Data Science & Machine Learning

Statistics Roadmap for Data Science! Phase 1: Fundamentals of Statistics 1️⃣ Basic Concepts -Introduction to Statistics -Types of Data -Descriptive Statistics 2️⃣ Probability -Basic Probability -Conditional Probability -Probability Distributions Phase 2: Intermediate Statistics 3️⃣ Inferential Statistics -Sampling and Sampling Distributions -Hypothesis Testing -Confidence Intervals 4️⃣ Regression Analysis -Linear Regression -Diagnostics and Validation Phase 3: Advanced Topics 5️⃣ Advanced Probability and Statistics -Advanced Probability Distributions -Bayesian Statistics 6️⃣ Multivariate Statistics -Principal Component Analysis (PCA) -Clustering Phase 4: Statistical Learning and Machine Learning 7️⃣ Statistical Learning -Introduction to Statistical Learning -Supervised Learning -Unsupervised Learning Phase 5: Practical Application 8️⃣ Tools and Software -Statistical Software (R, Python) -Data Visualization (Matplotlib, Seaborn, ggplot2) 9️⃣ Projects and Case Studies -Capstone Project -Case Studies Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624 ENJOY LEARNING 👍👍

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The Data Science skill no one talks about... Every aspiring data scientist I talk to thinks their job starts when someone else gives them:     1. a dataset, and     2. a clearly defined metric to optimize for, e.g. accuracy But it doesn’t. It starts with a business problem you need to understand, frame, and solve. This is the key data science skill that separates senior from junior professionals. Let’s go through an example. Example Imagine you are a data scientist at Uber. And your product lead tells you:

    👩‍💼: “We want to decrease user churn by 5% this quarter”

We say that a user churns when she decides to stop using Uber. But why? There are different reasons why a user would stop using Uber. For example:    1.  “Lyft is offering better prices for that geo” (pricing problem)    2. “Car waiting times are too long” (supply problem)    3. “The Android version of the app is very slow” (client-app performance problem) You build this list ↑ by asking the right questions to the rest of the team. You need to understand the user’s experience using the app, from HER point of view. Typically there is no single reason behind churn, but a combination of a few of these. The question is: which one should you focus on? This is when you pull out your great data science skills and EXPLORE THE DATA 🔎. You explore the data to understand how plausible each of the above explanations is. The output from this analysis is a single hypothesis you should consider further. Depending on the hypothesis, you will solve the data science problem differently. For example… Scenario 1: “Lyft Is Offering Better Prices” (Pricing Problem) One solution would be to detect/predict the segment of users who are likely to churn (possibly using an ML Model) and send personalized discounts via push notifications. To test your solution works, you will need to run an A/B test, so you will split a percentage of Uber users into 2 groups:     The A group. No user in this group will receive any discount.     The B group. Users from this group that the model thinks are likely to churn, will receive a price discount in their next trip. You could add more groups (e.g. C, D, E…) to test different pricing points.

In a nutshell

    1. Translating business problems into data science problems is the key data science skill that separates a senior from a junior data scientist. 2. Ask the right questions, list possible solutions, and explore the data to narrow down the list to one. 3. Solve this one data science problem

Python vs. R for aspiring data scientist In the growing field of data science, the question of Python vs R – which should a data scientists choose? that bothers professionals and students the most. Your decision will affect your career prospects, job opportunities, and even your work-related happiness greatly. As the demand for data scientists has been increasing day by day, getting to know the intricacies of these two powerful languages has become a must in this highly competitive field. Read more.....

➡ 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝 𝐃𝐞𝐯𝐢𝐚𝐭𝐢𝐨𝐧:-The Standard Deviation is the square root of the variance. It gives a measure of the average distance from the mean, which is easier to interpret than variance because it is in the same units as the data.

➡ 𝐕𝐚𝐫𝐢𝐚𝐧𝐜𝐞:Variance measures the average squared deviations from the mean. It gives us an idea of how much the data points vary around the mean. There are two types of variance: 𝐏𝐨𝐩𝐮𝐥𝐚𝐭𝐢𝐨𝐧 𝐕𝐚𝐫𝐢𝐚𝐧𝐜𝐞:- When we have data for the entire population. 𝐒𝐚𝐦𝐩𝐥𝐞 𝐕𝐚𝐫𝐢𝐚𝐧𝐜𝐞:- When the data is just a sample of a larger population.

𝐓𝐲𝐩𝐞𝐬 𝐨𝐟 𝐃𝐚𝐭𝐚 𝟏. 𝐐𝐮𝐚𝐥𝐢𝐭𝐚𝐭𝐢𝐯𝐞 𝐯𝐬. 𝐐𝐮𝐚𝐧𝐭𝐢𝐭𝐚𝐭𝐢𝐯𝐞 𝐐𝐮𝐚𝐥𝐢𝐭𝐚𝐭𝐢𝐯𝐞 𝐃𝐚𝐭𝐚: Describes characteristics or qualities (e.g., color, gender, brand). 𝐐𝐮𝐚𝐧𝐭𝐢𝐭𝐚𝐭𝐢𝐯𝐞 𝐃𝐚𝐭𝐚: Represents numerical values (e.g., age, height, income). 𝟐. 𝐃𝐢𝐬𝐜𝐫𝐞𝐭𝐞 𝐯𝐬. 𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬 𝐃𝐢𝐬𝐜𝐫𝐞𝐭𝐞 𝐃𝐚𝐭𝐚: Can only take on specific, separate values (e.g., number of siblings, number of cars). 𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬 𝐃𝐚𝐭𝐚: Can take on any value within a range (e.g., height, weight, time).

𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 is the backbone of data science. It provides the tools and techniques to collect, analyze, interpret, and present data. It's essential for making informed decisions, understanding patterns, and extracting meaningful insights. ** 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐜𝐞 𝐨𝐟 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 1- 𝐃𝐚𝐭𝐚-𝐃𝐫𝐢𝐯𝐞𝐧 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬: Statistics helps you make evidence-based decisions, reducing the risk of errors and improving outcomes. 2- 𝐏𝐚𝐭𝐭𝐞𝐫𝐧 𝐑𝐞𝐜𝐨𝐠𝐧𝐢𝐭𝐢𝐨𝐧: By analyzing data, you can identify trends, correlations, and anomalies that might otherwise go unnoticed. 3- 𝐇𝐲𝐩𝐨𝐭𝐡𝐞𝐬𝐢𝐬 𝐓𝐞𝐬𝐭𝐢𝐧𝐠: Statistics allows you to test hypotheses and determine if observed results are significant or due to chance. 4- 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐌𝐨𝐝𝐞𝐥𝐢𝐧𝐠: Statistical models can be used to predict future events or outcomes based on past data.

Data Science Interview Deep Dive Question: Explain the working mechanism of XGBoost and how it improves over traditional Gradient Boosting Machines (GBMs). Explain 3 key hyperparameters of XGBoost. My Answer: Working Mechanism: XGBoost, like other gradient boosting methods, builds an ensemble of weak learners, typically decision trees, by optimizing a loss function iteratively. It starts by fitting a base model (e.g., a single decision tree) and calculates the residual errors of the predictions. These residuals become the target for the next tree to predict. This process is repeated iteratively, with each new tree added to correct the errors made by the previous trees. The model’s final prediction is the sum of all the weak learners’ outputs. Differences Between XGBoost and Traditional GBMs: 1. Regularization: • XGBoost incorporates L1 (Lasso) and L2 (Ridge) regularization techniques in its objective function to prevent overfitting. This is a significant improvement over traditional GBMs, which do not have built-in regularization. 2. Second-Order Taylor Approximation: • XGBoost uses a second-order Taylor approximation for optimizing the loss function. This allows it to consider both the gradient (first derivative) and the Hessian (second derivative) of the loss function, providing a more accurate update to the model than traditional GBMs, which only use the first derivative. 3. Handling Missing Values: • XGBoost has an in-built mechanism for handling missing values by learning the best direction to take when it encounters missing data in the training phase, which is not present in traditional GBMs. 4. Parallel Processing: • XGBoost is designed to work in parallel, making it significantly faster than traditional GBMs. It achieves this by constructing trees in a parallelizable way, optimizing the memory usage, and using cache-aware access patterns. 5. Tree Pruning and Sparsity Awareness: • XGBoost uses a “max_depth” parameter instead of “num_iterations” to control tree growth, leading to more robust trees. It also performs post-pruning, reducing the number of splits after the tree is fully grown, thereby eliminating unnecessary branches. Key Hyperparameters and Their Impact: 1. Learning Rate (eta): • Controls the contribution of each tree to the final model. Lower values slow down the learning process but can lead to better generalization. It requires more boosting rounds to converge, increasing training time. 2. Number of Estimators (n_estimators): • The number of trees to be built. A higher number can lead to overfitting, while too few can lead to underfitting. Balancing this parameter is crucial for optimal model performance. 3. Maximum Depth (max_depth): • Controls the depth of each tree. Deeper trees can model more complex relationships but risk overfitting. Shallow trees, on the other hand, might underfit the data.

Virgilio Data Science This repository contains articles, GitHub repos and Kaggle kernels which provides data science and machine learning projects with code.                                                                           Creator:  virgili0 Stars ⭐️: 13.9k Forked By: 2.5k https://github.com/virgili0/Virgilio

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What 𝗠𝗟 𝗰𝗼𝗻𝗰𝗲𝗽𝘁𝘀 are commonly asked in 𝗱𝗮𝘁𝗮 𝘀𝗰𝗶𝗲𝗻𝗰𝗲 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀? These are fair game in interviews at 𝘀𝘁𝗮𝗿𝘁𝘂𝗽𝘀, 𝗰𝗼𝗻𝘀𝘂𝗹𝘁𝗶𝗻𝗴 & 𝗹𝗮𝗿𝗴𝗲 𝘁𝗲𝗰𝗵. 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 - Supervised vs. Unsupervised Learning - Overfitting and Underfitting - Cross-validation - Bias-Variance Tradeoff - Accuracy vs Interpretability - Accuracy vs Latency 𝗠𝗟 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 - Logistic Regression - Decision Trees - Random Forest - Support Vector Machines - K-Nearest Neighbors - Naive Bayes - Linear Regression - Ridge and Lasso Regression - K-Means Clustering - Hierarchical Clustering - PCA 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 𝗦𝘁𝗲𝗽𝘀 - EDA - Data Cleaning (e.g. missing value imputation) - Data Preprocessing (e.g. scaling) - Feature Engineering (e.g. aggregation) - Feature Selection (e.g. variable importance) - Model Training (e.g. gradient descent) - Model Evaluation (e.g. AUC vs Accuracy) - Model Productionization 𝗛𝘆𝗽𝗲𝗿𝗽𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿 𝗧𝘂𝗻𝗶𝗻𝗴 - Grid Search - Random Search - Bayesian Optimization 𝗠𝗟 𝗖𝗮𝘀𝗲𝘀 - [Capital One] Detect credit card fraudsters - [Amazon] Forecast monthly sales - [Airbnb] Estimate lifetime value of a guest I have curated the best interview resources to crack Data Science Interviews 👇👇 https://topmate.io/analyst/1024129 Like if you need similar content 😄👍

DATA SCIENCE INTERVIEW QUESTIONS [PART-4] Q. Why does overfitting occur? A. Overfitting happens when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data. This means that the noise or random fluctuations in the training data is picked up and learned as concepts by the model Q. What is ensemble learning? A. Ensemble learning is the process by which multiple models, such as classifiers or experts, are strategically generated and combined to solve a particular computational intelligence problem. Ensemble learning is primarily used to improve the (classification, prediction, function approximation, etc.) performance of a model, or reduce the likelihood of an unfortunate selection of a poor one. Q. What is F1 score? A. The F1 score is defined as the harmonic mean of precision and recall. As a short reminder, the harmonic mean is an alternative metric for the more common arithmetic mean. It is often useful when computing an average rate. In the F1 score, we compute the average of precision and recall. Q. What is pickling and unpickling? A.“Pickling” is the process whereby a Python object hierarchy is converted into a byte stream, and “unpickling” is the inverse operation, whereby a byte stream (from a binary file or bytes-like object) is converted back into an object hierarchy. Q. What is lambda function? A. Python Lambda Functions are anonymous function means that the function is without a name. As we already know that the def keyword is used to define a normal function in Python. Similarly, the lambda keyword is used to define an anonymous function in Python. Q. What is the trade of between bias and variance ? A. Bias is the simplifying assumptions made by the model to make the target function easier to approximate. Variance is the amount that the estimate of the target function will change given different training data. Trade-off is tension between the error introduced by the bias and the variance. ENJOY LEARNING 👍👍

What happens when we have correlated features in our data? In random forest, since random forest samples some features to build each tree, the information contained in correlated features is twice as much likely to be picked than any other information contained in other features. In general, when you are adding correlated features, it means that they linearly contains the same information and thus it will reduce the robustness of your model. Each time you train your model, your model might pick one feature or the other to "do the same job" i.e. explain some variance, reduce entropy, etc.

What are the main assumptions of linear regression? There are several assumptions of linear regression. If any of them is violated, model predictions and interpretation may be worthless or misleading. 1) Linear relationship between features and target variable. 2) Additivity means that the effect of changes in one of the features on the target variable does not depend on values of other features. For example, a model for predicting revenue of a company have of two features - the number of items a sold and the number of items b sold. When company sells more items a the revenue increases and this is independent of the number of items b sold. But, if customers who buy a stop buying b, the additivity assumption is violated. 3) Features are not correlated (no collinearity) since it can be difficult to separate out the individual effects of collinear features on the target variable. 4) Errors are independently and identically normally distributed (yi = B0 + B1*x1i + ... + errori): i) No correlation between errors (consecutive errors in the case of time series data). ii) Constant variance of errors - homoscedasticity. For example, in case of time series, seasonal patterns can increase errors in seasons with higher activity. iii) Errors are normaly distributed, otherwise some features will have more influence on the target variable than to others. If the error distribution is significantly non-normal, confidence intervals may be too wide or too narrow.

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