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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Top 10 Platforms to Practice SQL in 2025

Nitika Sharma
Nitika Sharma Last Updated : 04 Dec, 2024
5 min read

Introduction

SQL remains the cornerstone of data manipulation and analysis in the digital age. Whether you’re a budding data analyst, a seasoned database administrator, or a developer looking to refine your querying abilities, practicing SQL is pivotal. But where does one turn to refine their SQL skills? Fear not, for we’ve scoured the web to bring you the ultimate list of platforms where you can practice SQL until your queries, subqueries, and advanced SQL techniques are as sharp as Occam’s razor. Learn SQL on these platforms and master the art of writing efficient select statements, aggregate functions, and other complex SQL queries. So In this Article, we are talking about Platforms to Practice SQL. Top 10 Platforms where you can practice sql.Also in this article the platforms for SQL practice are best platforms that can help you in future also.

Table of contents

10 Best Platforms to Practice SQL

Top 10 Platforms to Practice SQL

Becoming a SQL master involves purposeful practice. Our selected platforms provide structured challenges, real-world scenarios, and instant feedback to advance your skills from beginner to expert.

Codecademy

Codecademy has long been a beacon for learners of all things code. Their interactive SQL course lets you write and run queries directly in the browser. With a hands-on approach and a progress tracker, you’ll be slicing through data sets with precision in no time.

Projects are an essential part of learning a new concept. Here are 10 amazing SQL projects for you to test your knowledge.

Analytics Vidhya

Analytics Vidhya’s SQL course is meticulously crafted to guide you through the intricacies of SQL. With a mix of video lessons and interactive exercises, we ensure that you’re not just practicing but also understanding the ‘why’ behind each query.

SQLZoo

SQLZoo is like a playground for SQL enthusiasts. It offers a variety of exercises that range from basic to advanced, all while keeping a playful tone. The immediate feedback on queries ensures a quick learning curve, and the diverse set of examples keeps the experience fresh.

LeetCode

With a vast array of SQL challenges that mirror technical interview questions, LeetCode is the perfect place to prepare for your next career leap. The community discussions and solutions provide a collaborative environment to learn and grow.

HackerRank

HackerRank offers a competitive edge to SQL practice. With its leaderboard and contests, you’re not just learning but vying for supremacy. The platform’s structured approach to problems helps build a strong foundation in SQL concepts.

Also Read: SQL For Data Science: A Beginner Guide!

Mode Analytics

Mode Analytics offers a unique twist by offering a SQL tutorial using real-world data. This approach gives you a taste of what data analysts deal with daily, making it an invaluable resource for those looking to apply their skills professionally.

StrataScratch

StrataScratch offers SQL problems sourced from real companies. This platform bridges practice and application, providing a glimpse into the types of SQL challenges you might face in a tech-driven workplace.

Data Lemur

Data Lemur is a famous online platform that offers a huge range of SQL coding demanding situations and interview questions. It caters to both novices and skilled builders, providing a established studying enjoy to enhance SQL talent. The platform capabilities real-global records scenarios, allowing users to practice writing efficient queries and gain arms-on enjoy in records analysis.

Exercism

Exercism is a community-driven platform that offers coding sporting events and exercise problems throughout a couple of programming languages, which includes SQL. The SQL song on Exercism makes a speciality of essential SQL concepts, along with querying, filtering, becoming a member of tables, and working with aggregations.

SQL Pad

SQL Pad is an interactive SQL gaining knowledge of platform that provides a consumer-friendly interface for writing and executing queries. It features a range of activities, tutorials, and coding demanding situations to help customers master SQL basics and advanced strategies. The platform also helps multiple database engines, allowing inexperienced persons to practice on exceptional SQL dialects and prepare for real-world eventualities.

How to Learn SQL in a Week Can I learn SQL in a week?

  1. Grasp the core concepts: Focus on understanding SQL Server, databases, tables, and data types.
  2. Master the basic SQL syntax and commands: Get comfortable with SELECT (to retrieve data), INSERT (to add data), UPDATE (to modify data), and DELETE (to remove data).
  3. Practice, Practice, Practice: Find resources that provide SQL exercises and hands-on practice with SQL queries, SQL code, and SQL joins.
  4. Dive into data analytics: Use SQL for data analysis and visualization.
  5. Explore advanced features: Learn about window functions and other advanced SQL functionalities.
  6. Apply machine learning: Understand how SQL can be used in machine learning contexts.

Here are some helpful resources:

  • Interactive tutorials: Websites like [LearnSQL.com] offer interactive tutorials that introduce you to SQL through practical exercises.
  • Online Courses: Platforms like Coursera, Udemy, or Khan Academy have beginner-friendly SQL courses you can complete in a week.
  • Practice databases: Many platforms offer free access to sample SQL databases you can use to experiment with your SQL queries.

Remember, consistency is key. Dedicate a few hours each day to learning and practicing basic SQL. While becoming a complete SQL master might take longer, a week is enough to establish a solid foundation in SQL database management and gain valuable skills in data analysis.

Conclusion

The path to SQL mastery is a personal journey that requires dedication, curiosity, and the right set of tools. The platforms listed above are more than just practice grounds; they are gateways to a world where data yields to your command. Whether you’re looking for interactive learning, real-world application, or competitive challenges, there’s a platform tailored to your learning style. So choose your arena, sharpen your mind, and let the queries begin. So their is Number of Platforms where you can sql whether it is sql commands , sql editor , sql learning , sql select or any structured query language of Microsoft.Basically you will get clear understanding on which Platforms to practice sql.

Enroll today for our SQL course and master the complex concepts in no time!

Frequently Asked Questions

Q1. Which site is best for SQL practice?

A. SQLZoo and LeetCode are recommended for SQL practice, providing interactive challenges and real-world scenarios to enhance skills.

Q2. What is the best way to practice SQL?

A. Practice SQL effectively by engaging in real projects, solving problems on platforms like HackerRank, and building databases for hands-on experience.

Q3. What is the best platform to practice SQL?

A. LeetCode: Extensive SQL challenges.
HackerRank: Wide range of SQL problems.
SQLZoo: Interactive tutorials and exercises.
Mode Analytics SQL Tutorial: Practical exercises with an interactive environment.
W3Schools: Comprehensive tutorials with an SQL editor.

Q4. Which site is best for SQL course?

A. For structured learning, Udemy and Coursera offer comprehensive SQL courses with hands-on projects, facilitating skill development and in-depth understanding.

Nitika Sharma
Nitika Sharma

Hello, I am Nitika, a tech-savvy Content Creator and Marketer. Creativity and learning new things come naturally to me. I have expertise in creating result-driven content strategies. I am well versed in SEO Management, Keyword Operations, Web Content Writing, Communication, Content Strategy, Editing, and Writing.

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