and overall activity. Identify Bots: Detect and mitigate bot accounts for platform integrity. SQL Queries 👇 https://lnkd.in/d_CmhJg4 #DataAnalytics #SQL #SocialMedia #InstagramAnalytics #DataScience #DataAnalyst

📊 Exciting Project Update: Income Prediction Dataset I'm thrilled to share my latest project – an income prediction dataset! 🚀 This dataset contains valuable insights into various demographic and work-related features, making it ideal for machine learning model training and exploratory data analysis. 🌐 Key Features: Income Levels: Categorized into <=50K and >50K. Demographic Details: Age, Gender, Education. Occupation and Work Details: Occupation, Workclass. 🤖 Machine Learning Enthusiasts: Explore the dataset, use it for predictive modeling, and uncover patterns that can contribute to impactful insights! The dataset is available on my GitHub: ["https://lnkd.in/gabJjViR"] 🚀 Contribute & Engage: If you have insights to share or ideas for improvement, feel free to contribute! Fork the repository and create a pull request. 🔗 Dataset Link: https://lnkd.in/gabJjViR #DataScience #MachineLearning #Dataset #IncomePrediction #GitHub

Hai everyone!🌟 Excited to share my latest project on a Business Intelligence during the 5th Semester of Statistics -a Click-Through Rate (CTR) Analysis Shiny App built using R Shiny! 🚀 This interactive dashboard allows users to upload datasets or manually input data, perform statistical analysis using a t-test, and visualize results through tables and plots. It's a powerful tool for marketers, providing insights into the effectiveness of different ad placement strategies based on CTR. Check out the application, explore data with dynamic DataTables, and understand the statistical significance of results. https://lnkd.in/gJd3y9B5 You can access the repository that contains the code for a Click-Through Rate (CTR) Analysis Shiny App in here👇🏻 https://lnkd.in/gbzTynxZ Feel free to play around, and let me know your thoughts or suggestions. 📊💡 #RShiny #DataAnalysis #MarketingAnalytics #DataScience #InteractiveDashboard