𝗔𝗿𝗲 𝗬𝗼𝘂 𝗦𝗸𝗶𝗽𝗽𝗶𝗻𝗴 𝗧𝗵𝗶𝘀 𝗜𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝗦𝘁𝗲𝗽 𝗪𝗵𝗲𝗻 𝗪𝗿𝗶𝘁𝗶𝗻𝗴 𝗦𝗤𝗟 𝗤𝘂𝗲𝗿𝗶𝗲𝘀?

𝗧𝗵𝗶𝗻𝗸 𝘆𝗼𝘂𝗿 𝗦𝗤𝗟 𝗾𝘂𝗲𝗿𝗶𝗲𝘀 𝗮𝗿𝗲 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁? 𝗬𝗼𝘂 𝗺𝗶𝗴𝗵𝘁 𝗯𝗲 𝘀𝗸𝗶𝗽𝗽𝗶𝗻𝗴 𝘁𝗵𝗶𝘀!

Hi everyone! Writing SQL queries can be tricky, especially if you forget to include one key part: indexing.

When I first started writing SQL queries, I didn’t pay much attention to indexing. My queries worked, but they took way longer to run.

Here’s why indexing is so important:

- 𝗪𝗵𝗮𝘁 𝗜𝘀 𝗜𝗻𝗱𝗲𝘅𝗶𝗻𝗴?: Indexing is like creating a shortcut for your database to find the data you need faster. Without it, your database might have to scan through all the data, making your queries slow.

- 𝗪𝗵𝘆 𝗜𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀: If your query takes too long, it can slow down your entire system. Adding the right indexes helps your queries run faster and more efficiently.

- 𝗛𝗼𝘄 𝘁𝗼 𝗨𝘀𝗲 𝗜𝗻𝗱𝗲𝘅𝗲𝘀: When you create a table, consider which columns are used often in WHERE clauses or JOIN conditions. Index those columns to speed up your queries.

Indexing is a simple step that can make a big difference in performance. Don’t skip it!


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5. File Handling 📁
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Hope you'll like it

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100 DBMS interview Questions

10 commonly asked data science interview questions along with their answers

1️⃣ What is the difference between supervised and unsupervised learning?
Supervised learning involves learning from labeled data to predict outcomes while unsupervised learning involves finding patterns in unlabeled data.

2️⃣ Explain the bias-variance tradeoff in machine learning.
The bias-variance tradeoff is a key concept in machine learning. Models with high bias have low complexity and over-simplify, while models with high variance are more complex and over-fit to the training data. The goal is to find the right balance between bias and variance.

3️⃣ What is the Central Limit Theorem and why is it important in statistics?
The Central Limit Theorem (CLT) states that the sampling distribution of the sample means will be approximately normally distributed regardless of the underlying population distribution, as long as the sample size is sufficiently large. It is important because it justifies the use of statistics, such as hypothesis testing and confidence intervals, on small sample sizes.

4️⃣ Describe the process of feature selection and why it is important in machine learning.
Feature selection is the process of selecting the most relevant features (variables) from a dataset. This is important because unnecessary features can lead to over-fitting, slower training times, and reduced accuracy.

5️⃣ What is the difference between overfitting and underfitting in machine learning? How do you address them?
Overfitting occurs when a model is too complex and fits the training data too well, resulting in poor performance on unseen data. Underfitting occurs when a model is too simple and cannot fit the training data well enough, resulting in poor performance on both training and unseen data. Techniques to address overfitting include regularization and early stopping, while techniques to address underfitting include using more complex models or increasing the amount of input data.

6️⃣ What is regularization and why is it used in machine learning?
Regularization is a technique used to prevent overfitting in machine learning. It involves adding a penalty term to the loss function to limit the complexity of the model, effectively reducing the impact of certain features.

7️⃣ How do you handle missing data in a dataset?
Handling missing data can be done by either deleting the missing samples, imputing the missing values, or using models that can handle missing data directly.

8️⃣ What is the difference between classification and regression in machine learning?
Classification is a type of supervised learning where the goal is to predict a categorical or discrete outcome, while regression is a type of supervised learning where the goal is to predict a continuous or numerical outcome.

9️⃣ Explain the concept of cross-validation and why it is used.
Cross-validation is a technique used to evaluate the performance of a machine learning model. It involves spliting the data into training and validation sets, and then training and evaluating the model on multiple such splits. Cross-validation gives a better idea of the model's generalization ability and helps prevent over-fitting.

🔟 What evaluation metrics would you use to evaluate a binary classification model?
Some commonly used evaluation metrics for binary classification models are accuracy, precision, recall, F1 score, and ROC-AUC. The choice of metric depends on the specific requirements of the problem.


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