Codebasics’ Post

🚀 After countless hours of meticulous data crunching, some early mornings!!! I proudly present my latest dashboard❤️ Virat Kohli's runs from the past 6 years across all formats. 📊 Analyzing Virat's rollercoaster journey, especially his lean patch from 2020-2022 where the form seemed to have taken an extended vacation, and his epic comeback in 2023, this dashboard captures it all. Hard work, resilience, and a sprinkle of sarcasm—because if data, life, and cricket were predictable, they’d be boring. Dive in and see how numbers tell the story of a legend! 💪🏏 I love you Virat❤️ You are an inspiration to millions👑 #DataScience #Cricket #ViratKohli #Dashboard #HardWork #Resilience #Comeback #Analytics #CricketData #LifeInNumbers #data #datavisualization

Hey data fam how are you all I hope everyone is doing well Nowadays getting motivation is easy just open a youtube video and be motivated or read something on social media like we easily get motivated by paralympics althetics What's the point of that short motivation if it's not implemented so understand this powerful msg from Dhaval Patel sir in that session which is my favourite part of the session and every aspiring Data analyst should apply in their journey Link is attached in the comments section 👇 Thankyou so much Keep learning Keep growing #data #learn #msg

Excited to have completed a comprehensive workshop on Cricket Analytics as part of my journey in pursuing a B.Tech in Data Science! 🏏📊 Grateful for the opportunity to dive deep into the world of sports analytics, leveraging data to gain insights into cricket strategies, player performance, and team dynamics. 📈💻 Let's keep pushing the boundaries of what data can reveal in the exciting realm of cricket! 🌐🏏 #DataScience #CricketAnalytics #SportsData #DataDrivenDecisions

Excited to share that I've completed the Cricket Analytics Course! 📊🏆. It was a fascinating deep dive into the world of cricket data analysis, learning how to extract insights from data to enhance performance and strategy. Looking forward to applying these analytics skills to gain a competitive edge in cricket analysis. Thank you, Mad About Sports, for such an enriching learning experience!!! #Cricket #Analytics #DataDriven

🏏 Exciting Announcement! 📊 Introducing My Latest Project: Cricket Match Dataset Analysis! 🏏 Hey everyone, I'm thrilled to share a project that's been a labor of love for me: analyzing cricket match datasets! 📈 In this project, I've compiled and meticulously analyzed a vast array of cricket match data to uncover fascinating insights about player performances, match outcomes, and team strategies. From the highs of thrilling victories to the intricacies of strategic play, this dataset offers a treasure trove of insights waiting to be discovered! Here's a glimpse into what you can expect from my analysis: 🌟 Player Performance Metrics: Dive deep into the statistics to understand what separates the top performers from the rest. 🏆 Match Outcome Predictions: Explore the factors that influence match outcomes and discover patterns that could tip the scales in favor of victory. 📊 Team Strategies Unveiled: Peel back the layers of strategy and tactics employed by teams to gain a competitive edge on the field. Whether you're a cricket enthusiast, a data aficionado, or simply curious about the intersection of sports and analytics, there's something here for everyone! Stay tuned for more updates as I share insights and findings from my analysis. And if you're interested in delving deeper into the world of cricket data analysis or collaborating on similar projects, I'd love to connect and chat further! Let's unravel the mysteries of cricket through the lens of data together! 🤓🏏 #CricketAnalysis #DataAnalytics #SportsData #CricketStats #LinkedInAnnouncement #dataanalysis #datavisualization

📊 IPL Data Dashboard (2008-2020) 🚀 I’m excited to share my latest project – a comprehensive dashboard analyzing IPL data from 2008 to 2020! 📅🏏 🔍 Key Highlights: Trends & Patterns: Explore how team performances and player statistics have evolved over the years. Top Performers: Discover which players and teams have consistently excelled throughout the seasons. Insights: Gain a deeper understanding of match outcomes, player contributions, and more. Can be able to select the player name in the slicer with respect to the batsman or Bowler and season you would like to see for. This dashboard provides a detailed overview and visual representation of the IPL’s rich history. ->Home page is having the Navigator to redirect to the destinations. ->Second page is having the information with respect to the season and you can refer to the statistics for the both Batsman and Bowler for the entire season. ->Third page gives you the information about the batsman individual statistics total runs scored against a team and strike-rate with respect to the overs and boundaries scored at last having the information related to the dismissal kind and to who he got dismissed most. ->Fourth page is having the information about the Bowler individual statistics total wickets taken against a team and season. statistics with respect to the economy rate on the overs basis and In which mode most of the wickets got dismissed and who got dismissed most of the times. ->Final page gives you the explanation about who is the Orange, Purple cap winner with respect to the Season Winner. Ideally it will show the data with respect to the individual records over all the seasons. Check it out and let me know what you think! 💬 What do you find most intriguing about the IPL data? Share your thoughts and insights below! The visual which is not showcasing is a Smart narrative visual can avoid that. Find below link to view the report: https://lnkd.in/eSyghvEs #IPL #DataAnalysis #Dashboard #DataVisualization #SportsAnalytics #Cricket #BigData #Virat #Kohli #Rohit #Sharama 😍 #Happylearning

🚀 Excited to share my project: an in-depth IPL Analysis Dashboard! 🏏📊 I've poured my passion for cricket and data analytics into this project, and I'm thrilled with the results. This dashboard offers a comprehensive look at the Indian Premier League (IPL) from 2008 to 2018, featuring: - **Wins by Teams Based on Fielding or Batting First**: Visual breakdown of team performance. - **Toss Decision Based on Winning Percentage**: Insights into how the toss influences game outcomes. - **Top 10 IPL Venues**: Analysis of match wins based on batting or fielding first. - **Title Winners**: A clear view of IPL title holders over the years. - **Top 10 MOM (Man of the Match) Award Winners**: Highlighting standout performers in IPL history. Working on this project has allowed me to hone my skills in data visualization and Excel. I'm proud to present this as my first step into the world of data analytics. I’d love to hear your thoughts and feedback! #DataAnalytics #IPLAnalysis #Cricket #Excel#DataVisualization #SportsAnalytics

********* Data Granularity : Understanding How Detail Levels Impact Your Analysis ********* Data granularity refers to the level of detail present in your data. It essentially defines how closely you're zooming in on the information you're analyzing. Here's a breakdown: High Granularity means you're looking at your data with a magnifying glass with most detailed information possible. Low Granularity means taking a step back and looking at the bigger picture.This is useful for understanding overall patterns and high-level trends. Let's understand how data granularity helps in performance analysis in a cricket match- 🧑💻 High Granularity -> 🥎 Ball-by-ball details: type of ball bowled (fastball, spin), where it landed (yorker, full toss). 🏃🏻♂️➡️ Fielder positioning: exact positions of fielders for each ball. 🏏 Batsman shot selection: Cover drive, pull shot. This level of detail can help in below analysis: ✅ Identify a bowler's strengths and weaknesses (e.g., tendency to bowl too many full tosses). ✅ Analyze a batsman's technique against specific types of deliveries. ✅ Develop targeted fielding strategies to exploit batsman weaknesses. 🧑💻 Low Granularity: 🔢 Overall runs scored and wickets lost by each team. 🎳 Bowling figures: Overs bowled, runs conceded, and wickets taken by each bowler. 🏏 Batting scores: Runs scored and balls faced by each batsman. This information is helpfulfor: ✅ Comparing team performance and identifying areas for improvement (batting collapse etc) ✅ Tracking individual player performance over time (batting average, bowler's strike rate etc). ✅ Making strategic decisions during the game (e.g.,bowling change based on the batsman). The key is to strike a balance. There's no one-size-fits-all approach. The optimal data granularity depends on the specific question you're trying to answer. By mastering data granularity, you can unlock deeper insights from your data and make informed decisions. Manish Kumar Singh #dataanalysis #datagranularity #dataanalytics #datainsights #businessintelligence #virat #data #dataanalytics #sortby #orderby #sql

🏏 𝐓𝐢𝐦𝐞 𝐒𝐞𝐫𝐢𝐞𝐬 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐮𝐬𝐢𝐧𝐠 𝐒𝐧𝐨𝐰𝐟𝐥𝐚𝐤𝐞 𝐀𝐒𝐎𝐅 𝐉𝐎𝐈𝐍 𝐨𝐧 𝐂𝐫𝐢𝐜𝐤𝐞𝐭 𝐃𝐚𝐭𝐚 Missing values is one of the big challenges of working with Time Series data. This makes it hard to correlate data, and you need to write complex queries to process such data. Snowflake ASOF JOIN simplifies this task. In this example, I have taken data from ESPN Cricinfo for the Indian Premier League India to analyze who has been the top scorer in the league over time. If I had used simple GROUP BY on this data, then Suresh Raina would have been excluded, then the second-highest run-getter in the league but didn't play in the 2020 season because of COVID. Once the data was analyzed, I used Tableau to create this bar chart race. It looks like Virat Kohli will lead the charts for years to come. If you want to learn more about ASOF JOINS and how to use it in your context, please read this useful blog by Yogitha Chilukuri. https://lnkd.in/eBiy9PQq #TimeSeries #Cricket #IPL #BarChartRace

📊 Analyzing Soccer Match Predictions: Bridging Data and Reality 📈⚽ As a Data Business Analyst, I'm constantly fascinated by how data-driven insights can illuminate complex scenarios, even in the world of sports. Yesterday's match between Estrela and Sporting Lisbon provides a compelling example of this intersection between data analysis and real-world outcomes. According to my football model, which incorporates various statistical factors, the home team (Estrela) was given a 10% chance of winning, with an expected goal tally of 1.07. Conversely, Sporting Lisbon, the away team, was heavily favored with a 75% chance of winning and an expected goal count of 2.94. The final scoreline of 1-2 in favor of Sporting Lisbon closely mirrored the model's prediction. This alignment between the predicted outcome and the actual result underscores the power of data analytics in forecasting sporting events. However, it's crucial to recognize that while data can provide valuable insights, soccer matches often defy simple predictions due to numerous unpredictable variables such as player performance, tactics, and game-changing moments. Nonetheless, as the soccer season progresses and more data becomes available, our models can continue to evolve and refine their predictions, offering even greater accuracy and insights into the beautiful game. Here's a snapshot of the model's prediction alongside the actual result for yesterday's match. Let's keep exploring the fascinating world where data meets sports! #DataAnalysis #SportsAnalytics #PredictiveModeling #DataDrivenInsights #Soccer #SportingEvents #DataScience #Analytics #DataVisualization