Data Science & Machine Learning

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Data Science Essential Libraries ✅

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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NLP techniques every Data Science professional should know! 1. Tokenization 2. Stop words removal 3. Stemming and Lemmatization 4. Named Entity Recognition 5. TF-IDF 6. Bag of Words

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#datascience

Want to become a Data Scientist? Here’s a quick roadmap with essential concepts: 1. Mathematics & Statistics Linear Algebra: Matrix operations, eigenvalues, eigenvectors, and decomposition, which are crucial for machine learning. Probability & Statistics: Hypothesis testing, probability distributions, Bayesian inference, confidence intervals, and statistical significance. Calculus: Derivatives, integrals, and gradients, especially partial derivatives, which are essential for understanding model optimization. 2. Programming Python or R: Choose a primary programming language for data science. Python: Libraries like NumPy, Pandas for data manipulation, and Scikit-Learn for machine learning. R: Especially popular in academia and finance, with libraries like dplyr and ggplot2 for data manipulation and visualization. SQL: Master querying and database management, essential for accessing, joining, and filtering large datasets. 3. Data Wrangling & Preprocessing Data Cleaning: Handle missing values, outliers, duplicates, and data formatting. Feature Engineering: Create meaningful features, handle categorical variables, and apply transformations (scaling, encoding, etc.). Exploratory Data Analysis (EDA): Visualize data distributions, correlations, and trends to generate hypotheses and insights. 4. Data Visualization Python Libraries: Use Matplotlib, Seaborn, and Plotly to visualize data. Tableau or Power BI: Learn interactive visualization tools for building dashboards. Storytelling: Develop skills to interpret and present data in a meaningful way to stakeholders. 5. Machine Learning Supervised Learning: Understand algorithms like Linear Regression, Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, and Support Vector Machines (SVM). Unsupervised Learning: Study clustering (K-means, DBSCAN) and dimensionality reduction (PCA, t-SNE). Evaluation Metrics: Understand accuracy, precision, recall, F1-score for classification and RMSE, MAE for regression. 6. Advanced Machine Learning & Deep Learning Neural Networks: Understand the basics of neural networks and backpropagation. Deep Learning: Get familiar with Convolutional Neural Networks (CNNs) for image processing and Recurrent Neural Networks (RNNs) for sequential data. Transfer Learning: Apply pre-trained models for specific use cases. Frameworks: Use TensorFlow Keras for building deep learning models. 7. Natural Language Processing (NLP) Text Preprocessing: Tokenization, stemming, lemmatization, stop-word removal. NLP Techniques: Understand bag-of-words, TF-IDF, and word embeddings (Word2Vec, GloVe). NLP Models: Work with recurrent neural networks (RNNs), transformers (BERT, GPT) for text classification, sentiment analysis, and translation. 8. Big Data Tools (Optional) Distributed Data Processing: Learn Hadoop and Spark for handling large datasets. Use Google BigQuery for big data storage and processing. 9. Data Science Workflows & Pipelines (Optional) ETL & Data Pipelines: Extract, Transform, and Load data using tools like Apache Airflow for automation. Set up reproducible workflows for data transformation, modeling, and monitoring. Model Deployment: Deploy models in production using Flask, FastAPI, or cloud services (AWS SageMaker, Google AI Platform). 10. Model Validation & Tuning Cross-Validation: Techniques like K-fold cross-validation to avoid overfitting. Hyperparameter Tuning: Use Grid Search, Random Search, and Bayesian Optimization to optimize model performance. Bias-Variance Trade-off: Understand how to balance bias and variance in models for better generalization. 11. Time Series Analysis Statistical Models: ARIMA, SARIMA, and Holt-Winters for time-series forecasting. Time Series: Handle seasonality, trends, and lags. Use LSTMs or Prophet for more advanced time-series forecasting. 12. Experimentation & A/B Testing Experiment Design: Learn how to set up and analyze controlled experiments. A/B Testing: Statistical techniques for comparing groups & measuring the impact of changes. ENJOY LEARNING 👍👍

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5 Key SQL Aggregate Functions for data analyst 🍞SUM(): Adds up all the values in a numeric column. 🍞AVG(): Calculates the average of a numeric column. 🍞COUNT(): Counts the total number of rows or non-NULL values in a column. 🍞MAX(): Returns the highest value in a column. 🍞MIN(): Returns the lowest value in a column.

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Top 20 #SQL INTERVIEW QUESTIONS 1️⃣ Explain Order of Execution of SQL query 2️⃣ Provide a use case for each of the functions Rank, Dense_Rank & Row_Number ( 💡 majority struggle ) 3️⃣ Write a query to find the cumulative sum/Running Total 4️⃣ Find the Most selling product by sales/ highest Salary of employees 5️⃣ Write a query to find the 2nd/nth highest Salary of employees 6️⃣ Difference between union vs union all 7️⃣ Identify if there any duplicates in a table 8️⃣ Scenario based Joins question, understanding of Inner, Left and Outer Joins via simple yet tricky question 9️⃣ LAG, write a query to find all those records where the transaction value is greater then previous transaction value 1️⃣ 0️⃣ Rank vs Dense Rank, query to find the 2nd highest Salary of employee ( Ideal soln should handle ties) 1️⃣ 1️⃣ Write a query to find the Running Difference (Ideal sol'n using windows function) 1️⃣ 2️⃣ Write a query to display year on year/month on month growth 1️⃣ 3️⃣ Write a query to find rolling average of daily sign-ups 1️⃣ 4️⃣ Write a query to find the running difference using self join (helps in understanding the logical approach, ideally this question is solved via windows function) 1️⃣ 5️⃣ Write a query to find the cumulative sum using self join (you can use windows function to solve this question) 1️⃣6️⃣ Differentiate between a clustered index and a non-clustered index? 1️⃣7️⃣ What is a Candidate key? 1️⃣8️⃣What is difference between Primary key and Unique key? 1️⃣9️⃣What's the difference between RANK & DENSE_RANK in SQL? 2️⃣0️⃣ Whats the difference between LAG & LEAD in SQL?

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Roadmap to become Data Scientist

𝗛𝗼𝘄 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗦𝗤𝗟 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 (𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗚𝗲𝘁𝘁𝗶𝗻𝗴 𝗢𝘃𝗲𝗿𝘄𝗵𝗲𝗹𝗺𝗲𝗱!)🧠 Let’s be honest: SQL seems simple… until JOINs, Subqueries, and Window Functions come crashing in. But mastering SQL doesn’t have to be hard. You just need the right roadmap—and that’s exactly what this is. Here’s a 5-step SQL journey to go from beginner to job-ready analyst👇 🔹 𝗦𝘁𝗲𝗽 𝟭: Nail the Basics (Learn to Think in SQL) Start with the foundations: ✅ SELECT, WHERE, ORDER BY ✅ DISTINCT, LIMIT, BETWEEN, LIKE ✅ COUNT, SUM, AVG, MIN, MAX Practice with small tables to build confidence. Use platforms like: ➡️ W3Schools ➡️ Modesql ➡️ LeetCode (easy problems) 🔹 𝗦𝘁𝗲𝗽 𝟮: Understand GROUP BY and Aggregations (The Analyst’s Superpower) This is where real-world queries begin. Learn: ✅ GROUP BY + HAVING ✅ Combining GROUP BY with COUNT/AVG ✅ Filtering aggregated data Example: "Find top 5 cities with the highest total sales in 2023" That’s GROUP BY magic. 🔹 𝗦𝘁𝗲𝗽 𝟯: MASTER JOINS (Stop Getting Confused) JOINS scare a lot of people. But they’re just pattern-matching across tables. Learn one by one: ✅ INNER JOIN ✅ LEFT JOIN ✅ RIGHT JOIN ✅ FULL OUTER JOIN ✅ SELF JOIN ✅ CROSS JOIN (rare, but good to know) Visualize them using Venn diagrams or draw sample tables—it helps! 🔹 𝗦𝘁𝗲𝗽 𝟰: Learn Subqueries and CTEs (Write Cleaner, Powerful SQL) ✅ Subqueries: Query inside another query ✅ CTEs (WITH clause): Cleaner and reusable queries ✅ Use them to break down complex problems CTEs = the secret sauce to writing queries recruiters love. 🔹 𝗦𝘁𝗲𝗽 𝟱: Level Up with Window Functions (Your Entry into Advanced SQL) If you want to stand out, this is it: ✅ ROW_NUMBER(), RANK(), DENSE_RANK() ✅ LAG(), LEAD(), NTILE() ✅ PARTITION BY and ORDER BY combo Use these to: ➡️ Find top N per group ➡️ Track user behavior over time ➡️ Do cohort analysis You don’t need 100 LeetCode problems. You need 10 real-world queries done deeply. Keep it simple. Keep it useful.