▎Essential Data Science Concepts Everyone Should Know:
1. Data Types and Structures:
• Categorical: Nominal (unordered, e.g., colors) and Ordinal (ordered, e.g., education levels)
• Numerical: Discrete (countable, e.g., number of children) and Continuous (measurable, e.g., height)
• Data Structures: Arrays, Lists, Dictionaries, DataFrames (for organizing and manipulating data)
2. Denoscriptive Statistics:
• Measures of Central Tendency: Mean, Median, Mode (describing the typical value)
• Measures of Dispersion: Variance, Standard Deviation, Range (describing the spread of data)
• Visualizations: Histograms, Boxplots, Scatterplots (for understanding data distribution)
3. Probability and Statistics:
• Probability Distributions: Normal, Binomial, Poisson (modeling data patterns)
• Hypothesis Testing: Formulating and testing claims about data (e.g., A/B testing)
• Confidence Intervals: Estimating the range of plausible values for a population parameter
4. Machine Learning:
• Supervised Learning: Regression (predicting continuous values) and Classification (predicting categories)
• Unsupervised Learning: Clustering (grouping similar data points) and Dimensionality Reduction (simplifying data)
• Model Evaluation: Accuracy, Precision, Recall, F1-score (assessing model performance)
5. Data Cleaning and Preprocessing:
• Missing Value Handling: Imputation, Deletion (dealing with incomplete data)
• Outlier Detection and Removal: Identifying and addressing extreme values
• Feature Engineering: Creating new features from existing ones (e.g., combining variables)
6. Data Visualization:
• Types of Charts: Bar charts, Line charts, Pie charts, Heatmaps (for communicating insights visually)
• Principles of Effective Visualization: Clarity, Accuracy, Aesthetics (for conveying information effectively)
7. Ethical Considerations in Data Science:
• Data Privacy and Security: Protecting sensitive information
• Bias and Fairness: Ensuring algorithms are unbiased and fair
8. Programming Languages and Tools:
• Python: Popular for data science with libraries like NumPy, Pandas, Scikit-learn
• R: Statistical programming language with strong visualization capabilities
• SQL: For querying and manipulating data in databases
9. Big Data and Cloud Computing:
• Hadoop and Spark: Frameworks for processing massive datasets
• Cloud Platforms: AWS, Azure, Google Cloud (for storing and analyzing data)
10. Domain Expertise:
• Understanding the Data: Knowing the context and meaning of data is crucial for effective analysis
• Problem Framing: Defining the right questions and objectives for data-driven decision making
Bonus:
• Data Storytelling: Communicating insights and findings in a clear and engaging manner
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍
1. Data Types and Structures:
• Categorical: Nominal (unordered, e.g., colors) and Ordinal (ordered, e.g., education levels)
• Numerical: Discrete (countable, e.g., number of children) and Continuous (measurable, e.g., height)
• Data Structures: Arrays, Lists, Dictionaries, DataFrames (for organizing and manipulating data)
2. Denoscriptive Statistics:
• Measures of Central Tendency: Mean, Median, Mode (describing the typical value)
• Measures of Dispersion: Variance, Standard Deviation, Range (describing the spread of data)
• Visualizations: Histograms, Boxplots, Scatterplots (for understanding data distribution)
3. Probability and Statistics:
• Probability Distributions: Normal, Binomial, Poisson (modeling data patterns)
• Hypothesis Testing: Formulating and testing claims about data (e.g., A/B testing)
• Confidence Intervals: Estimating the range of plausible values for a population parameter
4. Machine Learning:
• Supervised Learning: Regression (predicting continuous values) and Classification (predicting categories)
• Unsupervised Learning: Clustering (grouping similar data points) and Dimensionality Reduction (simplifying data)
• Model Evaluation: Accuracy, Precision, Recall, F1-score (assessing model performance)
5. Data Cleaning and Preprocessing:
• Missing Value Handling: Imputation, Deletion (dealing with incomplete data)
• Outlier Detection and Removal: Identifying and addressing extreme values
• Feature Engineering: Creating new features from existing ones (e.g., combining variables)
6. Data Visualization:
• Types of Charts: Bar charts, Line charts, Pie charts, Heatmaps (for communicating insights visually)
• Principles of Effective Visualization: Clarity, Accuracy, Aesthetics (for conveying information effectively)
7. Ethical Considerations in Data Science:
• Data Privacy and Security: Protecting sensitive information
• Bias and Fairness: Ensuring algorithms are unbiased and fair
8. Programming Languages and Tools:
• Python: Popular for data science with libraries like NumPy, Pandas, Scikit-learn
• R: Statistical programming language with strong visualization capabilities
• SQL: For querying and manipulating data in databases
9. Big Data and Cloud Computing:
• Hadoop and Spark: Frameworks for processing massive datasets
• Cloud Platforms: AWS, Azure, Google Cloud (for storing and analyzing data)
10. Domain Expertise:
• Understanding the Data: Knowing the context and meaning of data is crucial for effective analysis
• Problem Framing: Defining the right questions and objectives for data-driven decision making
Bonus:
• Data Storytelling: Communicating insights and findings in a clear and engaging manner
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍
👍15❤7
ML interview Question 📚
What is Quantization in machine learning?
Quantization the process of reducing the precision of the numbers used to represent a model's parameters, such as weights and activations. This is often done by converting 32-bit floating-point numbers (commonly used in training) to lower precision formats, like 16-bit or 8-bit integers.
Quantization is primarily used during model inference to:
1. Reduce model size: Lower precision numbers require less memory.
2. Improve computational efficiency: Operations on lower-precision data types are faster and require less power.
3. Speed up inference: Smaller models can be loaded faster, improving performance on edge devices like smartphones or IoT devices.
Quantization can lead to a small loss in model accuracy, as reducing precision can introduce rounding errors. But in many cases, the trade-off between accuracy and efficiency is worthwhile, especially for deployment on resource-constrained devices.
There are different types of quantization:
1. Post-training quantization: Applied after the model has been trained.
2.Quantization-aware training (QAT): Takes quantization into account during the training process to minimize the accuracy drop.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍
What is Quantization in machine learning?
Quantization the process of reducing the precision of the numbers used to represent a model's parameters, such as weights and activations. This is often done by converting 32-bit floating-point numbers (commonly used in training) to lower precision formats, like 16-bit or 8-bit integers.
Quantization is primarily used during model inference to:
1. Reduce model size: Lower precision numbers require less memory.
2. Improve computational efficiency: Operations on lower-precision data types are faster and require less power.
3. Speed up inference: Smaller models can be loaded faster, improving performance on edge devices like smartphones or IoT devices.
Quantization can lead to a small loss in model accuracy, as reducing precision can introduce rounding errors. But in many cases, the trade-off between accuracy and efficiency is worthwhile, especially for deployment on resource-constrained devices.
There are different types of quantization:
1. Post-training quantization: Applied after the model has been trained.
2.Quantization-aware training (QAT): Takes quantization into account during the training process to minimize the accuracy drop.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍
👍10❤5
Top ML Algorithms used by Top Tech Giants
1. Linear Regression: Simple yet powerful for predicting trends and behaviors, widely adopted across various sectors.
2. Logistic Regression: A go-to for binary classification tasks like fraud detection and customer churn, utilized by major corporations.
3. Random Forest: Renowned for its accuracy in complex decision-making processes, essential for handling multifaceted datasets.
4. Gradient Boosting Machines: Known for their precision in predictive modeling, crucial for dynamic pricing and fraud detection strategies.
5. Decision Trees: Preferred for their interpretability, ideal for customer segmentation and strategic business decisions.
6. K-Means Clustering: Effective in unsupervised learning for pattern discovery and customer segmentation.
7. Neural Networks/Deep Learning: Core technology for tasks demanding advanced image and speech recognition capabilities.
8. Support Vector Machines (SVM): Excellent for high-dimensional data analysis, particularly in image and text classification.
9. Naive Bayes: Fast and efficient, often used for text classification and sentiment analysis.
10. K-Nearest Neighbors (KNN): Best for small datasets where pattern recognition and recommendation systems are critical.
1. Linear Regression: Simple yet powerful for predicting trends and behaviors, widely adopted across various sectors.
2. Logistic Regression: A go-to for binary classification tasks like fraud detection and customer churn, utilized by major corporations.
3. Random Forest: Renowned for its accuracy in complex decision-making processes, essential for handling multifaceted datasets.
4. Gradient Boosting Machines: Known for their precision in predictive modeling, crucial for dynamic pricing and fraud detection strategies.
5. Decision Trees: Preferred for their interpretability, ideal for customer segmentation and strategic business decisions.
6. K-Means Clustering: Effective in unsupervised learning for pattern discovery and customer segmentation.
7. Neural Networks/Deep Learning: Core technology for tasks demanding advanced image and speech recognition capabilities.
8. Support Vector Machines (SVM): Excellent for high-dimensional data analysis, particularly in image and text classification.
9. Naive Bayes: Fast and efficient, often used for text classification and sentiment analysis.
10. K-Nearest Neighbors (KNN): Best for small datasets where pattern recognition and recommendation systems are critical.
👍18❤2
Data Science Tutorial for beginners
👇👇
https://www.kaggle.com/kanncaa1/data-sciencetutorial-for-beginners
👇👇
https://www.kaggle.com/kanncaa1/data-sciencetutorial-for-beginners
👍4
Some Essential tools and algorithms 👇👇
Programming Languages: Python (with libraries like NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, TensorFlow, PyTorch) and R
Data Manipulation and Analysis: SQL, Pandas, NumPy
Data Visualization: Matplotlib, Seaborn, Tableau, D3.js
Machine Learning Algorithms: Linear Regression, Logistic Regression, Decision Trees, Random Forests, Gradient Boosting, SVM, K-means, KNN, Neural Networks
Cloud Platforms: AWS, GCP, Azure
Programming Languages: Python (with libraries like NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, TensorFlow, PyTorch) and R
Data Manipulation and Analysis: SQL, Pandas, NumPy
Data Visualization: Matplotlib, Seaborn, Tableau, D3.js
Machine Learning Algorithms: Linear Regression, Logistic Regression, Decision Trees, Random Forests, Gradient Boosting, SVM, K-means, KNN, Neural Networks
Cloud Platforms: AWS, GCP, Azure
👍15❤1
Common Machine Learning Algorithms!
1️⃣ Linear Regression
->Used for predicting continuous values.
->Models the relationship between dependent and independent variables by fitting a linear equation.
2️⃣ Logistic Regression
->Ideal for binary classification problems.
->Estimates the probability that an instance belongs to a particular class.
3️⃣ Decision Trees
->Splits data into subsets based on the value of input features.
->Easy to visualize and interpret but can be prone to overfitting.
4️⃣ Random Forest
->An ensemble method using multiple decision trees.
->Reduces overfitting and improves accuracy by averaging multiple trees.
5️⃣ Support Vector Machines (SVM)
->Finds the hyperplane that best separates different classes.
->Effective in high-dimensional spaces and for classification tasks.
6️⃣ k-Nearest Neighbors (k-NN)
->Classifies data based on the majority class among the k-nearest neighbors.
->Simple and intuitive but can be computationally intensive.
7️⃣ K-Means Clustering
->Partitions data into k clusters based on feature similarity.
->Useful for market segmentation, image compression, and more.
8️⃣ Naive Bayes
->Based on Bayes' theorem with an assumption of independence among predictors.
->Particularly useful for text classification and spam filtering.
9️⃣ Neural Networks
->Mimic the human brain to identify patterns in data.
->Power deep learning applications, from image recognition to natural language processing.
🔟 Gradient Boosting Machines (GBM)
->Combines weak learners to create a strong predictive model.
->Used in various applications like ranking, classification, and regression.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍
1️⃣ Linear Regression
->Used for predicting continuous values.
->Models the relationship between dependent and independent variables by fitting a linear equation.
2️⃣ Logistic Regression
->Ideal for binary classification problems.
->Estimates the probability that an instance belongs to a particular class.
3️⃣ Decision Trees
->Splits data into subsets based on the value of input features.
->Easy to visualize and interpret but can be prone to overfitting.
4️⃣ Random Forest
->An ensemble method using multiple decision trees.
->Reduces overfitting and improves accuracy by averaging multiple trees.
5️⃣ Support Vector Machines (SVM)
->Finds the hyperplane that best separates different classes.
->Effective in high-dimensional spaces and for classification tasks.
6️⃣ k-Nearest Neighbors (k-NN)
->Classifies data based on the majority class among the k-nearest neighbors.
->Simple and intuitive but can be computationally intensive.
7️⃣ K-Means Clustering
->Partitions data into k clusters based on feature similarity.
->Useful for market segmentation, image compression, and more.
8️⃣ Naive Bayes
->Based on Bayes' theorem with an assumption of independence among predictors.
->Particularly useful for text classification and spam filtering.
9️⃣ Neural Networks
->Mimic the human brain to identify patterns in data.
->Power deep learning applications, from image recognition to natural language processing.
🔟 Gradient Boosting Machines (GBM)
->Combines weak learners to create a strong predictive model.
->Used in various applications like ranking, classification, and regression.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍
❤14👍9
Complete Data Science Roadmap
👇👇
1. Introduction to Data Science
- Overview and Importance
- Data Science Lifecycle
- Key Roles (Data Scientist, Analyst, Engineer)
2. Mathematics and Statistics
- Probability and Distributions
- Denoscriptive/Inferential Statistics
- Hypothesis Testing
- Linear Algebra and Calculus Basics
3. Programming Languages
- Python: NumPy, Pandas, Matplotlib
- R: dplyr, ggplot2
- SQL: Joins, Aggregations, CRUD
4. Data Collection & Preprocessing
- Data Cleaning and Wrangling
- Handling Missing Data
- Feature Engineering
5. Exploratory Data Analysis (EDA)
- Summary Statistics
- Data Visualization (Histograms, Box Plots, Correlation)
6. Machine Learning
- Supervised (Linear/Logistic Regression, Decision Trees)
- Unsupervised (K-Means, PCA)
- Model Selection and Cross-Validation
7. Advanced Machine Learning
- SVM, Random Forests, Boosting
- Neural Networks Basics
8. Deep Learning
- Neural Networks Architecture
- CNNs for Image Data
- RNNs for Sequential Data
9. Natural Language Processing (NLP)
- Text Preprocessing
- Sentiment Analysis
- Word Embeddings (Word2Vec)
10. Data Visualization & Storytelling
- Dashboards (Tableau, Power BI)
- Telling Stories with Data
11. Model Deployment
- Deploy with Flask or Django
- Monitoring and Retraining Models
12. Big Data & Cloud
- Introduction to Hadoop, Spark
- Cloud Tools (AWS, Google Cloud)
13. Data Engineering Basics
- ETL Pipelines
- Data Warehousing (Redshift, BigQuery)
14. Ethics in Data Science
- Ethical Data Usage
- Bias in AI Models
15. Tools for Data Science
- Jupyter, Git, Docker
16. Career Path & Certifications
- Building a Data Science Portfolio
Free Notes & Books to learn Data Science: https://news.1rj.ru/str/datasciencefree
Python Project Ideas: https://news.1rj.ru/str/dsabooks/85
Best Resources to learn Data Science 👇👇
Python Tutorial
Data Science Course by Kaggle
Machine Learning Course by Google
Best Data Science & Machine Learning Resources
Interview Process for Data Science Role at Amazon
Python Interview Resources
Join @free4unow_backup for more free courses
Like for more ❤️
ENJOY LEARNING👍👍
👇👇
1. Introduction to Data Science
- Overview and Importance
- Data Science Lifecycle
- Key Roles (Data Scientist, Analyst, Engineer)
2. Mathematics and Statistics
- Probability and Distributions
- Denoscriptive/Inferential Statistics
- Hypothesis Testing
- Linear Algebra and Calculus Basics
3. Programming Languages
- Python: NumPy, Pandas, Matplotlib
- R: dplyr, ggplot2
- SQL: Joins, Aggregations, CRUD
4. Data Collection & Preprocessing
- Data Cleaning and Wrangling
- Handling Missing Data
- Feature Engineering
5. Exploratory Data Analysis (EDA)
- Summary Statistics
- Data Visualization (Histograms, Box Plots, Correlation)
6. Machine Learning
- Supervised (Linear/Logistic Regression, Decision Trees)
- Unsupervised (K-Means, PCA)
- Model Selection and Cross-Validation
7. Advanced Machine Learning
- SVM, Random Forests, Boosting
- Neural Networks Basics
8. Deep Learning
- Neural Networks Architecture
- CNNs for Image Data
- RNNs for Sequential Data
9. Natural Language Processing (NLP)
- Text Preprocessing
- Sentiment Analysis
- Word Embeddings (Word2Vec)
10. Data Visualization & Storytelling
- Dashboards (Tableau, Power BI)
- Telling Stories with Data
11. Model Deployment
- Deploy with Flask or Django
- Monitoring and Retraining Models
12. Big Data & Cloud
- Introduction to Hadoop, Spark
- Cloud Tools (AWS, Google Cloud)
13. Data Engineering Basics
- ETL Pipelines
- Data Warehousing (Redshift, BigQuery)
14. Ethics in Data Science
- Ethical Data Usage
- Bias in AI Models
15. Tools for Data Science
- Jupyter, Git, Docker
16. Career Path & Certifications
- Building a Data Science Portfolio
Free Notes & Books to learn Data Science: https://news.1rj.ru/str/datasciencefree
Python Project Ideas: https://news.1rj.ru/str/dsabooks/85
Best Resources to learn Data Science 👇👇
Python Tutorial
Data Science Course by Kaggle
Machine Learning Course by Google
Best Data Science & Machine Learning Resources
Interview Process for Data Science Role at Amazon
Python Interview Resources
Join @free4unow_backup for more free courses
Like for more ❤️
ENJOY LEARNING👍👍
👍15👌5❤4👎1
Finance is one of the highest paid domains for Data Science jobs.
Here’s a complete step by step roadmap to learn Data Science for Finance 👇👇
Step 1: Understand the fundamentals of finance
Step 2: Learn essential programming languages and tools
Step 3: Learn the fundamentals of statistics for Data Science
Step 4: Learn Data Manipulation, Analysis, and Visualization
Step 5: Dive deep into Data Science and Machine Learning Algorithms
Step 6: Learn to work with Financial Data
Here’s a complete step by step roadmap to learn Data Science for Finance 👇👇
Step 1: Understand the fundamentals of finance
Step 2: Learn essential programming languages and tools
Step 3: Learn the fundamentals of statistics for Data Science
Step 4: Learn Data Manipulation, Analysis, and Visualization
Step 5: Dive deep into Data Science and Machine Learning Algorithms
Step 6: Learn to work with Financial Data
👍8❤1
Free Courses with Certificate
👇👇
https://news.1rj.ru/str/free4unow_backup
Best Telegram channels to get free coding & data science resources
👇👇
https://news.1rj.ru/str/addlist/4q2PYC0pH_VjZDk5
👇👇
https://news.1rj.ru/str/free4unow_backup
Best Telegram channels to get free coding & data science resources
👇👇
https://news.1rj.ru/str/addlist/4q2PYC0pH_VjZDk5
Telegram
Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT Expert
We provide unlimited Free Courses with Certificate to learn Python, Data Science, Java, Web development, AI, ML, Finance, Hacking, Marketing and many more from top websites.
For promotions: @love_data
For promotions: @love_data
👍6😁1
Forwarded from Jobs | Internships | Placement | Interviews
Data Analyst Jobs.pdf
112.2 KB
🏆 Data Analyst Jobs ✅
👉🏻 DO REACT IF YOU WANT MORE CONTENT LIKE THIS FOR FREE 🆓
👉🏻 DO REACT IF YOU WANT MORE CONTENT LIKE THIS FOR FREE 🆓
👍34❤7
Learn Data Science in 2024
𝟭. 𝗔𝗽𝗽𝗹𝘆 𝗣𝗮𝗿𝗲𝘁𝗼'𝘀 𝗟𝗮𝘄 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗝𝘂𝘀𝘁 𝗘𝗻𝗼𝘂𝗴𝗵 📚
Pareto's Law states that "that 80% of consequences come from 20% of the causes".
This law should serve as a guiding framework for the volume of content you need to know to be proficient in data science.
Often rookies make the mistake of overspending their time learning algorithms that are rarely applied in production. Learning about advanced algorithms such as XLNet, Bayesian SVD++, and BiLSTMs, are cool to learn.
But, in reality, you will rarely apply such algorithms in production (unless your job demands research and application of state-of-the-art algos).
For most ML applications in production - especially in the MVP phase, simple algos like logistic regression, K-Means, random forest, and XGBoost provide the biggest bang for the buck because of their simplicity in training, interpretation and productionization.
So, invest more time learning topics that provide immediate value now, not a year later.
𝟮. 𝗙𝗶𝗻𝗱 𝗮 𝗠𝗲𝗻𝘁𝗼𝗿 ⚡
There’s a Japanese proverb that says “Better than a thousand days of diligent study is one day with a great teacher.” This proverb directly applies to learning data science quickly.
Mentors can teach you about how to build a model in production and how to manage stakeholders - stuff that you don’t often read about in courses and books.
So, find a mentor who can teach you practical knowledge in data science.
𝟯. 𝗗𝗲𝗹𝗶𝗯𝗲𝗿𝗮𝘁𝗲 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 ✍️
If you are serious about growing your excelling in data science, you have to put in the time to nurture your knowledge. This means that you need to spend less time watching mindless videos on TikTok and spend more time reading books and watching video lectures.
Join @datasciencefree for more
ENJOY LEARNING 👍👍
𝟭. 𝗔𝗽𝗽𝗹𝘆 𝗣𝗮𝗿𝗲𝘁𝗼'𝘀 𝗟𝗮𝘄 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗝𝘂𝘀𝘁 𝗘𝗻𝗼𝘂𝗴𝗵 📚
Pareto's Law states that "that 80% of consequences come from 20% of the causes".
This law should serve as a guiding framework for the volume of content you need to know to be proficient in data science.
Often rookies make the mistake of overspending their time learning algorithms that are rarely applied in production. Learning about advanced algorithms such as XLNet, Bayesian SVD++, and BiLSTMs, are cool to learn.
But, in reality, you will rarely apply such algorithms in production (unless your job demands research and application of state-of-the-art algos).
For most ML applications in production - especially in the MVP phase, simple algos like logistic regression, K-Means, random forest, and XGBoost provide the biggest bang for the buck because of their simplicity in training, interpretation and productionization.
So, invest more time learning topics that provide immediate value now, not a year later.
𝟮. 𝗙𝗶𝗻𝗱 𝗮 𝗠𝗲𝗻𝘁𝗼𝗿 ⚡
There’s a Japanese proverb that says “Better than a thousand days of diligent study is one day with a great teacher.” This proverb directly applies to learning data science quickly.
Mentors can teach you about how to build a model in production and how to manage stakeholders - stuff that you don’t often read about in courses and books.
So, find a mentor who can teach you practical knowledge in data science.
𝟯. 𝗗𝗲𝗹𝗶𝗯𝗲𝗿𝗮𝘁𝗲 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 ✍️
If you are serious about growing your excelling in data science, you have to put in the time to nurture your knowledge. This means that you need to spend less time watching mindless videos on TikTok and spend more time reading books and watching video lectures.
Join @datasciencefree for more
ENJOY LEARNING 👍👍
👍15❤2👌1
Top 10 Python Libraries for Data Science & Machine Learning
1. NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.
2. Pandas: Pandas is a powerful data manipulation library that provides data structures like DataFrame and Series, which make it easy to work with structured data. It offers tools for data cleaning, reshaping, merging, and slicing data.
3. Matplotlib: Matplotlib is a plotting library for creating static, interactive, and animated visualizations in Python. It allows you to generate various types of plots, including line plots, bar charts, histograms, scatter plots, and more.
4. Scikit-learn: Scikit-learn is a machine learning library that provides simple and efficient tools for data mining and data analysis. It includes a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and model selection.
5. TensorFlow: TensorFlow is an open-source machine learning framework developed by Google. It enables you to build and train deep learning models using high-level APIs and tools for neural networks, natural language processing, computer vision, and more.
6. Keras: Keras is a high-level neural networks API that runs on top of TensorFlow, Theano, or Microsoft Cognitive Toolkit. It allows you to quickly prototype deep learning models with minimal code and easily experiment with different architectures.
7. Seaborn: Seaborn is a data visualization library based on Matplotlib that provides a high-level interface for creating attractive and informative statistical graphics. It simplifies the process of creating complex visualizations like heatmaps, violin plots, and pair plots.
8. Statsmodels: Statsmodels is a library that focuses on statistical modeling and hypothesis testing in Python. It offers a wide range of statistical models, including linear regression, logistic regression, time series analysis, and more.
9. XGBoost: XGBoost is an optimized gradient boosting library that provides an efficient implementation of the gradient boosting algorithm. It is widely used in machine learning competitions and has become a popular choice for building accurate predictive models.
10. NLTK (Natural Language Toolkit): NLTK is a library for natural language processing (NLP) that provides tools for text processing, tokenization, part-of-speech tagging, named entity recognition, sentiment analysis, and more. It is a valuable resource for working with textual data in data science projects.
Data Science Resources for Beginners
👇👇
https://drive.google.com/drive/folders/1uCShXgmol-fGMqeF2hf9xA5XPKVSxeTo
Share with credits: https://news.1rj.ru/str/datasciencefun
ENJOY LEARNING 👍👍
1. NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.
2. Pandas: Pandas is a powerful data manipulation library that provides data structures like DataFrame and Series, which make it easy to work with structured data. It offers tools for data cleaning, reshaping, merging, and slicing data.
3. Matplotlib: Matplotlib is a plotting library for creating static, interactive, and animated visualizations in Python. It allows you to generate various types of plots, including line plots, bar charts, histograms, scatter plots, and more.
4. Scikit-learn: Scikit-learn is a machine learning library that provides simple and efficient tools for data mining and data analysis. It includes a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and model selection.
5. TensorFlow: TensorFlow is an open-source machine learning framework developed by Google. It enables you to build and train deep learning models using high-level APIs and tools for neural networks, natural language processing, computer vision, and more.
6. Keras: Keras is a high-level neural networks API that runs on top of TensorFlow, Theano, or Microsoft Cognitive Toolkit. It allows you to quickly prototype deep learning models with minimal code and easily experiment with different architectures.
7. Seaborn: Seaborn is a data visualization library based on Matplotlib that provides a high-level interface for creating attractive and informative statistical graphics. It simplifies the process of creating complex visualizations like heatmaps, violin plots, and pair plots.
8. Statsmodels: Statsmodels is a library that focuses on statistical modeling and hypothesis testing in Python. It offers a wide range of statistical models, including linear regression, logistic regression, time series analysis, and more.
9. XGBoost: XGBoost is an optimized gradient boosting library that provides an efficient implementation of the gradient boosting algorithm. It is widely used in machine learning competitions and has become a popular choice for building accurate predictive models.
10. NLTK (Natural Language Toolkit): NLTK is a library for natural language processing (NLP) that provides tools for text processing, tokenization, part-of-speech tagging, named entity recognition, sentiment analysis, and more. It is a valuable resource for working with textual data in data science projects.
Data Science Resources for Beginners
👇👇
https://drive.google.com/drive/folders/1uCShXgmol-fGMqeF2hf9xA5XPKVSxeTo
Share with credits: https://news.1rj.ru/str/datasciencefun
ENJOY LEARNING 👍👍
👍23❤4
I have curated the list of best WhatsApp channels to learn coding & data science for FREE
Free Courses with Certificate: Free Courses With Certificate | WhatsApp Channel (https://whatsapp.com/channel/0029Vamhzk5JENy1Zg9KmO2g)
Jobs & Internship Opportunities:
https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
Web Development: Web Development | WhatsApp Channel (https://whatsapp.com/channel/0029VaiSdWu4NVis9yNEE72z)
Python Free Books & Projects: Python Programming | WhatsApp Channel (https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L)
Java Resources: Java Coding | WhatsApp Channel (https://whatsapp.com/channel/0029VamdH5mHAdNMHMSBwg1s)
Coding Interviews: Coding Interview | WhatsApp Channel (https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X)
SQL: SQL For Data Analysis | WhatsApp Channel (https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v)
Power BI: Power BI | WhatsApp Channel (https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c)
Programming Free Resources: Programming Resources | WhatsApp Channel (https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17)
Data Science Projects: Data Science Projects | WhatsApp Channel (https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y)
Learn Data Science & Machine Learning: Data Science and Machine Learning | WhatsApp Channel (https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D)
ENJOY LEARNING 👍👍
Free Courses with Certificate: Free Courses With Certificate | WhatsApp Channel (https://whatsapp.com/channel/0029Vamhzk5JENy1Zg9KmO2g)
Jobs & Internship Opportunities:
https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
Web Development: Web Development | WhatsApp Channel (https://whatsapp.com/channel/0029VaiSdWu4NVis9yNEE72z)
Python Free Books & Projects: Python Programming | WhatsApp Channel (https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L)
Java Resources: Java Coding | WhatsApp Channel (https://whatsapp.com/channel/0029VamdH5mHAdNMHMSBwg1s)
Coding Interviews: Coding Interview | WhatsApp Channel (https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X)
SQL: SQL For Data Analysis | WhatsApp Channel (https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v)
Power BI: Power BI | WhatsApp Channel (https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c)
Programming Free Resources: Programming Resources | WhatsApp Channel (https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17)
Data Science Projects: Data Science Projects | WhatsApp Channel (https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y)
Learn Data Science & Machine Learning: Data Science and Machine Learning | WhatsApp Channel (https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D)
ENJOY LEARNING 👍👍
👍9❤4
Introduction to Data Science: Complete Guide for Beginners
👇👇
https://medium.com/@data_analyst/introduction-to-data-science-complete-guide-for-beginners-af0517923d61
Like for more ❤️
👇👇
https://medium.com/@data_analyst/introduction-to-data-science-complete-guide-for-beginners-af0517923d61
Like for more ❤️
❤8
▎Essential Data Science Concepts Everyone Should Know:
1. Data Types and Structures:
• Categorical: Nominal (unordered, e.g., colors) and Ordinal (ordered, e.g., education levels)
• Numerical: Discrete (countable, e.g., number of children) and Continuous (measurable, e.g., height)
• Data Structures: Arrays, Lists, Dictionaries, DataFrames (for organizing and manipulating data)
2. Denoscriptive Statistics:
• Measures of Central Tendency: Mean, Median, Mode (describing the typical value)
• Measures of Dispersion: Variance, Standard Deviation, Range (describing the spread of data)
• Visualizations: Histograms, Boxplots, Scatterplots (for understanding data distribution)
3. Probability and Statistics:
• Probability Distributions: Normal, Binomial, Poisson (modeling data patterns)
• Hypothesis Testing: Formulating and testing claims about data (e.g., A/B testing)
• Confidence Intervals: Estimating the range of plausible values for a population parameter
4. Machine Learning:
• Supervised Learning: Regression (predicting continuous values) and Classification (predicting categories)
• Unsupervised Learning: Clustering (grouping similar data points) and Dimensionality Reduction (simplifying data)
• Model Evaluation: Accuracy, Precision, Recall, F1-score (assessing model performance)
5. Data Cleaning and Preprocessing:
• Missing Value Handling: Imputation, Deletion (dealing with incomplete data)
• Outlier Detection and Removal: Identifying and addressing extreme values
• Feature Engineering: Creating new features from existing ones (e.g., combining variables)
6. Data Visualization:
• Types of Charts: Bar charts, Line charts, Pie charts, Heatmaps (for communicating insights visually)
• Principles of Effective Visualization: Clarity, Accuracy, Aesthetics (for conveying information effectively)
7. Ethical Considerations in Data Science:
• Data Privacy and Security: Protecting sensitive information
• Bias and Fairness: Ensuring algorithms are unbiased and fair
8. Programming Languages and Tools:
• Python: Popular for data science with libraries like NumPy, Pandas, Scikit-learn
• R: Statistical programming language with strong visualization capabilities
• SQL: For querying and manipulating data in databases
9. Big Data and Cloud Computing:
• Hadoop and Spark: Frameworks for processing massive datasets
• Cloud Platforms: AWS, Azure, Google Cloud (for storing and analyzing data)
10. Domain Expertise:
• Understanding the Data: Knowing the context and meaning of data is crucial for effective analysis
• Problem Framing: Defining the right questions and objectives for data-driven decision making
Bonus:
• Data Storytelling: Communicating insights and findings in a clear and engaging manner
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍
1. Data Types and Structures:
• Categorical: Nominal (unordered, e.g., colors) and Ordinal (ordered, e.g., education levels)
• Numerical: Discrete (countable, e.g., number of children) and Continuous (measurable, e.g., height)
• Data Structures: Arrays, Lists, Dictionaries, DataFrames (for organizing and manipulating data)
2. Denoscriptive Statistics:
• Measures of Central Tendency: Mean, Median, Mode (describing the typical value)
• Measures of Dispersion: Variance, Standard Deviation, Range (describing the spread of data)
• Visualizations: Histograms, Boxplots, Scatterplots (for understanding data distribution)
3. Probability and Statistics:
• Probability Distributions: Normal, Binomial, Poisson (modeling data patterns)
• Hypothesis Testing: Formulating and testing claims about data (e.g., A/B testing)
• Confidence Intervals: Estimating the range of plausible values for a population parameter
4. Machine Learning:
• Supervised Learning: Regression (predicting continuous values) and Classification (predicting categories)
• Unsupervised Learning: Clustering (grouping similar data points) and Dimensionality Reduction (simplifying data)
• Model Evaluation: Accuracy, Precision, Recall, F1-score (assessing model performance)
5. Data Cleaning and Preprocessing:
• Missing Value Handling: Imputation, Deletion (dealing with incomplete data)
• Outlier Detection and Removal: Identifying and addressing extreme values
• Feature Engineering: Creating new features from existing ones (e.g., combining variables)
6. Data Visualization:
• Types of Charts: Bar charts, Line charts, Pie charts, Heatmaps (for communicating insights visually)
• Principles of Effective Visualization: Clarity, Accuracy, Aesthetics (for conveying information effectively)
7. Ethical Considerations in Data Science:
• Data Privacy and Security: Protecting sensitive information
• Bias and Fairness: Ensuring algorithms are unbiased and fair
8. Programming Languages and Tools:
• Python: Popular for data science with libraries like NumPy, Pandas, Scikit-learn
• R: Statistical programming language with strong visualization capabilities
• SQL: For querying and manipulating data in databases
9. Big Data and Cloud Computing:
• Hadoop and Spark: Frameworks for processing massive datasets
• Cloud Platforms: AWS, Azure, Google Cloud (for storing and analyzing data)
10. Domain Expertise:
• Understanding the Data: Knowing the context and meaning of data is crucial for effective analysis
• Problem Framing: Defining the right questions and objectives for data-driven decision making
Bonus:
• Data Storytelling: Communicating insights and findings in a clear and engaging manner
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍
❤11👍10
Here are 25 most common Deep Learning interview questions for ML research positions:
Fundamentals:
- What is deep learning, and how does it differ from traditional machine learning?
- What is an activation function, and why is it important? Explain three types of activation functions.
- You are using a deep neural network for prediction, but it overfits the training data. What can you do to reduce overfitting?
- What is the vanishing gradient problem in neural networks, and how can it be fixed?
- Explain the process of backpropagation.
Neural Network Architectures:
- Describe the architecture of a typical Convolutional Neural Network (CNN).
- What are Autoencoders, and what are three practical uses of them?
- What is a transformer architecture, and how is it used in NLP tasks?
- What is the role of pooling layers in CNNs?
- What are Recurrent Neural Networks (RNNs), and where are they used?
Training and Optimization:
- How does L1/L2 regularization affect a neural network?
- Why should we use Batch Normalization?
- How do you know if your model is suffering from exploding gradients?
- What is the purpose of dropout in neural networks, and how does it affect training?
- What are some hyperparameters used in training neural networks?
Advanced Topics:
- What are the main gates in LSTM networks, and what are their tasks?
- Explain how self-attention works in transformers.
- Can CNNs be used to classify 1D signals?
- What is transfer learning, and when is it recommended or not?
- How do depthwise separable convolutions improve CNNs?
Practical Implementation:
- Describe the process of pre-training and fine-tuning in transformers.
- What are the main challenges when training a deep learning model with limited data?
- How do you handle class imbalance in deep learning?
- What are the challenges of deploying deep learning models in production?
- How would you modify a pre-trained model from classification to regression?
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍
Fundamentals:
- What is deep learning, and how does it differ from traditional machine learning?
- What is an activation function, and why is it important? Explain three types of activation functions.
- You are using a deep neural network for prediction, but it overfits the training data. What can you do to reduce overfitting?
- What is the vanishing gradient problem in neural networks, and how can it be fixed?
- Explain the process of backpropagation.
Neural Network Architectures:
- Describe the architecture of a typical Convolutional Neural Network (CNN).
- What are Autoencoders, and what are three practical uses of them?
- What is a transformer architecture, and how is it used in NLP tasks?
- What is the role of pooling layers in CNNs?
- What are Recurrent Neural Networks (RNNs), and where are they used?
Training and Optimization:
- How does L1/L2 regularization affect a neural network?
- Why should we use Batch Normalization?
- How do you know if your model is suffering from exploding gradients?
- What is the purpose of dropout in neural networks, and how does it affect training?
- What are some hyperparameters used in training neural networks?
Advanced Topics:
- What are the main gates in LSTM networks, and what are their tasks?
- Explain how self-attention works in transformers.
- Can CNNs be used to classify 1D signals?
- What is transfer learning, and when is it recommended or not?
- How do depthwise separable convolutions improve CNNs?
Practical Implementation:
- Describe the process of pre-training and fine-tuning in transformers.
- What are the main challenges when training a deep learning model with limited data?
- How do you handle class imbalance in deep learning?
- What are the challenges of deploying deep learning models in production?
- How would you modify a pre-trained model from classification to regression?
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍
👍8🥰1
9 tips to improve your code:
- Declare variables close to usage
- Functions do 1 thing
- Avoid long functions
- Avoid long lines
- Don't repeat code
- Use denoscriptive variable/function names
- Use few arguments
- Simplify conditions (return age >17;)
- Remove unused code
Without errors, No-one can become a good programmer.
Errors are the most important phase of learning to code.
#coding
- Declare variables close to usage
- Functions do 1 thing
- Avoid long functions
- Avoid long lines
- Don't repeat code
- Use denoscriptive variable/function names
- Use few arguments
- Simplify conditions (return age >17;)
- Remove unused code
Without errors, No-one can become a good programmer.
Errors are the most important phase of learning to code.
#coding
👍14❤3