Advanced Skills to Elevate Your Data Analytics Career
1๏ธโฃ SQL Optimization & Performance Tuning
๐ Learn indexing, query optimization, and execution plans to handle large datasets efficiently.
2๏ธโฃ Machine Learning Basics
๐ค Understand supervised and unsupervised learning, feature engineering, and model evaluation to enhance analytical capabilities.
3๏ธโฃ Big Data Technologies
๐๏ธ Explore Spark, Hadoop, and cloud platforms like AWS, Azure, or Google Cloud for large-scale data processing.
4๏ธโฃ Data Engineering Skills
โ๏ธ Learn ETL pipelines, data warehousing, and workflow automation to streamline data processing.
5๏ธโฃ Advanced Python for Analytics
๐ Master libraries like Scikit-Learn, TensorFlow, and Statsmodels for predictive analytics and automation.
6๏ธโฃ A/B Testing & Experimentation
๐ฏ Design and analyze controlled experiments to drive data-driven decision-making.
7๏ธโฃ Dashboard Design & UX
๐จ Build interactive dashboards with Power BI, Tableau, or Looker that enhance user experience.
8๏ธโฃ Cloud Data Analytics
โ๏ธ Work with cloud databases like BigQuery, Snowflake, and Redshift for scalable analytics.
9๏ธโฃ Domain Expertise
๐ผ Gain industry-specific knowledge (e.g., finance, healthcare, e-commerce) to provide more relevant insights.
๐ Soft Skills & Leadership
๐ก Develop stakeholder management, storytelling, and mentorship skills to advance in your career.
Hope it helps :)
#dataanalytics
1๏ธโฃ SQL Optimization & Performance Tuning
๐ Learn indexing, query optimization, and execution plans to handle large datasets efficiently.
2๏ธโฃ Machine Learning Basics
๐ค Understand supervised and unsupervised learning, feature engineering, and model evaluation to enhance analytical capabilities.
3๏ธโฃ Big Data Technologies
๐๏ธ Explore Spark, Hadoop, and cloud platforms like AWS, Azure, or Google Cloud for large-scale data processing.
4๏ธโฃ Data Engineering Skills
โ๏ธ Learn ETL pipelines, data warehousing, and workflow automation to streamline data processing.
5๏ธโฃ Advanced Python for Analytics
๐ Master libraries like Scikit-Learn, TensorFlow, and Statsmodels for predictive analytics and automation.
6๏ธโฃ A/B Testing & Experimentation
๐ฏ Design and analyze controlled experiments to drive data-driven decision-making.
7๏ธโฃ Dashboard Design & UX
๐จ Build interactive dashboards with Power BI, Tableau, or Looker that enhance user experience.
8๏ธโฃ Cloud Data Analytics
โ๏ธ Work with cloud databases like BigQuery, Snowflake, and Redshift for scalable analytics.
9๏ธโฃ Domain Expertise
๐ผ Gain industry-specific knowledge (e.g., finance, healthcare, e-commerce) to provide more relevant insights.
๐ Soft Skills & Leadership
๐ก Develop stakeholder management, storytelling, and mentorship skills to advance in your career.
Hope it helps :)
#dataanalytics
๐2โค1
Step-by-step guide to become a Data Analyst in 2025โ๐
1. Learn the Fundamentals:
Start with Excel, basic statistics, and data visualization concepts.
2. Pick Up Key Tools & Languages:
Master SQL, Python (or R), and data visualization tools like Tableau or Power BI.
3. Get Formal Education or Certification:
A bachelorโs degree in a relevant field (like Computer Science, Math, or Economics) helps, but you can also do online courses or certifications in data analytics.
4. Build Hands-on Experience:
Work on real-world projectsโuse Kaggle datasets, internships, or freelance gigs to practice data cleaning, analysis, and visualization.
5. Create a Portfolio:
Showcase your projects on GitHub or a personal website. Include dashboards, reports, and code samples.
6. Develop Soft Skills:
Focus on communication, problem-solving, teamwork, and attention to detailโthese are just as important as technical skills.
7. Apply for Entry-Level Jobs:
Look for roles like โJunior Data Analystโ or โBusiness Analyst.โ Tailor your resume to highlight your skills and portfolio.
8. Keep Learning:
Stay updated with new tools (like AI-driven analytics), trends, and advanced topics such as machine learning or domain-specific analytics.
React โค๏ธ for more
1. Learn the Fundamentals:
Start with Excel, basic statistics, and data visualization concepts.
2. Pick Up Key Tools & Languages:
Master SQL, Python (or R), and data visualization tools like Tableau or Power BI.
3. Get Formal Education or Certification:
A bachelorโs degree in a relevant field (like Computer Science, Math, or Economics) helps, but you can also do online courses or certifications in data analytics.
4. Build Hands-on Experience:
Work on real-world projectsโuse Kaggle datasets, internships, or freelance gigs to practice data cleaning, analysis, and visualization.
5. Create a Portfolio:
Showcase your projects on GitHub or a personal website. Include dashboards, reports, and code samples.
6. Develop Soft Skills:
Focus on communication, problem-solving, teamwork, and attention to detailโthese are just as important as technical skills.
7. Apply for Entry-Level Jobs:
Look for roles like โJunior Data Analystโ or โBusiness Analyst.โ Tailor your resume to highlight your skills and portfolio.
8. Keep Learning:
Stay updated with new tools (like AI-driven analytics), trends, and advanced topics such as machine learning or domain-specific analytics.
React โค๏ธ for more
โค3๐1
โ
๐-๐๐ญ๐๐ฉ ๐๐จ๐๐๐ฆ๐๐ฉ ๐ญ๐จ ๐๐ฐ๐ข๐ญ๐๐ก ๐ข๐ง๐ญ๐จ ๐ญ๐ก๐ ๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ญ๐ข๐๐ฌ ๐
๐ข๐๐ฅ๐โ
๐โโ๏ธ๐๐ฎ๐ข๐ฅ๐ ๐๐๐ฒ ๐๐ค๐ข๐ฅ๐ฅ๐ฌ: Focus on core skillsโExcel, SQL, Power BI, and Python.
๐โโ๏ธ๐๐๐ง๐๐ฌ-๐๐ง ๐๐ซ๐จ๐ฃ๐๐๐ญ๐ฌ: Apply your skills to real-world data sets. Projects like sales analysis or customer segmentation show your practical experience. You can find projects on Youtube.
๐โโ๏ธ๐ ๐ข๐ง๐ ๐ ๐๐๐ง๐ญ๐จ๐ซ: Connect with someone experienced in data analytics for guidance(like me ๐ ). They can provide valuable insights, feedback, and keep you on track.
๐โโ๏ธ๐๐ซ๐๐๐ญ๐ ๐๐จ๐ซ๐ญ๐๐จ๐ฅ๐ข๐จ: Compile your projects in a portfolio or on GitHub. A solid portfolio catches a recruiterโs eye.
๐โโ๏ธ๐๐ซ๐๐๐ญ๐ข๐๐ ๐๐จ๐ซ ๐๐ง๐ญ๐๐ซ๐ฏ๐ข๐๐ฐ๐ฌ: Practice SQL queries and Python coding challenges on Hackerrank & LeetCode. Strengthening your problem-solving skills will prepare you for interviews.
๐โโ๏ธ๐๐ฎ๐ข๐ฅ๐ ๐๐๐ฒ ๐๐ค๐ข๐ฅ๐ฅ๐ฌ: Focus on core skillsโExcel, SQL, Power BI, and Python.
๐โโ๏ธ๐๐๐ง๐๐ฌ-๐๐ง ๐๐ซ๐จ๐ฃ๐๐๐ญ๐ฌ: Apply your skills to real-world data sets. Projects like sales analysis or customer segmentation show your practical experience. You can find projects on Youtube.
๐โโ๏ธ๐ ๐ข๐ง๐ ๐ ๐๐๐ง๐ญ๐จ๐ซ: Connect with someone experienced in data analytics for guidance(like me ๐ ). They can provide valuable insights, feedback, and keep you on track.
๐โโ๏ธ๐๐ซ๐๐๐ญ๐ ๐๐จ๐ซ๐ญ๐๐จ๐ฅ๐ข๐จ: Compile your projects in a portfolio or on GitHub. A solid portfolio catches a recruiterโs eye.
๐โโ๏ธ๐๐ซ๐๐๐ญ๐ข๐๐ ๐๐จ๐ซ ๐๐ง๐ญ๐๐ซ๐ฏ๐ข๐๐ฐ๐ฌ: Practice SQL queries and Python coding challenges on Hackerrank & LeetCode. Strengthening your problem-solving skills will prepare you for interviews.
โค1
Important questions to ace your machine learning interview with an approach to answer:
1. Machine Learning Project Lifecycle:
- Define the problem
- Gather and preprocess data
- Choose a model and train it
- Evaluate model performance
- Tune and optimize the model
- Deploy and maintain the model
2. Supervised vs Unsupervised Learning:
- Supervised Learning: Uses labeled data for training (e.g., predicting house prices from features).
- Unsupervised Learning: Uses unlabeled data to find patterns or groupings (e.g., clustering customer segments).
3. Evaluation Metrics for Regression:
- Mean Absolute Error (MAE)
- Mean Squared Error (MSE)
- Root Mean Squared Error (RMSE)
- R-squared (coefficient of determination)
4. Overfitting and Prevention:
- Overfitting: Model learns the noise instead of the underlying pattern.
- Prevention: Use simpler models, cross-validation, regularization.
5. Bias-Variance Tradeoff:
- Balancing error due to bias (underfitting) and variance (overfitting) to find an optimal model complexity.
6. Cross-Validation:
- Technique to assess model performance by splitting data into multiple subsets for training and validation.
7. Feature Selection Techniques:
- Filter methods (e.g., correlation analysis)
- Wrapper methods (e.g., recursive feature elimination)
- Embedded methods (e.g., Lasso regularization)
8. Assumptions of Linear Regression:
- Linearity
- Independence of errors
- Homoscedasticity (constant variance)
- No multicollinearity
9. Regularization in Linear Models:
- Adds a penalty term to the loss function to prevent overfitting by shrinking coefficients.
10. Classification vs Regression:
- Classification: Predicts a categorical outcome (e.g., class labels).
- Regression: Predicts a continuous numerical outcome (e.g., house price).
11. Dimensionality Reduction Algorithms:
- Principal Component Analysis (PCA)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
12. Decision Tree:
- Tree-like model where internal nodes represent features, branches represent decisions, and leaf nodes represent outcomes.
13. Ensemble Methods:
- Combine predictions from multiple models to improve accuracy (e.g., Random Forest, Gradient Boosting).
14. Handling Missing or Corrupted Data:
- Imputation (e.g., mean substitution)
- Removing rows or columns with missing data
- Using algorithms robust to missing values
15. Kernels in Support Vector Machines (SVM):
- Linear kernel
- Polynomial kernel
- Radial Basis Function (RBF) kernel
Data Science Interview Resources
๐๐
https://topmate.io/coding/914624
Like for more ๐
1. Machine Learning Project Lifecycle:
- Define the problem
- Gather and preprocess data
- Choose a model and train it
- Evaluate model performance
- Tune and optimize the model
- Deploy and maintain the model
2. Supervised vs Unsupervised Learning:
- Supervised Learning: Uses labeled data for training (e.g., predicting house prices from features).
- Unsupervised Learning: Uses unlabeled data to find patterns or groupings (e.g., clustering customer segments).
3. Evaluation Metrics for Regression:
- Mean Absolute Error (MAE)
- Mean Squared Error (MSE)
- Root Mean Squared Error (RMSE)
- R-squared (coefficient of determination)
4. Overfitting and Prevention:
- Overfitting: Model learns the noise instead of the underlying pattern.
- Prevention: Use simpler models, cross-validation, regularization.
5. Bias-Variance Tradeoff:
- Balancing error due to bias (underfitting) and variance (overfitting) to find an optimal model complexity.
6. Cross-Validation:
- Technique to assess model performance by splitting data into multiple subsets for training and validation.
7. Feature Selection Techniques:
- Filter methods (e.g., correlation analysis)
- Wrapper methods (e.g., recursive feature elimination)
- Embedded methods (e.g., Lasso regularization)
8. Assumptions of Linear Regression:
- Linearity
- Independence of errors
- Homoscedasticity (constant variance)
- No multicollinearity
9. Regularization in Linear Models:
- Adds a penalty term to the loss function to prevent overfitting by shrinking coefficients.
10. Classification vs Regression:
- Classification: Predicts a categorical outcome (e.g., class labels).
- Regression: Predicts a continuous numerical outcome (e.g., house price).
11. Dimensionality Reduction Algorithms:
- Principal Component Analysis (PCA)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
12. Decision Tree:
- Tree-like model where internal nodes represent features, branches represent decisions, and leaf nodes represent outcomes.
13. Ensemble Methods:
- Combine predictions from multiple models to improve accuracy (e.g., Random Forest, Gradient Boosting).
14. Handling Missing or Corrupted Data:
- Imputation (e.g., mean substitution)
- Removing rows or columns with missing data
- Using algorithms robust to missing values
15. Kernels in Support Vector Machines (SVM):
- Linear kernel
- Polynomial kernel
- Radial Basis Function (RBF) kernel
Data Science Interview Resources
๐๐
https://topmate.io/coding/914624
Like for more ๐
โค4๐ฅ1
10 Machine Learning Concepts You Must Know
1. Supervised vs Unsupervised Learning
Supervised Learning involves training a model on labeled data (input-output pairs). Examples: Linear Regression, Classification.
Unsupervised Learning deals with unlabeled data. The model tries to find hidden patterns or groupings. Examples: Clustering (K-Means), Dimensionality Reduction (PCA).
2. Bias-Variance Tradeoff
Bias is the error due to overly simplistic assumptions in the learning algorithm.
Variance is the error due to excessive sensitivity to small fluctuations in the training data.
Goal: Minimize both for optimal model performance. High bias โ underfitting; High variance โ overfitting.
3. Feature Engineering
The process of selecting, transforming, and creating variables (features) to improve model performance.
Examples: Normalization, encoding categorical variables, creating interaction terms, handling missing data.
4. Train-Test Split & Cross-Validation
Train-Test Split divides the dataset into training and testing subsets to evaluate model generalization.
Cross-Validation (e.g., k-fold) provides a more reliable evaluation by splitting data into k subsets and training/testing on each.
5. Confusion Matrix
A performance evaluation tool for classification models showing TP, TN, FP, FN.
From it, we derive:
Accuracy = (TP + TN) / Total
Precision = TP / (TP + FP)
Recall = TP / (TP + FN)
F1 Score = 2 * (Precision * Recall) / (Precision + Recall)
6. Gradient Descent
An optimization algorithm used to minimize the cost/loss function by iteratively updating model parameters in the direction of the negative gradient.
Variants: Batch GD, Stochastic GD (SGD), Mini-batch GD.
7. Regularization (L1/L2)
Techniques to prevent overfitting by adding a penalty term to the loss function.
L1 (Lasso): Adds absolute value of coefficients, can shrink some to zero (feature selection).
L2 (Ridge): Adds square of coefficients, tends to shrink but not eliminate coefficients.
8. Decision Trees & Random Forests
Decision Tree: A tree-structured model that splits data based on features. Easy to interpret.
Random Forest: An ensemble of decision trees; reduces overfitting and improves accuracy.
9. Support Vector Machines (SVM)
A supervised learning algorithm used for classification. It finds the optimal hyperplane that separates classes.
Uses kernels (linear, polynomial, RBF) to handle non-linearly separable data.
10. Neural Networks
Inspired by the human brain, these consist of layers of interconnected neurons.
Deep Neural Networks (DNNs) can model complex patterns.
The backbone of deep learning applications like image recognition, NLP, etc.
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
ENJOY LEARNING ๐๐
1. Supervised vs Unsupervised Learning
Supervised Learning involves training a model on labeled data (input-output pairs). Examples: Linear Regression, Classification.
Unsupervised Learning deals with unlabeled data. The model tries to find hidden patterns or groupings. Examples: Clustering (K-Means), Dimensionality Reduction (PCA).
2. Bias-Variance Tradeoff
Bias is the error due to overly simplistic assumptions in the learning algorithm.
Variance is the error due to excessive sensitivity to small fluctuations in the training data.
Goal: Minimize both for optimal model performance. High bias โ underfitting; High variance โ overfitting.
3. Feature Engineering
The process of selecting, transforming, and creating variables (features) to improve model performance.
Examples: Normalization, encoding categorical variables, creating interaction terms, handling missing data.
4. Train-Test Split & Cross-Validation
Train-Test Split divides the dataset into training and testing subsets to evaluate model generalization.
Cross-Validation (e.g., k-fold) provides a more reliable evaluation by splitting data into k subsets and training/testing on each.
5. Confusion Matrix
A performance evaluation tool for classification models showing TP, TN, FP, FN.
From it, we derive:
Accuracy = (TP + TN) / Total
Precision = TP / (TP + FP)
Recall = TP / (TP + FN)
F1 Score = 2 * (Precision * Recall) / (Precision + Recall)
6. Gradient Descent
An optimization algorithm used to minimize the cost/loss function by iteratively updating model parameters in the direction of the negative gradient.
Variants: Batch GD, Stochastic GD (SGD), Mini-batch GD.
7. Regularization (L1/L2)
Techniques to prevent overfitting by adding a penalty term to the loss function.
L1 (Lasso): Adds absolute value of coefficients, can shrink some to zero (feature selection).
L2 (Ridge): Adds square of coefficients, tends to shrink but not eliminate coefficients.
8. Decision Trees & Random Forests
Decision Tree: A tree-structured model that splits data based on features. Easy to interpret.
Random Forest: An ensemble of decision trees; reduces overfitting and improves accuracy.
9. Support Vector Machines (SVM)
A supervised learning algorithm used for classification. It finds the optimal hyperplane that separates classes.
Uses kernels (linear, polynomial, RBF) to handle non-linearly separable data.
10. Neural Networks
Inspired by the human brain, these consist of layers of interconnected neurons.
Deep Neural Networks (DNNs) can model complex patterns.
The backbone of deep learning applications like image recognition, NLP, etc.
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
ENJOY LEARNING ๐๐
โค5
Useful WhatsApp channels to learn AI Tools ๐ค
ChatGPT: https://whatsapp.com/channel/0029VapThS265yDAfwe97c23
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Generative AI: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
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Artificial Intelligence: https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E
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AI Studio: https://whatsapp.com/channel/0029VbAWNue1iUxjLo2DFx2U
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ChatGPT: https://whatsapp.com/channel/0029VapThS265yDAfwe97c23
OpenAI: https://whatsapp.com/channel/0029VbAbfqcLtOj7Zen5tt3o
Deepseek: https://whatsapp.com/channel/0029Vb9js9sGpLHJGIvX5g1w
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Generative AI: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
Prompt Engineering: https://whatsapp.com/channel/0029Vb6ISO1Fsn0kEemhE03b
Artificial Intelligence: https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E
Grok AI: https://whatsapp.com/channel/0029VbAU3pWChq6T5bZxUk1r
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โค3
Call for papers on AI to AI Journey* conference journal has started!
Prize for the best scientific paper - 1 million roubles!
Selected papers will be published in the scientific journal Doklady Mathematics.
๐ The journal:
โข Indexed in the largest bibliographic databases of scientific citations
โข Accessible to an international audience and published in the worldโs digital libraries
Submit your article by August 20 and get the opportunity not only to publish your research the scientific journal, but also to present it at the AI Journey conference.
Prize for the best article - 1 million roubles!
More detailed information can be found in the Selection Rules -> AI Journey
*AI Journey - a major online conference in the field of AI technologies
Prize for the best scientific paper - 1 million roubles!
Selected papers will be published in the scientific journal Doklady Mathematics.
๐ The journal:
โข Indexed in the largest bibliographic databases of scientific citations
โข Accessible to an international audience and published in the worldโs digital libraries
Submit your article by August 20 and get the opportunity not only to publish your research the scientific journal, but also to present it at the AI Journey conference.
Prize for the best article - 1 million roubles!
More detailed information can be found in the Selection Rules -> AI Journey
*AI Journey - a major online conference in the field of AI technologies
โค3
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 ๐๐
Hope this helps you ๐
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 ๐๐
Hope this helps you ๐
โค2