Data Science & Machine Learning

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Free Datasets to work on Power BI + SQL projects 👇👇 1. AdventureWorks Sample Database: - Link: [AdventureWorks Sample Database](https://docs.microsoft.com/en-us/sql/samples/adventureworks-install-configure?view=sql-server-ver15) - Description: A sample database provided by Microsoft, containing sales, products, customers, and other related data. 2. Online Retail Dataset: - Link: [UCI Machine Learning Repository - Online Retail Dataset](https://archive.ics.uci.edu/ml/datasets/online+retail) - Description: Transactional data from an online retail store, suitable for customer segmentation and sales analysis. 3. Supermarket Sales Dataset: - Link: [Supermarket Sales Dataset](https://www.kaggle.com/aungpyaeap/supermarket-sales) - Description: Sales data from a supermarket, useful for inventory management and sales performance analysis. 4. Yahoo Finance (Historical Stock Data): - Link: [Yahoo Finance](https://finance.yahoo.com/) - Description: Historical stock data for various companies, suitable for financial analysis and visualization. 5. Human Resources Analytics: Employee Attrition and Performance: - Link: [Kaggle HR Analytics Dataset](https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset) - Description: Employee data including demographics, performance, and attrition information, suitable for employee performance analysis. Bonus Open Sources Resources: https://t.me/DataPortfolio/16 These datasets are freely available for practicing Power BI and SQL skills. You can download them from the provided links and import them into your SQL database management system (e.g., MySQL, SQL Server, PostgreSQL) for hands-on ☺️💪

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ML Interview Question ⬇️ ➡️ Logistic Regression The interviewer asked to explain Logistic Regression along with its: 🔷 Cost function 🔷 Assumptions 🔷 Evaluation metrics Here is the step by step approach to answer: ☑️ Cost function: Point out how logistic regression uses log loss for classification. ☑️ Assumptions: Explain LR assumes features are independent and they have a linear link. ☑️ Evaluation metrics: Discuss accuracy, precision, and F1-score to measure performance. Knowing every concept is important but more than that, it is important to convey our knowledge💯

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Planning for Data Science or Data Engineering Interview. Focus on SQL & Python first. Here are some important questions which you should know. 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐒𝐐𝐋 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 1- Find out nth Order/Salary from the tables. 2- Find the no of output records in each join from given Table 1 & Table 2 3- YOY,MOM Growth related questions. 4- Find out Employee ,Manager Hierarchy (Self join related question) or Employees who are earning more than managers. 5- RANK,DENSERANK related questions 6- Some row level scanning medium to complex questions using CTE or recursive CTE, like (Missing no /Missing Item from the list etc.) 7- No of matches played by every team or Source to Destination flight combination using CROSS JOIN. 8-Use window functions to perform advanced analytical tasks, such as calculating moving averages or detecting outliers. 9- Implement logic to handle hierarchical data, such as finding all descendants of a given node in a tree structure. 10-Identify and remove duplicate records from a table. 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐏𝐲𝐭𝐡𝐨𝐧 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 1- Reversing a String using an Extended Slicing techniques. 2- Count Vowels from Given words . 3- Find the highest occurrences of each word from string and sort them in order. 4- Remove Duplicates from List. 5-Sort a List without using Sort keyword. 6-Find the pair of numbers in this list whose sum is n no. 7-Find the max and min no in the list without using inbuilt functions. 8-Calculate the Intersection of Two Lists without using Built-in Functions 9-Write Python code to make API requests to a public API (e.g., weather API) and process the JSON response. 10-Implement a function to fetch data from a database table, perform data manipulation, and update the database. Join for more: https://t.me/datasciencefun ENJOY LEARNING 👍👍

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Advanced Jupyter Notebook Shortcut Keys ⌨ Multicursor Editing: Ctrl + Click: Place multiple cursors for simultaneous editing. Navigate to Specific Cells: Ctrl + L: Center the active cell in the viewport. Ctrl + J: Jump to the first cell. Cell Output Management: Shift + L: Toggle line numbers in the code cell. Ctrl + M + H: Hide all cell outputs. Ctrl + M + O: Toggle all cell outputs. Markdown Editing: Ctrl + M + B: Add bullet points in Markdown. Ctrl + M + H: Insert a header in Markdown. Code Folding/Unfolding: Alt + Click: Fold or unfold a section of code. Quick Help: H: Open the help menu in Command Mode. These shortcuts improve workflow efficiency in Jupyter Notebook, helping you to code faster and more effectively. I have curated best Data Analytics Resources 👇👇 https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02 Like this post for more content like this 👍♥️ Share with credits: https://t.me/sqlspecialist Hope it helps :)

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Important data science topics you should definitely be aware of 1. Statistics & Probability Descriptive Statistics (mean, median, mode, variance, std deviation) Probability Distributions (Normal, Binomial, Poisson) Bayes' Theorem Hypothesis Testing (t-test, chi-square test, ANOVA) Confidence Intervals 2. Data Manipulation & Analysis Data wrangling/cleaning Handling missing values & outliers Feature engineering & scaling GroupBy operations Pivot tables Time series manipulation 3. Programming (Python/R) Data structures (lists, dictionaries, sets) Libraries: Python: pandas, NumPy, matplotlib, seaborn, scikit-learn R: dplyr, ggplot2, caret Writing reusable functions Working with APIs & files (CSV, JSON, Excel) 4. Data Visualization Plot types: bar, line, scatter, histograms, heatmaps, boxplots Dashboards (Power BI, Tableau, Plotly Dash, Streamlit) Communicating insights clearly 5. Machine Learning Supervised Learning Linear & Logistic Regression Decision Trees, Random Forest, Gradient Boosting (XGBoost, LightGBM) SVM, KNN Unsupervised Learning K-means Clustering PCA Hierarchical Clustering Model Evaluation Accuracy, Precision, Recall, F1-Score Confusion Matrix, ROC-AUC Cross-validation, Grid Search 6. Deep Learning (Basics) Neural Networks (perceptron, activation functions) CNNs, RNNs (just an overview unless you're going deep into DL) Frameworks: TensorFlow, PyTorch, Keras 7. SQL & Databases SELECT, WHERE, GROUP BY, JOINS, CTEs, Subqueries Window functions Indexes and Query Optimization 8. Big Data & Cloud (Basics) Hadoop, Spark AWS, GCP, Azure (basic knowledge of data services) 9. Deployment & MLOps (Basic Awareness) Model deployment (Flask, FastAPI) Docker basics CI/CD pipelines Model monitoring 10. Business & Domain Knowledge Framing a problem Understanding business KPIs Translating data insights into actionable strategies I have curated the best interview resources to crack Data Science Interviews 👇👇 https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D Like for the detailed explanation on each topic 😄👍

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Some important questions to crack data science interview Q. Describe how Gradient Boosting works. A. Gradient boosting is a type of machine learning boosting. It relies on the intuition that the best possible next model, when combined with previous models, minimizes the overall prediction error. If a small change in the prediction for a case causes no change in error, then next target outcome of the case is zero. Gradient boosting produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. Q. Describe the decision tree model. A. Decision Trees are a type of Supervised Machine Learning where the data is continuously split according to a certain parameter. The leaves are the decisions or the final outcomes. A decision tree is a machine learning algorithm that partitions the data into subsets. Q. What is a neural network? A. Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input. They, also known as Artificial Neural Networks, are the subset of Deep Learning. Q. Explain the Bias-Variance Tradeoff A. The bias–variance tradeoff is the property of a model that the variance of the parameter estimated across samples can be reduced by increasing the bias in the estimated parameters. Q. What’s the difference between L1 and L2 regularization? A. The main intuitive difference between the L1 and L2 regularization is that L1 regularization tries to estimate the median of the data while the L2 regularization tries to estimate the mean of the data to avoid overfitting. That value will also be the median of the data distribution mathematically. React ❤️ for more

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1. What do Tableau's sets and groups mean? Data is grouped using sets and groups according to predefined criteria. The primary distinction between the two is that although a set can have only two options—either in or out—a group can divide the dataset into several groups. A user should decide which group or sets to apply based on the conditions. 3.What do you mean by a Bag of Words (BOW)? It is used for word frequency or occurrences to train a classifier. It contains a text representation that describes the frequency with which words appear in a document. It has two steps: -A list of terms that are well-known. -A metric for determining the existence of well-known terms. 3. What are Nested Triggers? Triggers may implement DML by using INSERT, UPDATE, and DELETE statements. These triggers that contain DML and find other triggers for data modification are called Nested Triggers. 4. What is a True positive rate and a false positive rate? True positive rate or Recall: It gives us the percentage of the true positives captured by the model out of all the Actual Positive class. TPR = TP/ (TP+FN) False Positive rate: It gives us the percentage of all the false positives by my model prediction from the all Actual Negative class. FPR = FP/(FP+TN)

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