Which career impresses you the most?

Hard Pill To Swallow: 💊

Robots aren’t stealing your future - they’re taking the boring jobs. 

Meanwhile:

- Some YouTuber made six figures sharing what she loves. 
- A teen's random app idea just got funded.
- My friend quit banking to teach coding - he's killing it.

Here’s the thing:

Hard work still matters. But the rules of the game have changed. 

The real money is in solving problems, spreading ideas, and building cool stuff.

Call it evolution. Call it disruption. Whatever.

Crying about the old world won't help you thrive in the new one.

Create something.✨

#ai

Building a project will teach you more than watching a tutorial ever will.

Agree?

What's your plan?

Basic SQL commands and aggregate functions: 👇
https://t.iss.one/mysqldata/32

In a data science project, using multiple scalers can be beneficial when dealing with features that have different scales or distributions. Scaling is important in machine learning to ensure that all features contribute equally to the model training process and to prevent certain features from dominating others.

Here are some scenarios where using multiple scalers can be helpful in a data science project:

1. Standardization vs. Normalization: Standardization (scaling features to have a mean of 0 and a standard deviation of 1) and normalization (scaling features to a range between 0 and 1) are two common scaling techniques. Depending on the distribution of your data, you may choose to apply different scalers to different features.

2. RobustScaler vs. MinMaxScaler: RobustScaler is a good choice when dealing with outliers, as it scales the data based on percentiles rather than the mean and standard deviation. MinMaxScaler, on the other hand, scales the data to a specific range. Using both scalers can be beneficial when dealing with mixed types of data.

3. Feature engineering: In feature engineering, you may create new features that have different scales than the original features. In such cases, applying different scalers to different sets of features can help maintain consistency in the scaling process.

4. Pipeline flexibility: By using multiple scalers within a preprocessing pipeline, you can experiment with different scaling techniques and easily switch between them to see which one works best for your data.

5. Domain-specific considerations: Certain domains may require specific scaling techniques based on the nature of the data. For example, in image processing tasks, pixel values are often scaled differently than numerical features.

When using multiple scalers in a data science project, it's important to evaluate the impact of scaling on the model performance through cross-validation or other evaluation methods. Try experimenting with different scaling techniques to you find the optimal approach for your specific dataset and machine learning model.

Join our WhatsApp channel for Data Science Free Resources
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https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y

Here is a list of 50 data science interview questions that can help you prepare for a data science job interview. These questions cover a wide range of topics and levels of difficulty, so be sure to review them thoroughly and practice your answers.

Mathematics and Statistics:

1. What is the Central Limit Theorem, and why is it important in statistics?
2. Explain the difference between population and sample.
3. What is probability and how is it calculated?
4. What are the measures of central tendency, and when would you use each one?
5. Define variance and standard deviation.
6. What is the significance of hypothesis testing in data science?
7. Explain the p-value and its significance in hypothesis testing.
8. What is a normal distribution, and why is it important in statistics?
9. Describe the differences between a Z-score and a T-score.
10. What is correlation, and how is it measured?
11. What is the difference between covariance and correlation?
12. What is the law of large numbers?

Machine Learning:

13. What is machine learning, and how is it different from traditional programming?
14. Explain the bias-variance trade-off.
15. What are the different types of machine learning algorithms?
16. What is overfitting, and how can you prevent it?
17. Describe the k-fold cross-validation technique.
18. What is regularization, and why is it important in machine learning?
19. Explain the concept of feature engineering.
20. What is gradient descent, and how does it work in machine learning?
21. What is a decision tree, and how does it work?
22. What are ensemble methods in machine learning, and provide examples.
23. Explain the difference between supervised and unsupervised learning.
24. What is deep learning, and how does it differ from traditional neural networks?
25. What is a convolutional neural network (CNN), and where is it commonly used?
26. What is a recurrent neural network (RNN), and where is it commonly used?
27. What is the vanishing gradient problem in deep learning?
28. Describe the concept of transfer learning in deep learning.

Data Preprocessing:

29. What is data preprocessing, and why is it important in data science?
30. Explain missing data imputation techniques.
31. What is one-hot encoding, and when is it used?
32. How do you handle categorical data in machine learning?
33. Describe the process of data normalization and standardization.
34. What is feature scaling, and why is it necessary?
35. What is outlier detection, and how can you identify outliers in a dataset?

Data Exploration:

36. What is exploratory data analysis (EDA), and why is it important?
37. Explain the concept of data distribution.
38. What are box plots, and how are they used in EDA?
39. What is a histogram, and what insights can you gain from it?
40. Describe the concept of data skewness.
41. What are scatter plots, and how are they useful in data analysis?
42. What is a correlation matrix, and how is it used in EDA?
43. How do you handle imbalanced datasets in machine learning?

Model Evaluation:

44. What are the common metrics used for evaluating classification models?
45. Explain precision, recall, and F1-score.
46. What is ROC curve analysis, and what does it measure?
47. How do you choose the appropriate evaluation metric for a regression problem?
48. Describe the concept of confusion matrix.
49. What is cross-entropy loss, and how is it used in classification problems?
50. Explain the concept of AUC-ROC.

x = [1, 2, 3]
y = (4, 5, 6)
z = x + list(y)
print(z)

Comment below the correct answer 👇

Python Tip for the day:
Use the "enumerate" function to iterate over a sequence and get the index of each element.

Sometimes when you're iterating over a list or other sequence in Python, you need to keep track of the index of the current element. One way to do this is to use a counter variable and increment it on each iteration, but this can be tedious and error-prone.

A better way to get the index of each element is to use the built-in "enumerate" function. The "enumerate" function takes an iterable (such as a list or tuple) as its argument and returns a sequence of (index, value) tuples, where "index" is the index of the current element and "value" is the value of the current element. Here's an example:

 Iterate over a list of strings and print each string with its index
strings = ['apple', 'banana', 'cherry', 'date']
for i, s in enumerate(strings):
    print(f"{i}: {s}")

In this example, we use the "enumerate" function to iterate over a list of strings. On each iteration, the "enumerate" function returns a tuple containing the index of the current string and the string itself. We use tuple unpacking to assign these values to the variables "i" and "s", and then print out the index and string on a separate line.

The output of this code would be:

 apple
1: banana
2: cherry
3: date

Using the "enumerate" function can make your code more concise and easier to read, especially when you need to keep track of the index of each element in a sequence.

Python Projects With API.pdf

AI Journey 2024: Glimpse into AI-Driven Future

The AI Journey International Conference on Artificial Intelligence and Machine Learning will once again bring together developers, scientists, and AI enthusiasts. With 200+ speakers from more than ten countries, including China, India, UAE, Indonesia, and Iran, the conference will glimpse an AI-enriched future.

AI Journey will be held in Moscow on December 11–13, with each day highlighting a different track: Society, Business, and Science.

On December 11, the focus will be on Society, where BRICS experts, business, and government representatives will discuss the key role of technologies and AI as a means to address social issues. Attendees will gain insights into various AI-related success stories and how AI supports the sustainable development of the planet.

December 12 will be dedicated to Business. This track will feature leading experts such as Jaspreet Bindra, Dr. Aisha Bint Butti Bin Bishr, Janet Sawari, Karuna Gopal , and Hammam Riza, who will elaborate on real-world implementation of AI in business, and how business and industry can benefit from it.

December 13 will be all about Science. Sessions will feature international researchers sharing insights into the latest AI technology and the AI’s impact on research and science in general. Swagatam Das, Vladimir Spokoiny, Dedi Darwis, Gonzalo Ferrer, and other international experts will delve into the latest scientific advances ranging from generative models and quantum technologies to cybersecurity, educational tools, and medicine. Speakers from Sber, Moscow Institute of Physics and Technology, Innopolis University, and others will share how AI is transforming learning, development, reading, and art in everyday life. The Science Day will also immerse all AI newbies in the world of artificial intelligence with a special AIJ Junior track.

The AI Journey will host the awards ceremony for the finalists of the AI Challenge for young data scientists and the AIJ Contest for experienced AI professionals.

Join the live broadcast. Be up to date with the top AI news!

What are you learning?