T. Scott Clendaniel’s Post

Great visualization

#ArtificialIntelligence #DataScience #Technology 🎉👏🎉👏🎉 𝗧𝗢𝗣 𝟭𝟭 #BigData 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝘀 via Avi Chawla! 𝗜𝗻𝗰𝗹𝘂𝗱𝗲𝘀: ▶ 𝗡𝗼𝗿𝗺𝗮𝗹 ▶ 𝗕𝗲𝗿𝗻𝗼𝘂𝗹𝗹𝗶 ▶ 𝗕𝗶𝗻𝗼𝗺𝗶𝗮𝗹 ▶ 𝗣𝗼𝗶𝘀𝘀𝗼𝗻 ▶ 𝗘𝘅𝗽𝗼𝗻𝗲𝗻𝘁𝗶𝗮𝗹 ▶ 𝗚𝗮𝗺𝗺𝗮 ▶ 𝗕𝗲𝘁𝗮 ▶ 𝗨𝗻𝗶𝗳𝗼𝗿𝗺 ▶ 𝗦𝘁𝘂𝗱𝗲𝗻𝘁 "𝘁" ▶ 𝗟𝗼𝗴 𝗡𝗼𝗿𝗺𝗮𝗹 ▶ 𝗪𝗲𝗶𝗯𝘂𝗹𝗹 Be sure to check out more gems like this on the blog for Daily Dose of Data Science! https://bit.ly/3Hz1cpt Enjoy, T. Scott Clendaniel/ #TScottClendaniel

distributions on Deep learning

#ArtificialIntelligence #DataScience #Technology 🎉👏🎉👏🎉 𝗧𝗢𝗣 𝟭𝟭 #BigData 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝘀 via Avi Chawla! 𝗜𝗻𝗰𝗹𝘂𝗱𝗲𝘀: ▶ 𝗡𝗼𝗿𝗺𝗮𝗹 ▶ 𝗕𝗲𝗿𝗻𝗼𝘂𝗹𝗹𝗶 ▶ 𝗕𝗶𝗻𝗼𝗺𝗶𝗮𝗹 ▶ 𝗣𝗼𝗶𝘀𝘀𝗼𝗻 ▶ 𝗘𝘅𝗽𝗼𝗻𝗲𝗻𝘁𝗶𝗮𝗹 ▶ 𝗚𝗮𝗺𝗺𝗮 ▶ 𝗕𝗲𝘁𝗮 ▶ 𝗨𝗻𝗶𝗳𝗼𝗿𝗺 ▶ 𝗦𝘁𝘂𝗱𝗲𝗻𝘁 "𝘁" ▶ 𝗟𝗼𝗴 𝗡𝗼𝗿𝗺𝗮𝗹 ▶ 𝗪𝗲𝗶𝗯𝘂𝗹𝗹 Be sure to check out more gems like this on the blog for Daily Dose of Data Science! https://bit.ly/3Hz1cpt Enjoy, T. Scott Clendaniel/ #TScottClendaniel

#ArtificialIntelligence #DataScience #Technology 🎉👏🎉👏🎉 𝗧𝗢𝗣 𝟭𝟭 #BigData 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝘀 via Avi Chawla! 𝗜𝗻𝗰𝗹𝘂𝗱𝗲𝘀: ▶ 𝗡𝗼𝗿𝗺𝗮𝗹 ▶ 𝗕𝗲𝗿𝗻𝗼𝘂𝗹𝗹𝗶 ▶ 𝗕𝗶𝗻𝗼𝗺𝗶𝗮𝗹 ▶ 𝗣𝗼𝗶𝘀𝘀𝗼𝗻 ▶ 𝗘𝘅𝗽𝗼𝗻𝗲𝗻𝘁𝗶𝗮𝗹 ▶ 𝗚𝗮𝗺𝗺𝗮 ▶ 𝗕𝗲𝘁𝗮 ▶ 𝗨𝗻𝗶𝗳𝗼𝗿𝗺 ▶ 𝗦𝘁𝘂𝗱𝗲𝗻𝘁 "𝘁" ▶ 𝗟𝗼𝗴 𝗡𝗼𝗿𝗺𝗮𝗹 ▶ 𝗪𝗲𝗶𝗯𝘂𝗹𝗹 Be sure to check out more gems like this on the blog for Daily Dose of Data Science! https://bit.ly/3Hz1cpt Enjoy, T. Scott Clendaniel/ #TScottClendaniel

#ArtificialIntelligence #DataScience #Technology 🎉👏🎉👏🎉 𝗧𝗢𝗣 𝟭𝟭 #BigData 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝘀 via Avi Chawla! 𝗜𝗻𝗰𝗹𝘂𝗱𝗲𝘀: ▶ 𝗡𝗼𝗿𝗺𝗮𝗹 ▶ 𝗕𝗲𝗿𝗻𝗼𝘂𝗹𝗹𝗶 ▶ 𝗕𝗶𝗻𝗼𝗺𝗶𝗮𝗹 ▶ 𝗣𝗼𝗶𝘀𝘀𝗼𝗻 ▶ 𝗘𝘅𝗽𝗼𝗻𝗲𝗻𝘁𝗶𝗮𝗹 ▶ 𝗚𝗮𝗺𝗺𝗮 ▶ 𝗕𝗲𝘁𝗮 ▶ 𝗨𝗻𝗶𝗳𝗼𝗿𝗺 ▶ 𝗦𝘁𝘂𝗱𝗲𝗻𝘁 "𝘁" ▶ 𝗟𝗼𝗴 𝗡𝗼𝗿𝗺𝗮𝗹 ▶ 𝗪𝗲𝗶𝗯𝘂𝗹𝗹 Be sure to check out more gems like this on the blog for Daily Dose of Data Science! https://bit.ly/3Hz1cpt Enjoy, T. Scott Clendaniel/ #TScottClendaniel

📊 Curious about the world of data science? Here are 11 essential probability distributions that every data enthusiast should know! 🌍 Think of these distributions as different lenses 🔍 that help us understand and describe various real-world phenomena: 1. Normal Distribution: The classic "bell curve" 🔔 that pops up everywhere! 2. Bernoulli Distribution: Flip a coin 🪙 - heads or tails? 3. Binomial Distribution: Like flipping a coin multiple times and counting the wins! 🎉 4. Poisson Distribution: Predicting rare events, like how many people win the lottery 🍀 5. Exponential Distribution: Modeling waiting times ⏰, like how long until the next bus arrives 6. Gamma Distribution: A flexible friend that adapts to many situations 🦸♂️ 7. Beta Distribution: Dealing with probabilities and proportions, like the chances of your favorite team winning 🏆 8. Uniform Distribution: When everything has an equal chance of happening 🎲 9. Student t-distribution: Estimating averages when you have a small sample size 🧮 10. Log Normal Distribution: Helping us handle data that's skewed or uneven 📈 11. Weibull Distribution: Analyzing how long things last, from lightbulbs to friendships! 💡👥 By understanding these distributions, you can make better data-driven decisions 📈📉 and unlock the secrets hidden in the numbers! 🔓 Want to dive deeper? Check out this cool blog post! 🌐 blog.dailydoseofds.com #DataScience #Statistics #ProbabilityDistributions #DataLiteracy #NumbersAreFun

This is a cheat sheet we all need to keep handy. If you aren’t doing distributions often it’s easy to forget!

#ArtificialIntelligence #DataScience #Technology 🎉👏🎉👏🎉 𝗧𝗢𝗣 𝟭𝟭 #BigData 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝘀 via Avi Chawla! 𝗜𝗻𝗰𝗹𝘂𝗱𝗲𝘀: ▶ 𝗡𝗼𝗿𝗺𝗮𝗹 ▶ 𝗕𝗲𝗿𝗻𝗼𝘂𝗹𝗹𝗶 ▶ 𝗕𝗶𝗻𝗼𝗺𝗶𝗮𝗹 ▶ 𝗣𝗼𝗶𝘀𝘀𝗼𝗻 ▶ 𝗘𝘅𝗽𝗼𝗻𝗲𝗻𝘁𝗶𝗮𝗹 ▶ 𝗚𝗮𝗺𝗺𝗮 ▶ 𝗕𝗲𝘁𝗮 ▶ 𝗨𝗻𝗶𝗳𝗼𝗿𝗺 ▶ 𝗦𝘁𝘂𝗱𝗲𝗻𝘁 "𝘁" ▶ 𝗟𝗼𝗴 𝗡𝗼𝗿𝗺𝗮𝗹 ▶ 𝗪𝗲𝗶𝗯𝘂𝗹𝗹 Be sure to check out more gems like this on the blog for Daily Dose of Data Science! https://bit.ly/3Hz1cpt Enjoy, T. Scott Clendaniel/ #TScottClendaniel

Exploratory Data Analysis (EDA), particularly focusing on understanding the distribution of the data, is crucial in data science for several reasons: 1. Informs Model Selection: Different data science models have different underlying assumptions about the data they work with. Understanding the distribution of your data helps to choose the most appropriate model for your analysis. For example: If the data is heavily skewed (lopsided towards one side), a linear regression model might not be suitable. It might need to consider models that can handle non-normal distributions. 2. Identifies Outliers and Anomalies: Distributions can reveal outliers, data points that fall far outside the majority of the data. These outliers can significantly impact analysis if not handled appropriately. For example: A sales dataset might have a few exceptionally high outliers due to one-time promotions. Identifying these outliers helps you understand the typical sales pattern and avoid skewing the results. 3. Guides Feature Engineering: Feature engineering is the process of creating new features from existing data to improve model performance. Analyzing distributions can help to identify opportunities for feature creation. For example: If a feature has a very wide range of values, it might be beneficial to transform it (e.g., taking the logarithm) to create a more manageable distribution for modeling. 4. Provides Context for Interpretation: Once you build a model, interpreting its results is crucial. Understanding the distribution of the data helps to put those results in context. For example: While predicting customer churn, knowing the distribution of customer tenures can help you understand the model's performance across different customer segments. 5. Ensures Data Quality: Analyzing distributions can uncover potential data quality issues, such as missing values or inconsistencies. For example: If a feature has a large number of missing values concentrated in a specific range, it might indicate a data collection problem that needs to be addressed. So, understanding the distribution of your data is a fundamental step in any data science project. It plays a critical role in choosing the right tools, handling data issues, and ultimately, drawing reliable conclusions from your analysis. #AI #ml #artificialinteligence #machinelearning #datascience #dataanalytics #datamining #bigdata #EDA #datavisualization