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1. Master the fundamentals of Statistics
Understand probability, distributions, and hypothesis testing
Differentiate between descriptive vs inferential statistics
Learn various sampling techniques
2. Get hands-on with Python & SQL
Work with data structures, pandas, numpy, and matplotlib
Practice writing optimized SQL queries
Master joins, filters, groupings, and window functions
3. Build real-world projects
Construct end-to-end data pipelines
Develop predictive models with machine learning
Create business-focused dashboards
4. Practice case study interviews
Learn to break down ambiguous business problems
Ask clarifying questions to gather requirements
Think aloud and structure your answers logically
5. Mock interviews with feedback
Use platforms like Pramp or connect with peers
Record and review your answers for improvement
Gather feedback on your explanation and presence
6. Revise machine learning concepts
Understand supervised vs unsupervised learning
Grasp overfitting, underfitting, and bias-variance tradeoff
Know how to evaluate models (precision, recall, F1-score, AUC, etc.)
7. Brush up on system design (if applicable)
Learn how to design scalable data pipelines
Compare real-time vs batch processing
Familiarize with tools: Apache Spark, Kafka, Airflow
8. Strengthen storytelling with data
Apply the STAR method in behavioral questions
Simplify complex technical topics
Emphasize business impact and insight-driven decisions
9. Customize your resume and portfolio
Tailor your resume for each job role
Include links to projects or GitHub profiles
Match your skills to job descriptions
10. Stay consistent and track progress
Set clear weekly goals
Monitor covered topics and completed tasks
Reflect regularly and adapt your plan as needed
#DataScience #InterviewPrep #MLInterviews #DataEngineering #SQL #Python #Statistics #MachineLearning #DataStorytelling #SystemDesign #CareerGrowth #DataScienceRoadmap #PortfolioBuilding #MockInterviews #JobHuntingTips
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Over the last year, several articles have been written to help candidates prepare for data science technical interviews. These resources cover a wide range of topics including machine learning, SQL, programming, statistics, and probability.
1️⃣ Machine Learning (ML) Interview
Types of ML Q&A in Data Science Interview
https://shorturl.at/syN37
ML Interview Q&A for Data Scientists
https://shorturl.at/HVWY0
Crack the ML Coding Q&A
https://shorturl.at/CDW08
Deep Learning Interview Q&A
https://shorturl.at/lHPZ6
Top LLMs Interview Q&A
https://shorturl.at/wGRSZ
Top CV Interview Q&A [Part 1]
https://rb.gy/51jcfi
Part 2
https://rb.gy/hqgkbg
Part 3
https://rb.gy/5z87be
2️⃣ SQL Interview Preparation
13 SQL Statements for 90% of Data Science Tasks
https://rb.gy/dkdcl1
SQL Window Functions: Simplifying Complex Queries
https://t.ly/EwSlH
Ace the SQL Questions in the Technical Interview
https://lnkd.in/gNQbYMX9
Unlocking the Power of SQL: How to Ace Top N Problem Questions
https://lnkd.in/gvxVwb9n
How To Ace the SQL Ratio Problems
https://lnkd.in/g6JQqPNA
Cracking the SQL Window Function Coding Questions
https://lnkd.in/gk5u6hnE
SQL & Database Interview Q&A
https://lnkd.in/g75DsEfw
6 Free Resources for SQL Interview Preparation
https://lnkd.in/ghhiG79Q
3️⃣ Programming Questions
Foundations of Data Structures [Part 1]
https://lnkd.in/gX_ZcmRq
Part 2
https://lnkd.in/gATY4rTT
Top Important Python Questions [Conceptual]
https://lnkd.in/gJKaNww5
Top Important Python Questions [Data Cleaning and Preprocessing]
https://lnkd.in/g-pZBs3A
Top Important Python Questions [Machine & Deep Learning]
https://lnkd.in/gZwcceWN
Python Interview Q&A
https://lnkd.in/gcaXc_JE
5 Python Tips for Acing DS Coding Interview
https://lnkd.in/gsj_Hddd
4️⃣ Statistics
Mastering 5 Statistics Concepts to Boost Success
https://lnkd.in/gxEuHiG5
Mastering Hypothesis Testing for Interviews
https://lnkd.in/gSBbbmF8
Introduction to A/B Testing
https://lnkd.in/g35Jihw6
Statistics Interview Q&A for Data Scientists
https://lnkd.in/geHCCt6Q
5️⃣ Probability
15 Probability Concepts to Review [Part 1]
https://lnkd.in/g2rK2tQk
Part 2
https://lnkd.in/gQhXnKwJ
Probability Interview Q&A [Conceptual Questions]
https://lnkd.in/g5jyKqsp
Probability Interview Q&A [Mathematical Questions]
https://lnkd.in/gcWvPhVj
🔜 All links are available in the GitHub repository:
https://lnkd.in/djcgcKRT
Types of ML Q&A in Data Science Interview
https://shorturl.at/syN37
ML Interview Q&A for Data Scientists
https://shorturl.at/HVWY0
Crack the ML Coding Q&A
https://shorturl.at/CDW08
Deep Learning Interview Q&A
https://shorturl.at/lHPZ6
Top LLMs Interview Q&A
https://shorturl.at/wGRSZ
Top CV Interview Q&A [Part 1]
https://rb.gy/51jcfi
Part 2
https://rb.gy/hqgkbg
Part 3
https://rb.gy/5z87be
13 SQL Statements for 90% of Data Science Tasks
https://rb.gy/dkdcl1
SQL Window Functions: Simplifying Complex Queries
https://t.ly/EwSlH
Ace the SQL Questions in the Technical Interview
https://lnkd.in/gNQbYMX9
Unlocking the Power of SQL: How to Ace Top N Problem Questions
https://lnkd.in/gvxVwb9n
How To Ace the SQL Ratio Problems
https://lnkd.in/g6JQqPNA
Cracking the SQL Window Function Coding Questions
https://lnkd.in/gk5u6hnE
SQL & Database Interview Q&A
https://lnkd.in/g75DsEfw
6 Free Resources for SQL Interview Preparation
https://lnkd.in/ghhiG79Q
Foundations of Data Structures [Part 1]
https://lnkd.in/gX_ZcmRq
Part 2
https://lnkd.in/gATY4rTT
Top Important Python Questions [Conceptual]
https://lnkd.in/gJKaNww5
Top Important Python Questions [Data Cleaning and Preprocessing]
https://lnkd.in/g-pZBs3A
Top Important Python Questions [Machine & Deep Learning]
https://lnkd.in/gZwcceWN
Python Interview Q&A
https://lnkd.in/gcaXc_JE
5 Python Tips for Acing DS Coding Interview
https://lnkd.in/gsj_Hddd
Mastering 5 Statistics Concepts to Boost Success
https://lnkd.in/gxEuHiG5
Mastering Hypothesis Testing for Interviews
https://lnkd.in/gSBbbmF8
Introduction to A/B Testing
https://lnkd.in/g35Jihw6
Statistics Interview Q&A for Data Scientists
https://lnkd.in/geHCCt6Q
15 Probability Concepts to Review [Part 1]
https://lnkd.in/g2rK2tQk
Part 2
https://lnkd.in/gQhXnKwJ
Probability Interview Q&A [Conceptual Questions]
https://lnkd.in/g5jyKqsp
Probability Interview Q&A [Mathematical Questions]
https://lnkd.in/gcWvPhVj
https://lnkd.in/djcgcKRT
#DataScience #InterviewPrep #MachineLearning #SQL #Python #Statistics #Probability #CodingInterview #AIBootcamp #DeepLearning #LLMs #ComputerVision #GitHubResources #CareerInDataScience
✉️ Our Telegram channels: https://yangx.top/addlist/0f6vfFbEMdAwODBk📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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If you are doing regression modeling in Python for explanatory purposes, don't use scikit-learn - it's not set up for explanatory modeling. Use #statsmodels. It's set up much better for immediately showing you all the underlying parameters of your model and helping you interpret your results..
#analytics #peopleanalytics #datascience #rstats #python
#analytics #peopleanalytics #datascience #rstats #python
✉️ Our Telegram channels: https://yangx.top/addlist/0f6vfFbEMdAwODBk📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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Forwarded from Python | Machine Learning | Coding | R
#DataScience #SQL #Python #MachineLearning #Statistics #BusinessAnalytics #ProductCaseStudies #DataScienceProjects #InterviewPrep #LearnDataScience #YouTubeLearning #CodingInterview #MLInterview #SQLProjects #PythonForDataScience
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Topic: Handling Datasets of All Types – Part 1 of 5: Introduction and Basic Concepts
---
1. What is a Dataset?
• A dataset is a structured collection of data, usually organized in rows and columns, used for analysis or training machine learning models.
---
2. Types of Datasets
• Structured Data: Tables, spreadsheets with rows and columns (e.g., CSV, Excel).
• Unstructured Data: Images, text, audio, video.
• Semi-structured Data: JSON, XML files containing hierarchical data.
---
3. Common Dataset Formats
• CSV (Comma-Separated Values)
• Excel (.xls, .xlsx)
• JSON (JavaScript Object Notation)
• XML (eXtensible Markup Language)
• Images (JPEG, PNG, TIFF)
• Audio (WAV, MP3)
---
4. Loading Datasets in Python
• Use libraries like
• Use libraries like
---
5. Basic Dataset Exploration
• Check shape and size:
• Preview data:
• Check for missing values:
---
6. Summary
• Understanding dataset types is crucial before processing.
• Loading and exploring datasets helps identify cleaning and preprocessing needs.
---
Exercise
• Load a CSV and JSON dataset in Python, print their shapes, and identify missing values.
---
#DataScience #Datasets #DataLoading #Python #DataExploration
https://yangx.top/DataScienceM
---
1. What is a Dataset?
• A dataset is a structured collection of data, usually organized in rows and columns, used for analysis or training machine learning models.
---
2. Types of Datasets
• Structured Data: Tables, spreadsheets with rows and columns (e.g., CSV, Excel).
• Unstructured Data: Images, text, audio, video.
• Semi-structured Data: JSON, XML files containing hierarchical data.
---
3. Common Dataset Formats
• CSV (Comma-Separated Values)
• Excel (.xls, .xlsx)
• JSON (JavaScript Object Notation)
• XML (eXtensible Markup Language)
• Images (JPEG, PNG, TIFF)
• Audio (WAV, MP3)
---
4. Loading Datasets in Python
• Use libraries like
pandas for structured data:import pandas as pd
df = pd.read_csv('data.csv')
• Use libraries like
json for JSON files:import json
with open('data.json') as f:
data = json.load(f)
---
5. Basic Dataset Exploration
• Check shape and size:
print(df.shape)
• Preview data:
print(df.head())
• Check for missing values:
print(df.isnull().sum())
---
6. Summary
• Understanding dataset types is crucial before processing.
• Loading and exploring datasets helps identify cleaning and preprocessing needs.
---
Exercise
• Load a CSV and JSON dataset in Python, print their shapes, and identify missing values.
---
#DataScience #Datasets #DataLoading #Python #DataExploration
https://yangx.top/DataScienceM
❤3👍2
Topic: Handling Datasets of All Types – Part 2 of 5: Data Cleaning and Preprocessing
---
1. Importance of Data Cleaning
• Real-world data is often noisy, incomplete, or inconsistent.
• Cleaning improves data quality and model performance.
---
2. Handling Missing Data
• Detect missing values using
• Strategies to handle missing data:
* Remove rows or columns with missing values:
* Impute missing values with mean, median, or mode:
---
3. Handling Outliers
• Outliers can skew analysis and model results.
• Detect outliers using:
* Boxplots
* Z-score method
* IQR (Interquartile Range)
• Handle by removal or transformation.
---
4. Data Normalization and Scaling
• Many ML models require features to be on a similar scale.
• Common techniques:
* Min-Max Scaling (scales values between 0 and 1)
* Standardization (mean = 0, std = 1)
---
5. Encoding Categorical Variables
• Convert categorical data into numerical:
* Label Encoding: Assigns an integer to each category.
* One-Hot Encoding: Creates binary columns for each category.
---
6. Summary
• Data cleaning is essential for reliable modeling.
• Handling missing values, outliers, scaling, and encoding are key preprocessing steps.
---
Exercise
• Load a dataset, identify missing values, and apply mean imputation.
• Detect outliers using IQR and remove them.
• Normalize numeric features using standardization.
---
#DataCleaning #DataPreprocessing #MachineLearning #Python #DataScience
https://yangx.top/DataScienceM
---
1. Importance of Data Cleaning
• Real-world data is often noisy, incomplete, or inconsistent.
• Cleaning improves data quality and model performance.
---
2. Handling Missing Data
• Detect missing values using
isnull() or isna() in pandas.• Strategies to handle missing data:
* Remove rows or columns with missing values:
df.dropna(inplace=True)
* Impute missing values with mean, median, or mode:
df['column'].fillna(df['column'].mean(), inplace=True)
---
3. Handling Outliers
• Outliers can skew analysis and model results.
• Detect outliers using:
* Boxplots
* Z-score method
* IQR (Interquartile Range)
• Handle by removal or transformation.
---
4. Data Normalization and Scaling
• Many ML models require features to be on a similar scale.
• Common techniques:
* Min-Max Scaling (scales values between 0 and 1)
* Standardization (mean = 0, std = 1)
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
df_scaled = scaler.fit_transform(df[['feature1', 'feature2']])
---
5. Encoding Categorical Variables
• Convert categorical data into numerical:
* Label Encoding: Assigns an integer to each category.
* One-Hot Encoding: Creates binary columns for each category.
pd.get_dummies(df['category_column'])
---
6. Summary
• Data cleaning is essential for reliable modeling.
• Handling missing values, outliers, scaling, and encoding are key preprocessing steps.
---
Exercise
• Load a dataset, identify missing values, and apply mean imputation.
• Detect outliers using IQR and remove them.
• Normalize numeric features using standardization.
---
#DataCleaning #DataPreprocessing #MachineLearning #Python #DataScience
https://yangx.top/DataScienceM
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Topic: Handling Datasets of All Types – Part 2 of 5: Data Cleaning and Preprocessing
---
1. Importance of Data Cleaning
• Real-world data is often noisy, incomplete, or inconsistent.
• Cleaning improves data quality and model performance.
---
2. Handling Missing Data
• Detect missing values using
• Strategies to handle missing data:
* Remove rows or columns with missing values:
* Impute missing values with mean, median, or mode:
---
3. Handling Outliers
• Outliers can skew analysis and model results.
• Detect outliers using:
* Boxplots
* Z-score method
* IQR (Interquartile Range)
• Handle by removal or transformation.
---
4. Data Normalization and Scaling
• Many ML models require features to be on a similar scale.
• Common techniques:
* Min-Max Scaling (scales values between 0 and 1)
* Standardization (mean = 0, std = 1)
---
5. Encoding Categorical Variables
• Convert categorical data into numerical:
* Label Encoding: Assigns an integer to each category.
* One-Hot Encoding: Creates binary columns for each category.
---
6. Summary
• Data cleaning is essential for reliable modeling.
• Handling missing values, outliers, scaling, and encoding are key preprocessing steps.
---
Exercise
• Load a dataset, identify missing values, and apply mean imputation.
• Detect outliers using IQR and remove them.
• Normalize numeric features using standardization.
---
#DataCleaning #DataPreprocessing #MachineLearning #Python #DataScience
https://yangx.top/DataScience4M
---
1. Importance of Data Cleaning
• Real-world data is often noisy, incomplete, or inconsistent.
• Cleaning improves data quality and model performance.
---
2. Handling Missing Data
• Detect missing values using
isnull() or isna() in pandas.• Strategies to handle missing data:
* Remove rows or columns with missing values:
df.dropna(inplace=True)
* Impute missing values with mean, median, or mode:
df['column'].fillna(df['column'].mean(), inplace=True)
---
3. Handling Outliers
• Outliers can skew analysis and model results.
• Detect outliers using:
* Boxplots
* Z-score method
* IQR (Interquartile Range)
• Handle by removal or transformation.
---
4. Data Normalization and Scaling
• Many ML models require features to be on a similar scale.
• Common techniques:
* Min-Max Scaling (scales values between 0 and 1)
* Standardization (mean = 0, std = 1)
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
df_scaled = scaler.fit_transform(df[['feature1', 'feature2']])
---
5. Encoding Categorical Variables
• Convert categorical data into numerical:
* Label Encoding: Assigns an integer to each category.
* One-Hot Encoding: Creates binary columns for each category.
pd.get_dummies(df['category_column'])
---
6. Summary
• Data cleaning is essential for reliable modeling.
• Handling missing values, outliers, scaling, and encoding are key preprocessing steps.
---
Exercise
• Load a dataset, identify missing values, and apply mean imputation.
• Detect outliers using IQR and remove them.
• Normalize numeric features using standardization.
---
#DataCleaning #DataPreprocessing #MachineLearning #Python #DataScience
https://yangx.top/DataScience4M
❤4👍1
Topic: Handling Datasets of All Types – Part 4 of 5: Text Data Processing and Natural Language Processing (NLP)
---
1. Understanding Text Data
• Text data is unstructured and requires preprocessing to convert into numeric form for ML models.
• Common tasks: classification, sentiment analysis, language modeling.
---
2. Text Preprocessing Steps
• Tokenization: Splitting text into words or subwords.
• Lowercasing: Convert all text to lowercase for uniformity.
• Removing Punctuation and Stopwords: Clean unnecessary words.
• Stemming and Lemmatization: Reduce words to their root form.
---
3. Encoding Text Data
• Bag-of-Words (BoW): Represents text as word count vectors.
• TF-IDF (Term Frequency-Inverse Document Frequency): Weighs words based on importance.
• Word Embeddings: Dense vector representations capturing semantic meaning (e.g., Word2Vec, GloVe).
---
4. Loading and Processing Text Data in Python
---
5. Handling Large Text Datasets
• Use libraries like NLTK, spaCy, and Transformers.
• For deep learning, tokenize using models like BERT or GPT.
---
6. Summary
• Text data needs extensive preprocessing and encoding.
• Choosing the right representation is crucial for model success.
---
Exercise
• Clean a set of sentences by tokenizing and removing stopwords.
• Convert cleaned text into TF-IDF vectors.
---
#NLP #TextProcessing #DataScience #MachineLearning #Python
https://yangx.top/DataScienceM
---
1. Understanding Text Data
• Text data is unstructured and requires preprocessing to convert into numeric form for ML models.
• Common tasks: classification, sentiment analysis, language modeling.
---
2. Text Preprocessing Steps
• Tokenization: Splitting text into words or subwords.
• Lowercasing: Convert all text to lowercase for uniformity.
• Removing Punctuation and Stopwords: Clean unnecessary words.
• Stemming and Lemmatization: Reduce words to their root form.
---
3. Encoding Text Data
• Bag-of-Words (BoW): Represents text as word count vectors.
• TF-IDF (Term Frequency-Inverse Document Frequency): Weighs words based on importance.
• Word Embeddings: Dense vector representations capturing semantic meaning (e.g., Word2Vec, GloVe).
---
4. Loading and Processing Text Data in Python
from sklearn.feature_extraction.text import TfidfVectorizer
texts = ["I love data science.", "Data science is fun."]
vectorizer = TfidfVectorizer(stop_words='english')
X = vectorizer.fit_transform(texts)
---
5. Handling Large Text Datasets
• Use libraries like NLTK, spaCy, and Transformers.
• For deep learning, tokenize using models like BERT or GPT.
---
6. Summary
• Text data needs extensive preprocessing and encoding.
• Choosing the right representation is crucial for model success.
---
Exercise
• Clean a set of sentences by tokenizing and removing stopwords.
• Convert cleaned text into TF-IDF vectors.
---
#NLP #TextProcessing #DataScience #MachineLearning #Python
https://yangx.top/DataScienceM
❤3👍1
Topic: Handling Datasets of All Types – Part 5 of 5: Working with Time Series and Tabular Data
---
1. Understanding Time Series Data
• Time series data is a sequence of data points collected over time intervals.
• Examples: stock prices, weather data, sensor readings.
---
2. Loading and Exploring Time Series Data
---
3. Key Time Series Concepts
• Trend: Long-term increase or decrease in data.
• Seasonality: Repeating patterns at regular intervals.
• Noise: Random variations.
---
4. Preprocessing Time Series
• Handle missing data using forward/backward fill.
• Resample data to different frequencies (daily, monthly).
---
5. Working with Tabular Data
• Tabular data consists of rows (samples) and columns (features).
• Often requires handling missing values, encoding categorical variables, and scaling features (covered in previous parts).
---
6. Summary
• Time series data requires special preprocessing due to temporal order.
• Tabular data is the most common format, needing cleaning and feature engineering.
---
Exercise
• Load a time series dataset, fill missing values, and resample it monthly.
• For tabular data, encode categorical variables and scale numerical features.
---
#TimeSeries #TabularData #DataScience #MachineLearning #Python
https://yangx.top/DataScienceM
---
1. Understanding Time Series Data
• Time series data is a sequence of data points collected over time intervals.
• Examples: stock prices, weather data, sensor readings.
---
2. Loading and Exploring Time Series Data
import pandas as pd
df = pd.read_csv('time_series.csv', parse_dates=['date'], index_col='date')
print(df.head())
---
3. Key Time Series Concepts
• Trend: Long-term increase or decrease in data.
• Seasonality: Repeating patterns at regular intervals.
• Noise: Random variations.
---
4. Preprocessing Time Series
• Handle missing data using forward/backward fill.
df.fillna(method='ffill', inplace=True)
• Resample data to different frequencies (daily, monthly).
df_resampled = df.resample('M').mean()---
5. Working with Tabular Data
• Tabular data consists of rows (samples) and columns (features).
• Often requires handling missing values, encoding categorical variables, and scaling features (covered in previous parts).
---
6. Summary
• Time series data requires special preprocessing due to temporal order.
• Tabular data is the most common format, needing cleaning and feature engineering.
---
Exercise
• Load a time series dataset, fill missing values, and resample it monthly.
• For tabular data, encode categorical variables and scale numerical features.
---
#TimeSeries #TabularData #DataScience #MachineLearning #Python
https://yangx.top/DataScienceM
❤5
Topic: 25 Important Questions on Handling Datasets of All Types in Python
---
1. What are the common types of datasets?
Structured, unstructured, and semi-structured.
---
2. How do you load a CSV file in Python?
Using
---
3. How to check for missing values in a dataset?
Using
---
4. What methods can you use to handle missing data?
Remove rows/columns, mean/median/mode imputation, interpolation.
---
5. How to detect outliers in data?
Using boxplots, z-score, or interquartile range (IQR) methods.
---
6. What is data normalization?
Scaling data to a specific range, often \[0,1].
---
7. What is data standardization?
Rescaling data to have zero mean and unit variance.
---
8. How to encode categorical variables?
Label encoding or one-hot encoding.
---
9. What libraries help with image data processing in Python?
OpenCV, Pillow, scikit-image.
---
10. How do you load and preprocess images for ML models?
Resize, normalize pixel values, data augmentation.
---
11. How can audio data be loaded in Python?
Using libraries like
---
12. What are MFCCs in audio processing?
Mel-frequency cepstral coefficients – features extracted from audio signals.
---
13. How do you preprocess text data?
Tokenization, removing stopwords, stemming, lemmatization.
---
14. What is TF-IDF?
A technique to weigh words based on frequency and importance.
---
15. How do you handle variable-length sequences in text or time series?
Padding sequences or using packed sequences.
---
16. How to handle time series missing data?
Forward fill, backward fill, interpolation.
---
17. What is data augmentation?
Creating new data samples by transforming existing data.
---
18. How to split datasets into training and testing sets?
Using
---
19. What is batch processing in ML?
Processing data in small batches during training for efficiency.
---
20. How to save and load datasets efficiently?
Using formats like HDF5, pickle, or TFRecord.
---
21. What is feature scaling and why is it important?
Adjusting features to a common scale to improve model training.
---
22. How to detect and remove duplicate data?
Using
---
23. What is one-hot encoding and when to use it?
Converting categorical variables to binary vectors, used for nominal categories.
---
24. How to handle imbalanced datasets?
Techniques like oversampling, undersampling, or synthetic data generation (SMOTE).
---
25. How to visualize datasets in Python?
Using matplotlib, seaborn, or plotly for charts and graphs.
---
#DataScience #DataHandling #Python #MachineLearning #DataPreprocessing
https://yangx.top/DataScience4M
---
1. What are the common types of datasets?
Structured, unstructured, and semi-structured.
---
2. How do you load a CSV file in Python?
Using
pandas.read_csv() function.---
3. How to check for missing values in a dataset?
Using
df.isnull().sum() in pandas.---
4. What methods can you use to handle missing data?
Remove rows/columns, mean/median/mode imputation, interpolation.
---
5. How to detect outliers in data?
Using boxplots, z-score, or interquartile range (IQR) methods.
---
6. What is data normalization?
Scaling data to a specific range, often \[0,1].
---
7. What is data standardization?
Rescaling data to have zero mean and unit variance.
---
8. How to encode categorical variables?
Label encoding or one-hot encoding.
---
9. What libraries help with image data processing in Python?
OpenCV, Pillow, scikit-image.
---
10. How do you load and preprocess images for ML models?
Resize, normalize pixel values, data augmentation.
---
11. How can audio data be loaded in Python?
Using libraries like
librosa or scipy.io.wavfile.---
12. What are MFCCs in audio processing?
Mel-frequency cepstral coefficients – features extracted from audio signals.
---
13. How do you preprocess text data?
Tokenization, removing stopwords, stemming, lemmatization.
---
14. What is TF-IDF?
A technique to weigh words based on frequency and importance.
---
15. How do you handle variable-length sequences in text or time series?
Padding sequences or using packed sequences.
---
16. How to handle time series missing data?
Forward fill, backward fill, interpolation.
---
17. What is data augmentation?
Creating new data samples by transforming existing data.
---
18. How to split datasets into training and testing sets?
Using
train_test_split from scikit-learn.---
19. What is batch processing in ML?
Processing data in small batches during training for efficiency.
---
20. How to save and load datasets efficiently?
Using formats like HDF5, pickle, or TFRecord.
---
21. What is feature scaling and why is it important?
Adjusting features to a common scale to improve model training.
---
22. How to detect and remove duplicate data?
Using
df.duplicated() and df.drop_duplicates().---
23. What is one-hot encoding and when to use it?
Converting categorical variables to binary vectors, used for nominal categories.
---
24. How to handle imbalanced datasets?
Techniques like oversampling, undersampling, or synthetic data generation (SMOTE).
---
25. How to visualize datasets in Python?
Using matplotlib, seaborn, or plotly for charts and graphs.
---
#DataScience #DataHandling #Python #MachineLearning #DataPreprocessing
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📘 Ultimate Guide to Graph Neural Networks (GNNs): Part 1 — Foundations of Graph Theory & Why GNNs Revolutionize AI
Duration: ~45 minutes reading time | Comprehensive beginner-to-advanced introduction
Let's start: https://hackmd.io/@husseinsheikho/GNN-1
Duration: ~45 minutes reading time | Comprehensive beginner-to-advanced introduction
Let's start: https://hackmd.io/@husseinsheikho/GNN-1
#GraphNeuralNetworks #GNN #MachineLearning #DeepLearning #AI #NeuralNetworks #DataScience #GraphTheory #ArtificialIntelligence #PyTorchGeometric #NodeClassification #LinkPrediction #GraphRepresentation #AIforBeginners #AdvancedAI
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📘 Ultimate Guide to Graph Neural Networks (GNNs): Part 2 — The Message Passing Framework: Mathematical Heart of All GNNs
Duration: ~60 minutes reading time | Comprehensive deep dive into the core mechanism powering modern GNNs
Let's study: https://hackmd.io/@husseinsheikho/GNN-2
Duration: ~60 minutes reading time | Comprehensive deep dive into the core mechanism powering modern GNNs
Let's study: https://hackmd.io/@husseinsheikho/GNN-2
#GraphNeuralNetworks #GNN #MachineLearning #DeepLearning #AI #NeuralNetworks #DataScience #GraphTheory #ArtificialIntelligence #PyTorchGeometric #MessagePassing #GraphAlgorithms #NodeClassification #LinkPrediction #GraphRepresentation #AIforBeginners #AdvancedAI
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Duration: ~60 minutes reading time | Comprehensive deep dive into cutting-edge GNN architectures
#GraphNeuralNetworks #GNN #MachineLearning #DeepLearning #AI #NeuralNetworks #DataScience #GraphTheory #ArtificialIntelligence #PyTorchGeometric #GraphTransformers #TemporalGNNs #GeometricDeepLearning #AdvancedGNNs #AIforBeginners #AdvancedAI
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📘 Ultimate Guide to Graph Neural Networks (GNNs): Part 4 — GNN Training Dynamics, Optimization Challenges, and Scalability Solutions
Duration: ~45 minutes reading time | Comprehensive guide to training GNNs effectively at scale
Part 4-A: https://hackmd.io/@husseinsheikho/GNN4-A
Part4-B: https://hackmd.io/@husseinsheikho/GNN4-B
Duration: ~45 minutes reading time | Comprehensive guide to training GNNs effectively at scale
Part 4-A: https://hackmd.io/@husseinsheikho/GNN4-A
Part4-B: https://hackmd.io/@husseinsheikho/GNN4-B
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📘 Ultimate Guide to Graph Neural Networks (GNNs): Part 5 — GNN Applications Across Domains: Real-World Impact in 30 Minutes
Duration: ~30 minutes reading time | Practical guide to GNN applications with concrete ROI metrics
Link: https://hackmd.io/@husseinsheikho/GNN-5
Duration: ~30 minutes reading time | Practical guide to GNN applications with concrete ROI metrics
Link: https://hackmd.io/@husseinsheikho/GNN-5
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📘 Ultimate Guide to Graph Neural Networks (GNNs): Part 6 — Advanced Frontiers, Ethics, and Future Directions
Duration: ~50 minutes reading time | Cutting-edge insights on where GNNs are headed
Let's read: https://hackmd.io/@husseinsheikho/GNN-6
Duration: ~50 minutes reading time | Cutting-edge insights on where GNNs are headed
Let's read: https://hackmd.io/@husseinsheikho/GNN-6
#GraphNeuralNetworks #GNN #MachineLearning #DeepLearning #AI #NeuralNetworks #DataScience #GraphTheory #ArtificialIntelligence #FutureOfGNNs #EmergingResearch #EthicalAI #GNNBestPractices #AdvancedAI #50MinuteRead
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📘 Ultimate Guide to Graph Neural Networks (GNNs): Part 7 — Advanced Implementation, Multimodal Integration, and Scientific Applications
Duration: ~60 minutes reading time | Deep dive into cutting-edge GNN implementations and applications
Read: https://hackmd.io/@husseinsheikho/GNN7
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Duration: ~60 minutes reading time | Deep dive into cutting-edge GNN implementations and applications
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#GraphNeuralNetworks #GNN #MachineLearning #DeepLearning #AI #NeuralNetworks #DataScience #GraphTheory #ArtificialIntelligence #AdvancedGNNs #MultimodalLearning #ScientificAI #GNNImplementation #60MinuteRead
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PyTorch Masterclass: Part 1 – Foundations of Deep Learning with PyTorch
Duration: ~120 minutes
Link: https://hackmd.io/@husseinsheikho/pytorch-1
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Duration: ~120 minutes
Link: https://hackmd.io/@husseinsheikho/pytorch-1
#PyTorch #DeepLearning #MachineLearning #AI #NeuralNetworks #DataScience #Python #Tensors #Autograd #Backpropagation #GradientDescent #AIForBeginners #PyTorchTutorial #MachineLearningEngineer
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Best Practice for R :: Cheat Sheet
More: https://github.com/wurli/r-best-practice
#rstats #stats #datascience
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More: https://github.com/wurli/r-best-practice
#rstats #stats #datascience
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✨ Object Tracking with YOLOv8 and Python ✨
📖 Table of Contents Object Tracking with YOLOv8 and Python YOLOv8: Reliable Object Detection and Tracking Understanding YOLOv8 Architecture Mosaic Data Augmentation Anchor-Free Detection C2f (Coarse-to-Fine) Module Decoupled Head Loss Object Detection and Tracking with YOLOv8 Object Detection Object T...
🏷️ #AdvancedComputerVision #DataScience #DeepLearning #MachineLearning #ObjectDetection #ObjectTracking #ProgrammingTutorials #Tutorial #VideoObjectTracking #YOLO
📖 Table of Contents Object Tracking with YOLOv8 and Python YOLOv8: Reliable Object Detection and Tracking Understanding YOLOv8 Architecture Mosaic Data Augmentation Anchor-Free Detection C2f (Coarse-to-Fine) Module Decoupled Head Loss Object Detection and Tracking with YOLOv8 Object Detection Object T...
🏷️ #AdvancedComputerVision #DataScience #DeepLearning #MachineLearning #ObjectDetection #ObjectTracking #ProgrammingTutorials #Tutorial #VideoObjectTracking #YOLO