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TensorFlow Cheat Sheet for Deep Learning

Essential Scikit-Learn Methods for Machine Learning

Data Visualization Revision Notes Matplotlib + Seaborn

Complete Pandas Revision Notes for Data Science Internships

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🚀 Database Scaling: Partitioning vs Sharding 1. Partitioning (Same Server) 🧩 Splits a table into sub-tables within one database instance. ➤   How: divides data by rules like ID ranges or dates. ➤   Wins: Faster queries (scans less data) and better security control. ➤   Risk: Complicates backups and recovery. 2. Sharding (Multi-Server) 🌐 Distributes data across multiple independent servers (horizontal scaling). ➤   How: Uses a "shard key" (like geographic region) to route data. ➤   Wins: Massive storage scale, higher throughput, and isolated outages. ➤   Risk: High complexity in management and querying. 💡 Key Difference: Partitioning organizes data on one machine; Sharding spreads it across many.

Data Science Techniques

What is the difference between data scientist, data engineer, data analyst and business intelligence? 🧑🔬 Data Scientist Focus: Using data to build models, make predictions, and solve complex problems. Cleans and analyzes data Builds machine learning models Answers “Why is this happening?” and “What will happen next?” Works with statistics, algorithms, and coding (Python, R) Example: Predict which customers are likely to cancel next month 🛠️ Data Engineer Focus: Building and maintaining the systems that move and store data. Designs and builds data pipelines (ETL/ELT) Manages databases, data lakes, and warehouses Ensures data is clean, reliable, and ready for others to use Uses tools like SQL, Airflow, Spark, and cloud platforms (AWS, Azure, GCP) Example: Create a system that collects app data every hour and stores it in a warehouse 📊 Data Analyst Focus: Exploring data and finding insights to answer business questions. Pulls and visualizes data (dashboards, reports) Answers “What happened?” or “What’s going on right now?” Works with SQL, Excel, and tools like Tableau or Power BI Less coding and modeling than a data scientist Example: Analyze monthly sales and show trends by region 📈 Business Intelligence (BI) Professional Focus: Helping teams and leadership understand data through reports and dashboards. Designs dashboards and KPIs (key performance indicators) Translates data into stories for non-technical users Often overlaps with data analyst role but more focused on reporting Tools: Power BI, Looker, Tableau, Qlik Example: Build a dashboard showing company performance by department 🧩 Summary Table Data Scientist - What will happen? Tools: Python, R, ML tools, predictions & models Data Engineer - How does the data move and get stored? Tools: SQL, Spark, cloud tools, infrastructure & pipelines Data Analyst - What happened? Tools: SQL, Excel, BI tools, reports & exploration BI Professional - How can we see business performance clearly? Tools: Power BI, Tableau, dashboards & insights for decision-makers 🎯 In short: Data Engineers build the roads. Data Scientists drive smart cars to predict traffic. Data Analysts look at traffic data to see patterns. BI Professionals show everyone the traffic report on a screen.

Python book for machine learning

Data Science Roadmap | |-- Fundamentals | |-- Mathematics | | |-- Linear Algebra | | |-- Calculus | | |-- Probability and Statistics | | | |-- Programming | | |-- Python | | |-- R | | |-- SQL | |-- Data Collection and Cleaning | |-- Data Sources | | |-- APIs | | |-- Web Scraping | | |-- Databases | | | |-- Data Cleaning | | |-- Missing Values | | |-- Data Transformation | | |-- Data Normalization | |-- Data Analysis | |-- Exploratory Data Analysis (EDA) | | |-- Descriptive Statistics | | |-- Data Visualization | | |-- Hypothesis Testing | | | |-- Data Wrangling | | |-- Pandas | | |-- NumPy | | |-- dplyr (R) | |-- Machine Learning | |-- Supervised Learning | | |-- Regression | | |-- Classification | | | |-- Unsupervised Learning | | |-- Clustering | | |-- Dimensionality Reduction | | | |-- Reinforcement Learning | | |-- Q-Learning | | |-- Policy Gradient Methods | | | |-- Model Evaluation | | |-- Cross-Validation | | |-- Performance Metrics | | |-- Hyperparameter Tuning | |-- Deep Learning | |-- Neural Networks | | |-- Feedforward Networks | | |-- Backpropagation | | | |-- Advanced Architectures | | |-- Convolutional Neural Networks (CNN) | | |-- Recurrent Neural Networks (RNN) | | |-- Transformers | | | |-- Tools and Frameworks | | |-- TensorFlow | | |-- PyTorch | |-- Natural Language Processing (NLP) | |-- Text Preprocessing | | |-- Tokenization | | |-- Stop Words Removal | | |-- Stemming and Lemmatization | | | |-- NLP Techniques | | |-- Word Embeddings | | |-- Sentiment Analysis | | |-- Named Entity Recognition (NER) | |-- Data Visualization | |-- Basic Plotting | | |-- Matplotlib | | |-- Seaborn | | |-- ggplot2 (R) | | | |-- Interactive Visualization | | |-- Plotly | | |-- Bokeh | | |-- Dash | |-- Big Data | |-- Tools and Frameworks | | |-- Hadoop | | |-- Spark | | | |-- NoSQL Databases | |-- MongoDB | |-- Cassandra | |-- Cloud Computing | |-- Cloud Platforms | | |-- AWS | | |-- Google Cloud | | |-- Azure | | | |-- Data Services | |-- Data Storage (S3, Google Cloud Storage) | |-- Data Pipelines (Dataflow, AWS Data Pipeline) | |-- Model Deployment | |-- Serving Models | | |-- Flask/Django | | |-- FastAPI | | | |-- Model Monitoring | |-- Performance Tracking | |-- A/B Testing | |-- Domain Knowledge | |-- Industry-Specific Applications | | |-- Finance | | |-- Healthcare | | |-- Retail | |-- Ethical and Responsible AI | |-- Bias and Fairness | |-- Privacy and Security | |-- Interpretability and Explainability | |-- Communication and Storytelling | |-- Reporting | |-- Dashboarding | |-- Presentation Skills | |-- Advanced Topics | |-- Time Series Analysis | |-- Anomaly Detection | |-- Graph Analytics | |-- *PH4N745M* └-- Comments |-- # Single-line comment (Python) └-- /* Multi-line comment (Python/R) */

✅ Data Science Fundamental Concepts You Should Know 📊🧠 1️⃣ Data Collection Gathering raw data from various sources like databases, APIs, or web scraping for analysis. 2️⃣ Data Cleaning & Preprocessing Preparing data by handling missing values, removing duplicates, correcting errors, and formatting for analysis. 3️⃣ Exploratory Data Analysis (EDA) Using statistics and visualization to understand data patterns, trends, and detect outliers. 4️⃣ Statistical Inference Drawing conclusions about populations using sample data through hypothesis testing, confidence intervals, and p-values. 5️⃣ Data Visualization Creating charts and graphs (bar, line, scatter, histograms) to communicate insights clearly using tools like Matplotlib, Seaborn, or Tableau. 6️⃣ Feature Engineering Transforming raw data into meaningful features that improve model performance, such as scaling, encoding and creating new variables. 7️⃣ Machine Learning Basics Building predictive models by training algorithms on data: ⦁ Supervised Learning (regression, classification) ⦁ Unsupervised Learning (clustering, dimensionality reduction) 8️⃣ Model Evaluation Assessing model accuracy using metrics like accuracy, precision, recall, F1 score (classification) and RMSE, MAE (regression). 9️⃣ Model Deployment Putting your trained model into production so it can make real-time predictions or support decision-making. 🔟 Big Data & Tools Handling large datasets using technologies like Hadoop, Spark, and databases such as SQL/NoSQL. 1️⃣1️⃣ Programming & Libraries Essential coding skills in Python or R, with libraries like Pandas, NumPy, Scikit-learn for analysis and modeling. 1️⃣2️⃣ Data Ethics & Privacy Ensuring responsible use of data, respecting privacy laws (GDPR), and avoiding biases in models. 💡 Tap ❤️ for more!

✅10 Most Useful SQL Interview Queries (with Examples) 💼 1️⃣ Find the second highest salary: SELECT MAX(salary)  FROM employees  WHERE salary < (SELECT MAX(salary) FROM employees); 2️⃣ Count employees in each department: SELECT department, COUNT(*)  FROM employees  GROUP BY department; 3️⃣ Fetch duplicate emails: SELECT email, COUNT(*)  FROM users  GROUP BY email  HAVING COUNT(*) > 1; 4️⃣ Join orders with customer names: SELECT c.name, o.order_date  FROM customers c  JOIN orders o ON c.id = o.customer_id; 5️⃣ Get top 3 highest salaries: SELECT DISTINCT salary  FROM employees  ORDER BY salary DESC  LIMIT 3; 6️⃣ Retrieve latest 5 logins: SELECT * FROM logins  ORDER BY login_time DESC  LIMIT 5; 7️⃣ Employees with no manager: SELECT name  FROM employees  WHERE manager_id IS NULL; 8️⃣ Search names starting with ‘S’: SELECT * FROM employees  WHERE name LIKE 'S%'; 9️⃣ Total sales per month: SELECT MONTH(order_date) AS month, SUM(amount)  FROM sales  GROUP BY MONTH(order_date); 🔟 Delete inactive users: DELETE FROM users  WHERE last_active < '2023-01-01'; ✅ Tip: Master subqueries, joins, groupings & filters – they show up in nearly every interview! 💬 Tap ❤️ for more!

Ever wondered what the difference is between a Data Analyst and a Data Scientist? Both roles are in high demand, but they tackle data in different ways.