Here are some essential AI terms that every data scientist should know:
* Machine Learning (ML): A subfield of AI that allows computers to learn without being explicitly programmed. ML algorithms learn from data to make predictions or decisions.
* Deep Learning (DL): A type of machine learning that uses artificial neural networks to model complex data. Deep learning models are inspired by the structure and function of the human brain.
* Natural Language Processing (NLP): A subfield of AI that deals with the interaction between computers and human language. NLP tasks include machine translation, sentiment analysis, and speech recognition.
* Computer Vision (CV): A subfield of AI that deals with the extraction of information from images and videos. CV tasks include object detection, image classification, and facial recognition.
* Big Data: Large and complex datasets that are difficult to store, process, and analyze using traditional methods. Big data often includes data from multiple sources and in various formats.
* Artificial Neural Network (ANN): A computational model inspired by the structure and function of the human brain. ANNs consist of interconnected nodes called neurons that can process information and learn from data.
* Algorithm: A set of instructions that a computer can follow to perform a specific task. In AI, algorithms are used to train machine learning models and to make predictions or decisions.
* Bias: A systematic preference for or against a particular outcome. Bias can be present in data, algorithms, and models. It's important to be aware of bias and to take steps to mitigate it.
* Explainability: The ability to understand how a machine learning model makes decisions. Explainable models are more trustworthy and easier to debug.
* Ethics: The branch of philosophy that deals with what is right and wrong. AI ethics is concerned with the development and use of AI in a responsible and ethical manner.
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* Machine Learning (ML): A subfield of AI that allows computers to learn without being explicitly programmed. ML algorithms learn from data to make predictions or decisions.
* Deep Learning (DL): A type of machine learning that uses artificial neural networks to model complex data. Deep learning models are inspired by the structure and function of the human brain.
* Natural Language Processing (NLP): A subfield of AI that deals with the interaction between computers and human language. NLP tasks include machine translation, sentiment analysis, and speech recognition.
* Computer Vision (CV): A subfield of AI that deals with the extraction of information from images and videos. CV tasks include object detection, image classification, and facial recognition.
* Big Data: Large and complex datasets that are difficult to store, process, and analyze using traditional methods. Big data often includes data from multiple sources and in various formats.
* Artificial Neural Network (ANN): A computational model inspired by the structure and function of the human brain. ANNs consist of interconnected nodes called neurons that can process information and learn from data.
* Algorithm: A set of instructions that a computer can follow to perform a specific task. In AI, algorithms are used to train machine learning models and to make predictions or decisions.
* Bias: A systematic preference for or against a particular outcome. Bias can be present in data, algorithms, and models. It's important to be aware of bias and to take steps to mitigate it.
* Explainability: The ability to understand how a machine learning model makes decisions. Explainable models are more trustworthy and easier to debug.
* Ethics: The branch of philosophy that deals with what is right and wrong. AI ethics is concerned with the development and use of AI in a responsible and ethical manner.
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Here are some essential machine learning algorithms that every data scientist should know:
* Linear Regression: This is a supervised learning algorithm that is used for continuous target variables. It finds a linear relationship between a dependent variable (y) and one or more independent variables (X). It's widely used for tasks like predicting house prices or stock prices.
* Logistic Regression: This is another supervised learning algorithm that is used for binary classification problems. It predicts the probability of an event happening based on independent variables. It's commonly used for tasks like spam email detection or credit card fraud detection.
* Decision Tree: This is a supervised learning algorithm that uses a tree-like model to classify data. It breaks down a decision into a series of smaller and simpler decisions. Decision trees are easily interpretable, making them a good choice for understanding how a model makes predictions.
* Support Vector Machine (SVM): This is a supervised learning algorithm that can be used for both classification and regression tasks. It finds a hyperplane that best separates the data points into different categories. SVMs are known for their good performance on high-dimensional data.
* K-Nearest Neighbors (KNN): This is a supervised learning algorithm that classifies data points based on the labels of their nearest neighbors. The number of neighbors (k) is a parameter that can be tuned to improve the performance of the algorithm. KNN is a simple and easy-to-understand algorithm, but it can be computationally expensive for large datasets.
* Random Forest: This is a supervised learning algorithm that is an ensemble of decision trees. Random forests are often more accurate and robust than single decision trees. They are also less prone to overfitting.
* Naive Bayes: This is a supervised learning algorithm that is based on Bayes' theorem. It assumes that the features are independent of each other, which is often not the case in real-world data. However, Naive Bayes can be a good choice for tasks where the features are indeed independent or when the computational cost is a major concern.
* K-Means Clustering: This is an unsupervised learning algorithm that is used to group data points into k clusters. The k clusters are chosen to minimize the within-cluster sum of squares (WCSS). K-means clustering is a simple and efficient algorithm, but it is sensitive to the initialization of the cluster centers.
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* Linear Regression: This is a supervised learning algorithm that is used for continuous target variables. It finds a linear relationship between a dependent variable (y) and one or more independent variables (X). It's widely used for tasks like predicting house prices or stock prices.
* Logistic Regression: This is another supervised learning algorithm that is used for binary classification problems. It predicts the probability of an event happening based on independent variables. It's commonly used for tasks like spam email detection or credit card fraud detection.
* Decision Tree: This is a supervised learning algorithm that uses a tree-like model to classify data. It breaks down a decision into a series of smaller and simpler decisions. Decision trees are easily interpretable, making them a good choice for understanding how a model makes predictions.
* Support Vector Machine (SVM): This is a supervised learning algorithm that can be used for both classification and regression tasks. It finds a hyperplane that best separates the data points into different categories. SVMs are known for their good performance on high-dimensional data.
* K-Nearest Neighbors (KNN): This is a supervised learning algorithm that classifies data points based on the labels of their nearest neighbors. The number of neighbors (k) is a parameter that can be tuned to improve the performance of the algorithm. KNN is a simple and easy-to-understand algorithm, but it can be computationally expensive for large datasets.
* Random Forest: This is a supervised learning algorithm that is an ensemble of decision trees. Random forests are often more accurate and robust than single decision trees. They are also less prone to overfitting.
* Naive Bayes: This is a supervised learning algorithm that is based on Bayes' theorem. It assumes that the features are independent of each other, which is often not the case in real-world data. However, Naive Bayes can be a good choice for tasks where the features are indeed independent or when the computational cost is a major concern.
* K-Means Clustering: This is an unsupervised learning algorithm that is used to group data points into k clusters. The k clusters are chosen to minimize the within-cluster sum of squares (WCSS). K-means clustering is a simple and efficient algorithm, but it is sensitive to the initialization of the cluster centers.
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How to piss off a Data Scientist in just 7 seconds:
☑ Peek at an AB experiment early, and insist that we can ship the feature now.
☑ Discard their analyses because it doesn’t agree with your gut feeling.
☑ Ask for data to support a conclusion that you’ve already made.
☑ Request an AI solution because “leadership wants one”.
☑ Argue that Data Science isn’t the sexiest career.
☑ Insist that they’re not real scientists.
☑ Peek at an AB experiment early, and insist that we can ship the feature now.
☑ Discard their analyses because it doesn’t agree with your gut feeling.
☑ Ask for data to support a conclusion that you’ve already made.
☑ Request an AI solution because “leadership wants one”.
☑ Argue that Data Science isn’t the sexiest career.
☑ Insist that they’re not real scientists.
🤣15👍5👌1
NLP Steps
1. Import Libraries:
NLP modules: Popular choices include NLTK and spaCy. These libraries offer functionalities for various NLP tasks like tokenization, stemming, and lemmatization.
2. Load the Dataset:
This involves loading the text data you want to analyze. This could be from a text file, CSV file, or even an API that provides textual data.
3. Text Preprocessing:
This is a crucial step that cleans and prepares the text data for further processing. Here's a breakdown of the sub-steps you mentioned:
Removing HTML Tags: This removes any HTML code embedded within the text, as it's not relevant for NLP tasks.
Removing Punctuations: Punctuations like commas, periods, etc., don't hold much meaning on their own. Removing them can improve the analysis.
Stemming (Optional): This reduces words to their base form (e.g., "running" becomes "run").
Expanding Contractions: This expands contractions like "don't" to "do not" for better understanding by the NLP system.
4. Tokenization:
This breaks down the text into individual units, typically words. It allows us to analyze the text one element at a time.
5. Stemming (Optional, can be done in Text Preprocessing):
As mentioned earlier, stemming reduces words to their base form.
6. Part-of-Speech (POS) Tagging:
This assigns a grammatical tag (e.g., noun, verb, adjective) to each word in the text. It helps understand the function of each word in the sentence.
7. Lemmatization:
Similar to stemming, lemmatization reduces words to their base form, but it considers the context and aims for a grammatically correct root word (e.g., "running" becomes "run").
8. Label Encoding (if applicable):
If your task involves classifying text data, you might need to convert textual labels (e.g., "positive," "negative") into numerical values for the model to understand.
9. Feature Extraction:
This step involves creating features from the preprocessed text data that can be used by machine learning models.
Bag-of-Words (BOW): Represents text as a histogram of word occurrences.
10. Text to Numerical Vector Conversion:
This converts the textual features into numerical vectors that machine learning models can understand. Here are some common techniques:
BOW (CountVectorizer): Creates a vector representing word frequencies.
TF-IDF Vectorizer: Similar to BOW but considers the importance of words based on their document and corpus frequency.
Word2Vec: This technique represents words as vectors based on their surrounding words, capturing semantic relationships.
GloVe: Another word embedding technique similar to Word2Vec, trained on a large text corpus.
11. Data Splitting:
The preprocessed data is often split into training, validation, and test sets.
12. Model Building:
This involves choosing and training an NLP model suitable for your task. Common NLP models include:
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1. Import Libraries:
NLP modules: Popular choices include NLTK and spaCy. These libraries offer functionalities for various NLP tasks like tokenization, stemming, and lemmatization.
2. Load the Dataset:
This involves loading the text data you want to analyze. This could be from a text file, CSV file, or even an API that provides textual data.
3. Text Preprocessing:
This is a crucial step that cleans and prepares the text data for further processing. Here's a breakdown of the sub-steps you mentioned:
Removing HTML Tags: This removes any HTML code embedded within the text, as it's not relevant for NLP tasks.
Removing Punctuations: Punctuations like commas, periods, etc., don't hold much meaning on their own. Removing them can improve the analysis.
Stemming (Optional): This reduces words to their base form (e.g., "running" becomes "run").
Expanding Contractions: This expands contractions like "don't" to "do not" for better understanding by the NLP system.
4. Tokenization:
This breaks down the text into individual units, typically words. It allows us to analyze the text one element at a time.
5. Stemming (Optional, can be done in Text Preprocessing):
As mentioned earlier, stemming reduces words to their base form.
6. Part-of-Speech (POS) Tagging:
This assigns a grammatical tag (e.g., noun, verb, adjective) to each word in the text. It helps understand the function of each word in the sentence.
7. Lemmatization:
Similar to stemming, lemmatization reduces words to their base form, but it considers the context and aims for a grammatically correct root word (e.g., "running" becomes "run").
8. Label Encoding (if applicable):
If your task involves classifying text data, you might need to convert textual labels (e.g., "positive," "negative") into numerical values for the model to understand.
9. Feature Extraction:
This step involves creating features from the preprocessed text data that can be used by machine learning models.
Bag-of-Words (BOW): Represents text as a histogram of word occurrences.
10. Text to Numerical Vector Conversion:
This converts the textual features into numerical vectors that machine learning models can understand. Here are some common techniques:
BOW (CountVectorizer): Creates a vector representing word frequencies.
TF-IDF Vectorizer: Similar to BOW but considers the importance of words based on their document and corpus frequency.
Word2Vec: This technique represents words as vectors based on their surrounding words, capturing semantic relationships.
GloVe: Another word embedding technique similar to Word2Vec, trained on a large text corpus.
11. Data Splitting:
The preprocessed data is often split into training, validation, and test sets.
12. Model Building:
This involves choosing and training an NLP model suitable for your task. Common NLP models include:
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Data Science job listings can be confusing because:
- some expect Data Scientists to be like Data Engineers and want you to build ridiculous pipelines from scratch
- some expect Data Scientists to be like Business Analysts and require you to build Tableau dashboards for shareholders
- some expect Data Scientists to be like Software Engineers and want you to create scalable applications for serving ML models
- some expect Data Scientists to be like MLops Engineers and ask you to set up and maintain CI/CD workflows
When will we all agree on what Data Scientists should and should not do?
- some expect Data Scientists to be like Data Engineers and want you to build ridiculous pipelines from scratch
- some expect Data Scientists to be like Business Analysts and require you to build Tableau dashboards for shareholders
- some expect Data Scientists to be like Software Engineers and want you to create scalable applications for serving ML models
- some expect Data Scientists to be like MLops Engineers and ask you to set up and maintain CI/CD workflows
When will we all agree on what Data Scientists should and should not do?
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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.
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Hope this helps you 😊
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://news.1rj.ru/str/datasciencefun
Like if you need similar content 😄👍
Hope this helps you 😊
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If you want to become a Data Scientist, you NEED to have product sense.
10 interview questions to test your product sense 👇
1. Netflix believes that viewers who watch foreign language content are more likely to remain subscribed. How would you prove or disprove this hypothesis?
2. LinkedIn believes that users who regularly update their skills get more job offers. How would you go about investigating this?
3. Snapchat is considering ways to capture an older demographic. As a Data Scientist, how would you advice your team on this?
4. Spotify leadership is wondering if they should divest from any product lines. How would you go about making a recommendation to the leadership team?
5. YouTube believes that creators who produce Shorts get better distribution on their Longs. How would you prove or disprove this hypothesis?
6. What are some suggestions you have for improving the Airbnb app? How would you go about testing this?
7. Instagram wants to develop features to help travelers. What are some ideas you have to help achieve this goal?
8. Amazon Web Services (AWS) leadership is wondering if they should discontinue any of their cloud services. How would you go about making a recommendation to the leadership team?
9. Salesforce is considering ways to better serve small businesses. As a Data Scientist, how would you advise your team on this?
10. Asana is a B2B business, and they’re considering ways to increase user adoption of their product.
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10 interview questions to test your product sense 👇
1. Netflix believes that viewers who watch foreign language content are more likely to remain subscribed. How would you prove or disprove this hypothesis?
2. LinkedIn believes that users who regularly update their skills get more job offers. How would you go about investigating this?
3. Snapchat is considering ways to capture an older demographic. As a Data Scientist, how would you advice your team on this?
4. Spotify leadership is wondering if they should divest from any product lines. How would you go about making a recommendation to the leadership team?
5. YouTube believes that creators who produce Shorts get better distribution on their Longs. How would you prove or disprove this hypothesis?
6. What are some suggestions you have for improving the Airbnb app? How would you go about testing this?
7. Instagram wants to develop features to help travelers. What are some ideas you have to help achieve this goal?
8. Amazon Web Services (AWS) leadership is wondering if they should discontinue any of their cloud services. How would you go about making a recommendation to the leadership team?
9. Salesforce is considering ways to better serve small businesses. As a Data Scientist, how would you advise your team on this?
10. Asana is a B2B business, and they’re considering ways to increase user adoption of their product.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍
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Pandas vs. Polars: Which one should you use for your next data project?
Here’s a comparison to help you to choose the right tool:
1. 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲:
𝗣𝗮𝗻𝗱𝗮𝘀: Great for small to medium-sized datasets but can slow down with larger data due to its row-based memory layout.
𝗣𝗼𝗹𝗮𝗿𝘀: Optimized for speed with a columnar memory layout, making it much faster for large datasets and complex operations.
2. 𝗘𝗮𝘀𝗲 𝗼𝗳 𝗨𝘀𝗲:
𝗣𝗮𝗻𝗱𝗮𝘀: Highly intuitive and widely adopted, making it easy to find resources, tutorials, and community support.
𝗣𝗼𝗹𝗮𝗿𝘀: Newer and less intuitive for those used to Pandas, but it's catching up quickly with comprehensive documentation and growing community support.
3. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆:
𝗣𝗮𝗻𝗱𝗮𝘀: Can be memory-intensive, especially with large DataFrames. Requires careful management to avoid memory issues.
𝗣𝗼𝗹𝗮𝗿𝘀: Designed for efficient memory usage, handling larger datasets better without requiring extensive optimization.
4. 𝗔𝗣𝗜 𝗮𝗻𝗱 𝗦𝘆𝗻𝘁𝗮𝘅:
𝗣𝗮𝗻𝗱𝗮𝘀: Large and mature API with extensive functionality for data manipulation and analysis.
𝗣𝗼𝗹𝗮𝗿𝘀: Offers a similar API to Pandas but focuses on a more modern and efficient approach. Some differences in syntax may require a learning curve.
5. 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹𝗶𝘀𝗺:
𝗣𝗮𝗻𝗱𝗮𝘀: Lacks built-in parallelism, requiring additional libraries like Dask for parallel processing.
𝗣𝗼𝗹𝗮𝗿𝘀: Built-in parallelism out of the box, leveraging multi-threading to speed up computations.
Choose Pandas for its simplicity and compatibility with existing projects. Go for Polars when performance and efficiency with large datasets are important.
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Here’s a comparison to help you to choose the right tool:
1. 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲:
𝗣𝗮𝗻𝗱𝗮𝘀: Great for small to medium-sized datasets but can slow down with larger data due to its row-based memory layout.
𝗣𝗼𝗹𝗮𝗿𝘀: Optimized for speed with a columnar memory layout, making it much faster for large datasets and complex operations.
2. 𝗘𝗮𝘀𝗲 𝗼𝗳 𝗨𝘀𝗲:
𝗣𝗮𝗻𝗱𝗮𝘀: Highly intuitive and widely adopted, making it easy to find resources, tutorials, and community support.
𝗣𝗼𝗹𝗮𝗿𝘀: Newer and less intuitive for those used to Pandas, but it's catching up quickly with comprehensive documentation and growing community support.
3. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆:
𝗣𝗮𝗻𝗱𝗮𝘀: Can be memory-intensive, especially with large DataFrames. Requires careful management to avoid memory issues.
𝗣𝗼𝗹𝗮𝗿𝘀: Designed for efficient memory usage, handling larger datasets better without requiring extensive optimization.
4. 𝗔𝗣𝗜 𝗮𝗻𝗱 𝗦𝘆𝗻𝘁𝗮𝘅:
𝗣𝗮𝗻𝗱𝗮𝘀: Large and mature API with extensive functionality for data manipulation and analysis.
𝗣𝗼𝗹𝗮𝗿𝘀: Offers a similar API to Pandas but focuses on a more modern and efficient approach. Some differences in syntax may require a learning curve.
5. 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹𝗶𝘀𝗺:
𝗣𝗮𝗻𝗱𝗮𝘀: Lacks built-in parallelism, requiring additional libraries like Dask for parallel processing.
𝗣𝗼𝗹𝗮𝗿𝘀: Built-in parallelism out of the box, leveraging multi-threading to speed up computations.
Choose Pandas for its simplicity and compatibility with existing projects. Go for Polars when performance and efficiency with large datasets are important.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍
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