7 Free Kaggle Micro-Courses for Data Science Beginners with Certification

Python

https://www.kaggle.com/learn/python

Pandas

https://www.kaggle.com/learn/pandas

Data visualization

https://www.kaggle.com/learn/data-visualization

Intro to sql

https://www.kaggle.com/learn/intro-to-sql

Advanced Sql

https://www.kaggle.com/learn/advanced-sql

Intro to ML

https://www.kaggle.com/learn/intro-to-machine-learning

Advanced ML

https://www.kaggle.com/learn/intermediate-machine-learning

#datascienceprojects #kaggle

SQL ASSIGNMENT

#Check your fundamental knowledge

A-Z of essential data science concepts

A: Algorithm - A set of rules or instructions for solving a problem or completing a task.
B: Big Data - Large and complex datasets that traditional data processing applications are unable to handle efficiently.
C: Classification - A type of machine learning task that involves assigning labels to instances based on their characteristics.
D: Data Mining - The process of discovering patterns and extracting useful information from large datasets.
E: Ensemble Learning - A machine learning technique that combines multiple models to improve predictive performance.
F: Feature Engineering - The process of selecting, extracting, and transforming features from raw data to improve model performance.
G: Gradient Descent - An optimization algorithm used to minimize the error of a model by adjusting its parameters iteratively.
H: Hypothesis Testing - A statistical method used to make inferences about a population based on sample data.
I: Imputation - The process of replacing missing values in a dataset with estimated values.
J: Joint Probability - The probability of the intersection of two or more events occurring simultaneously.
K: K-Means Clustering - A popular unsupervised machine learning algorithm used for clustering data points into groups.
L: Logistic Regression - A statistical model used for binary classification tasks.
M: Machine Learning - A subset of artificial intelligence that enables systems to learn from data and improve performance over time.
N: Neural Network - A computer system inspired by the structure of the human brain, used for various machine learning tasks.
O: Outlier Detection - The process of identifying observations in a dataset that significantly deviate from the rest of the data points.
P: Precision and Recall - Evaluation metrics used to assess the performance of classification models.
Q: Quantitative Analysis - The process of using mathematical and statistical methods to analyze and interpret data.
R: Regression Analysis - A statistical technique used to model the relationship between a dependent variable and one or more independent variables.
S: Support Vector Machine - A supervised machine learning algorithm used for classification and regression tasks.
T: Time Series Analysis - The study of data collected over time to detect patterns, trends, and seasonal variations.
U: Unsupervised Learning - Machine learning techniques used to identify patterns and relationships in data without labeled outcomes.
V: Validation - The process of assessing the performance and generalization of a machine learning model using independent datasets.
W: Weka - A popular open-source software tool used for data mining and machine learning tasks.
X: XGBoost - An optimized implementation of gradient boosting that is widely used for classification and regression tasks.
Y: Yarn - A resource manager used in Apache Hadoop for managing resources across distributed clusters.
Z: Zero-Inflated Model - A statistical model used to analyze data with excess zeros, commonly found in count data.

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

Credits: https://t.iss.one/datasciencefun

Like if you need similar content 😄👍

Hope this helps you 😊

⌨️ Python Libraries For Data Science

5 POWERFUL Tips to Learn Python Faster: https://medium.com/@data_analyst/5-powerful-tips-to-learn-python-faster-still-works-in-2025-7cc22c032581?sk=ee41c4ef166475eb9c3f2191aba3204d

Python from scratch
by University of Waterloo

0. Introduction
1. First steps
2. Built-in functions
3. Storing and using information
4. Creating functions
5. Booleans
6. Branching
7. Building better programs
8. Iteration using while
9. Storing elements in a sequence
10. Iteration using for
11. Bundling information into objects
12. Structuring data
13. Recursion

https://open.cs.uwaterloo.ca/python-from-scratch/

#python

Agree?

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
👇👇
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.