Data Science @datasciencekull

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L1 Regularization: A Beginner's Guide to Enhanced Model Performance #DataScience #statistics #MachineLearning #modelperformance statonomy.blogspot.com/2024/02/l1-reg…

Best Jobs for Introverts: Unlock Your Introvert Superpowers!! #introverts #mentalhealth #mentalhealthAwareness #menta gogetcalm.blogspot.com/2024/02/best-j…

Unsure what Kaplan-Meier curves are & how they unlock survival insights? Demystify this #dataanalysis tool & see its real-world applications. Click to learn! #DataScience #DataAnalytics statonomy.blogspot.com/2024/02/using-…

"Explore the transformative impact of #GoogleAI on the future of #Artificialintelligence. Discover how cutting-edge technologies and innovations are shaping the landscape, driving advancements that redefine the possibilities of #AI statonomy.blogspot.com/2024/01/google…

Explore the significance of Shannon Diversity Index also called as Shannon-Wiener Index in ecological research and understand how it measures species diversity. #Statistics #DataAnalytics statonomy.blogspot.com/2024/01/shanno…

Witness the clash of statistical titans! T-Test vs. Chi-Square demystified in this comprehensive guide. Equip yourself with the knowledge to wield the right statistical weapon. #Statistics #ttest #DataScience #DataAnalytics statonomy.blogspot.com/2024/02/t-test…

"📊 Unlocking the Power of Primary Data Collection! Dive into our latest blog post where we demystify the methods behind gathering first-hand insights. From surveys to interviews, discover the tools shaping meaningful data.#DataCollection #ResearchMethods" statonomy.blogspot.com/2024/01/primar…

"Diving into the world of statistics! 📊✨ Discovering the grammar of science, where data transforms into meaningful insights. Embrace the power to explore, analyze, and uncover patterns. Let's turn numbers into stories! #Statistics #DataJourney" statonomy.blogspot.com/2024/01/statis…

Kulli Data Science: Understanding the Sigmoid Function in AI #Calculus #MachineLearning #DataScience kullidatascience.blogspot.com/2024/01/unders…

Kulli Data Science: Mastering EM Algorithm: Techniques and Best Practices #MachineLearning #AI #DataScience kullidatascience.blogspot.com/2024/01/Expect…

Demystifying Confusion Matrix: A Step-by-Step Guide for R Users! #DataScience #DataAnalytics kullidatascience.blogspot.com/2024/01/Confus…

A/B Testing and Causal Inference are stuck in the dark ages. Enter Causal ML - a new suite of uplift modeling and causal inference methods. Let's dive in. 1. Causal machine learning (ML) refers to a branch of machine learning that focuses on understanding and modeling the causal relationships in data, rather than just finding correlations. 2. Causal ML is particularly useful in decision-making scenarios where understanding the cause of an event is more important than just predicting its occurrence. Typical use case in business: Did spending money on online advertising increase sales? 3. This often involves Causal Inference, Counterfactual Reasoning, Causal Discovery, and Intervention Effect Estimation. Let's break these terms down. 4. Causal Inference: Determining whether a relationship between two variables is causal or merely correlational. This often involves statistical methods that attempt to control for confounding variables. 5. Counterfactual Reasoning: Understanding what would happen to one variable if you changed another, often framed as "what if" questions. For instance, "What would happen to hotel cancelations outcomes if lead time (time between purchase and hotel stay) was reduced?" 6. Causal Discovery: Identifying causal relationships from data without prior knowledge about the potential connections between variables. 7. Intervention and Treatment Effects: Estimating the impact of interventions (like a new advertisement or email campaign) on an outcome. CausalML Package: causalml.readthedocs.io/en/latest/abou… === Want to learn Causal ML in #Python? I have a free workshop where I will share how to use the CausalML library, which is used by big companies like Uber, Google, and more for their online experiments. 👉 Register here (seats are limited to 500): us02web.zoom.us/webinar/regist…

Probability distributions are frequently used in statistics and data science in business. In 2 minutes, I'll teach you everything I know about distributions. Let's dive in. 1. Statistical Distributions: There are literally 100s of distributions to choose from when modeling data. Choices seem endless. The image is aimed at simplifying distributional selection. 2. Discrete Distributions: Discrete distributions are used when the data can take on only specific, distinct values. These values are often integers, like the number of sales calls made or the number of customers that converted. 3. Continuous distributions are used for data that can take on any value within a range or interval. These values are typically real numbers, like the percentage of visitors that converted or the forecasted revenue over the next 6 months. 4. Probability Mass Function (Discrete): Discrete distributions are described by a probability mass function, which gives the probability that a discrete random variable is exactly equal to some value. In a graph, a discrete distribution is often represented by a series of bars, where each bar represents the probability of each discrete outcome. 5. Probability Density Function (Continuous): Continuous distributions are described by a probability density function. The probability of the variable falling within a particular range is given by the area under the curve of the PDF within that range. In a graph, a continuous distribution is usually represented by a smooth curve. 6. Parametric Models: Many models assume a specific distribution. Linear Regression: Assumes normally distributed errors. Logistic Regression: Assumes a binomial distribution of the response variable. Naive Bayes Classifier: Assumes data features follow a certain distribution (like Gaussian, Multinomial, or Bernoulli) depending on the variant of the model. 7. Non-Parametric Models: In general, these models do not make strong assumptions about the form of the data distribution. Decision Trees, K-Nearest Neighbors, Support Vector Machines. 8. Loss Functions: Distributions will come up in Loss Functions in Machine Learning (e.g. XGBoost, LightGBM, CatBoost). Selecting the right Loss Function can often improve performance. I have used distributions for Loss Functions including Poisson and Tweedie Loss. Poisson is used for count data. Tweedie for mixed continuous data with many zeros like intermittent demand forecasting problems. 9. Picking the right distribution: The best way is to visualize the data. Histograms and kernel density plots can help you match the raw data to a distribution. Image Credit: pages.stern.nyu.edu/~adamodar/pdfi… === Ready to learn Data Science for Business? I put together a free on-demand workshop that covers the 10 skills that helped me make the transition to Data Scientist: learn.business-science.io/free-rtrack-ma… And if you'd like to speed it up, I have a live workshop next week where I'll share how to use ChatGPT for Data Science: learn.business-science.io/registration-c… If you like this post, please reshare ♻️ it so others can get value.

Forecasting time series is what made me stand out as a data scientist. But it took me 1 year to master ARIMA. In 1 minute, I'll teach you what took me 1 year. Let's go. 1. ARIMA and SARIMA are both statistical models used for forecasting time series data, where the goal is to predict future points in the series. 2. Business Uses: I got my start with ARIMA using it to predict sales demand (demand forecasting). But ARIMA and forecasting are also used heavily in econometrics, finance, retail, energy demand, and any situation where you need to know the future based on historical time series data. 3. ARIMA Decomposed: AR-I-MA stands for Autoregressive (AR), Integrated (I), Moving Average (MA). 4. Autoregressive (AR): This part of the model captures the relationship between an observation and a specified number of lagged observations. 5. Integrated (I): This involves differencing the time series data to make it stationary. A stationary time series is one whose properties do not depend on the time at which the series is observed, meaning it doesn't have trends or seasonal patterns. 6. Moving Average (MA): This part of the model allows the modeling of the error term as a linear combination of error terms occurring contemporaneously and at various times in the past. 7. Lowercase pdq notation: A non-seasonal ARIMA model is denoted as ARIMA(p, d, q) where: p is the number of lag observations in the model (AR part). d is the degree of differencing required to make the time series stationary. q is the size of the moving average window (MA part). 8. Linear Regression: The ARIMA is simply a Linear Regression model that includes the autoregressive (AR) components and the "moving average" (MA) aka the error terms. 9. SARIMA: Seasonal Autoregressive Integrated Moving-Average extends ARIMA by supporting Seasonal component(s). 10. PDQ-M Notation: Uppercase PDQ defines the orders, which are multiplied by M, the seasonal period (e.g. 4 for quarterly or 12 for monthly). === Ready to learn Data Science for Business? I put together a free on-demand workshop that covers the 10 skills that helped me make the transition to Data Scientist: learn.business-science.io/free-rtrack-ma… And if you'd like to speed it up, I have a live workshop where I'll share how to use ChatGPT for Data Science: learn.business-science.io/registration-c… If you like this post, please reshare ♻️ it so others can get value.

Ordinary Least Squares (OLS) Regression is one of the most important tools in a Data Scientist's toolbox. Here's everything you need to know. 1. OLS regression aims to find the best-fitting linear equation that describes the relationship between the dependent variable (often denoted as Y) and independent variables (denoted as X1, X2, ..., Xn). 2. OLS does this by minimizing the sum of the squares of the differences between the observed dependent variable values and those predicted by the linear model. These differences are called "residuals." 3. "Best fit" in the context of OLS means that the sum of the squares of the residuals is as small as possible. Mathematically, it's about finding the values of β0, β1, ..., βn that minimize this sum. 4. Slopes (β1, β2, ..., βn): These coefficients represent the change in the dependent variable for a one-unit change in the corresponding independent variable, holding other variables constant. 5. R-squared (R²): This statistic measures the proportion of variance in the dependent variable that is predictable from the independent variables. It ranges from 0 to 1, with higher values indicating a better fit of the model to the data. 6. t-Statistics and p-Values: For each coefficient, the t-statistic and its associated p-value test the null hypothesis that the coefficient is equal to zero (no effect). A small p-value (< 0.05) suggests that you can reject the null hypothesis. 7. Confidence Intervals: These intervals provide a range of plausible values for each coefficient (usually at the 95% confidence level). Understanding and interpreting these outputs is crucial for assessing the quality of the model, understanding the relationships between variables, and making predictions or conclusions based on the model. ==== Ready to learn Data Science for Business? I put together a free on-demand workshop that covers the 10 skills that helped me make the transition to Data Scientist: learn.business-science.io/free-rtrack-ma… And if you'd like to speed it up, I have a live workshop next week where I'll share how to use ChatGPT for Data Science: learn.business-science.io/registration-c…

@realmeIndia August 28 2023 #realme5thAnniversary #realmetheboss #realme

@realmeIndia Will this device compete with #redmi125g ?

Will @realmeIndia give tough fight to #redmi125g ? @RedmiIndia ? Mm. Will see..

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