Forwarded from Artificial Intelligence
๐ฑ ๐ฅ๐ฒ๐ฎ๐น-๐ช๐ผ๐ฟ๐น๐ฑ ๐ง๐ฒ๐ฐ๐ต ๐ฃ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐๐ ๐๐ผ ๐๐๐ถ๐น๐ฑ ๐ฌ๐ผ๐๐ฟ ๐ฅ๐ฒ๐๐๐บ๐ฒ โ ๐ช๐ถ๐๐ต ๐๐๐น๐น ๐ง๐๐๐ผ๐ฟ๐ถ๐ฎ๐น๐!๐
Are you ready to build real-world tech projects that donโt just look good on your resume, but actually teach you practical, job-ready skills?๐งโ๐ป๐
Hereโs a curated list of 5 high-value development tutorials โ covering everything from full-stack development and real-time chat apps to AI form builders and reinforcement learningโจ๏ธ๐ป
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3UtCSLO
Theyโre real, portfolio-worthy projects you can start todayโ ๏ธ
Are you ready to build real-world tech projects that donโt just look good on your resume, but actually teach you practical, job-ready skills?๐งโ๐ป๐
Hereโs a curated list of 5 high-value development tutorials โ covering everything from full-stack development and real-time chat apps to AI form builders and reinforcement learningโจ๏ธ๐ป
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3UtCSLO
Theyโre real, portfolio-worthy projects you can start todayโ ๏ธ
โค1
Forwarded from Artificial Intelligence
๐ฏ ๐๐ฟ๐ฒ๐ฒ ๐ฆ๐ค๐ ๐ฌ๐ผ๐๐ง๐๐ฏ๐ฒ ๐ฃ๐น๐ฎ๐๐น๐ถ๐๐๐ ๐ง๐ต๐ฎ๐ ๐ช๐ถ๐น๐น ๐ ๐ฎ๐ธ๐ฒ ๐ฌ๐ผ๐ ๐ฎ ๐ค๐๐ฒ๐ฟ๐ ๐ฃ๐ฟ๐ผ ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฑ๐
Still stuck Googling โWhat is SQL?โ every time you start a new project?๐ต
Youโre not alone. Many beginners bounce between tutorials without ever feeling confident writing SQL queries on their own.๐จโ๐ปโจ๏ธ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4f1F6LU
Letโs dive into the ones that are actually worth your timeโ ๏ธ
Still stuck Googling โWhat is SQL?โ every time you start a new project?๐ต
Youโre not alone. Many beginners bounce between tutorials without ever feeling confident writing SQL queries on their own.๐จโ๐ปโจ๏ธ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4f1F6LU
Letโs dive into the ones that are actually worth your timeโ ๏ธ
โค1
Top 10 Data Science Concepts You Should Know ๐ง
1. Data Cleaning: Garbage In, Garbage Out. You can't build great models on messy data. Learn to spot and fix errors before you start. Seriously, this is the most important step.
2. EDA: Your Data's Secret Diary. Before you build anything, EXPLORE! Understand your data's quirks, distributions, and relationships. Visualizations are your best friend here.
3. Feature Engineering: Turning Data into Gold. Raw data is often useless. Feature engineering is how you transform it into something your models can actually learn from. Think about what the data represents.
4. Machine Learning: The Right Tool for the Job. Don't just throw algorithms at problems. Understand why you're using linear regression vs. a random forest.
5. Model Validation: Are You Lying to Yourself? Too many people build models that look great on paper but fail in the real world. Rigorous validation is essential.
6. Feature Selection: Less Can Be More. Get rid of the noise! Focusing on the most important features improves performance and interpretability.
7. Dimensionality Reduction: Simplify, Simplify, Simplify. High-dimensional data can be a nightmare. Learn techniques to reduce complexity without losing valuable information.
8. Model Optimization: Squeeze Every Last Drop. Fine-tuning your model parameters can make a huge difference. But be careful not to overfit!
9. Data Visualization: Tell a Story People Understand. Don't just dump charts on a page. Craft a narrative that highlights key insights.
10. Big Data: When Things Get Serious. If you're dealing with massive datasets, you'll need specialized tools like Hadoop and Spark. But don't start here! Master the fundamentals first.
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
1. Data Cleaning: Garbage In, Garbage Out. You can't build great models on messy data. Learn to spot and fix errors before you start. Seriously, this is the most important step.
2. EDA: Your Data's Secret Diary. Before you build anything, EXPLORE! Understand your data's quirks, distributions, and relationships. Visualizations are your best friend here.
3. Feature Engineering: Turning Data into Gold. Raw data is often useless. Feature engineering is how you transform it into something your models can actually learn from. Think about what the data represents.
4. Machine Learning: The Right Tool for the Job. Don't just throw algorithms at problems. Understand why you're using linear regression vs. a random forest.
5. Model Validation: Are You Lying to Yourself? Too many people build models that look great on paper but fail in the real world. Rigorous validation is essential.
6. Feature Selection: Less Can Be More. Get rid of the noise! Focusing on the most important features improves performance and interpretability.
7. Dimensionality Reduction: Simplify, Simplify, Simplify. High-dimensional data can be a nightmare. Learn techniques to reduce complexity without losing valuable information.
8. Model Optimization: Squeeze Every Last Drop. Fine-tuning your model parameters can make a huge difference. But be careful not to overfit!
9. Data Visualization: Tell a Story People Understand. Don't just dump charts on a page. Craft a narrative that highlights key insights.
10. Big Data: When Things Get Serious. If you're dealing with massive datasets, you'll need specialized tools like Hadoop and Spark. But don't start here! Master the fundamentals first.
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
โค2๐ฑ1
Forwarded from Python Projects & Resources
๐๐ฑ ๐๐ฅ๐๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐ง๐ผ ๐๐ผ๐ผ๐๐ ๐ฌ๐ผ๐๐ฟ ๐ง๐ฒ๐ฐ๐ต ๐๐ฎ๐ฟ๐ฒ๐ฒ๐ฟ! ๐
Upgrade your skills and earn industry-recognized certificates โ 100% FREE!
โ Big Data Analytics โ https://pdlink.in/4nzRoza
โ AI & ML โ https://pdlink.in/401SWry
โ Cloud Computing โ https://pdlink.in/3U2sMkR
โ Cyber Security โ https://pdlink.in/4nzQaDQ
โ Other Tech Courses โ https://pdlink.in/4lIN673
๐ฏ Enroll Now & Get Certified for FREE
Upgrade your skills and earn industry-recognized certificates โ 100% FREE!
โ Big Data Analytics โ https://pdlink.in/4nzRoza
โ AI & ML โ https://pdlink.in/401SWry
โ Cloud Computing โ https://pdlink.in/3U2sMkR
โ Cyber Security โ https://pdlink.in/4nzQaDQ
โ Other Tech Courses โ https://pdlink.in/4lIN673
๐ฏ Enroll Now & Get Certified for FREE
30-days learning plan to cover data science fundamental algorithms, important concepts, and practical applications ๐๐
### Week 1: Introduction and Basics
Day 1: Introduction to Data Science
- Overview of data science, its importance, and key concepts.
Day 2: Python Basics for Data Science
- Python syntax, variables, data types, and basic operations.
Day 3: Data Structures in Python
- Lists, dictionaries, sets, and tuples.
Day 4: Data Manipulation with Pandas
- Introduction to Pandas, Series, DataFrame, basic operations.
Day 5: Data Visualization with Matplotlib and Seaborn
- Creating basic plots (line, bar, scatter), customizing plots.
Day 6: Introduction to Numpy
- Arrays, array operations, mathematical functions.
Day 7: Data Cleaning and Preprocessing
- Handling missing values, data normalization, and scaling.
### Week 2: Exploratory Data Analysis and Statistical Foundations
Day 8: Exploratory Data Analysis (EDA)
- Techniques for summarizing and visualizing data.
Day 9: Probability and Statistics Basics
- Descriptive statistics, probability distributions, and hypothesis testing.
Day 10: Introduction to SQL for Data Science
- Basic SQL commands for data retrieval and manipulation.
Day 11: Linear Regression
- Concept, assumptions, implementation, and evaluation metrics (R-squared, RMSE).
Day 12: Logistic Regression
- Concept, implementation, and evaluation metrics (confusion matrix, ROC-AUC).
Day 13: Regularization Techniques
- Lasso and Ridge regression, preventing overfitting.
Day 14: Model Evaluation and Validation
- Cross-validation, bias-variance tradeoff, train-test split.
### Week 3: Supervised Learning
Day 15: Decision Trees
- Concept, implementation, advantages, and disadvantages.
Day 16: Random Forest
- Ensemble learning, bagging, and random forest implementation.
Day 17: Gradient Boosting
- Boosting, Gradient Boosting Machines (GBM), and implementation.
Day 18: Support Vector Machines (SVM)
- Concept, kernel trick, implementation, and tuning.
Day 19: k-Nearest Neighbors (k-NN)
- Concept, distance metrics, implementation, and tuning.
Day 20: Naive Bayes
- Concept, assumptions, implementation, and applications.
Day 21: Model Tuning and Hyperparameter Optimization
- Grid search, random search, and Bayesian optimization.
### Week 4: Unsupervised Learning and Advanced Topics
Day 22: Clustering with k-Means
- Concept, algorithm, implementation, and evaluation metrics (silhouette score).
Day 23: Hierarchical Clustering
- Agglomerative clustering, dendrograms, and implementation.
Day 24: Principal Component Analysis (PCA)
- Dimensionality reduction, variance explanation, and implementation.
Day 25: Association Rule Learning
- Apriori algorithm, market basket analysis, and implementation.
Day 26: Natural Language Processing (NLP) Basics
- Text preprocessing, tokenization, and basic NLP tasks.
Day 27: Time Series Analysis
- Time series decomposition, ARIMA model, and forecasting.
Day 28: Introduction to Deep Learning
- Neural networks, perceptron, backpropagation, and implementation.
Day 29: Convolutional Neural Networks (CNNs)
- Concept, architecture, and applications in image processing.
Day 30: Recurrent Neural Networks (RNNs)
- Concept, LSTM, GRU, and applications in sequential data.
Best Resources to learn Data Science ๐๐
kaggle.com/learn
t.iss.one/datasciencefun
developers.google.com/machine-learning/crash-course
topmate.io/coding/914624
t.iss.one/pythonspecialist
freecodecamp.org/learn/machine-learning-with-python/
Join @free4unow_backup for more free courses
Like for more โค๏ธ
ENJOY LEARNING๐๐
### Week 1: Introduction and Basics
Day 1: Introduction to Data Science
- Overview of data science, its importance, and key concepts.
Day 2: Python Basics for Data Science
- Python syntax, variables, data types, and basic operations.
Day 3: Data Structures in Python
- Lists, dictionaries, sets, and tuples.
Day 4: Data Manipulation with Pandas
- Introduction to Pandas, Series, DataFrame, basic operations.
Day 5: Data Visualization with Matplotlib and Seaborn
- Creating basic plots (line, bar, scatter), customizing plots.
Day 6: Introduction to Numpy
- Arrays, array operations, mathematical functions.
Day 7: Data Cleaning and Preprocessing
- Handling missing values, data normalization, and scaling.
### Week 2: Exploratory Data Analysis and Statistical Foundations
Day 8: Exploratory Data Analysis (EDA)
- Techniques for summarizing and visualizing data.
Day 9: Probability and Statistics Basics
- Descriptive statistics, probability distributions, and hypothesis testing.
Day 10: Introduction to SQL for Data Science
- Basic SQL commands for data retrieval and manipulation.
Day 11: Linear Regression
- Concept, assumptions, implementation, and evaluation metrics (R-squared, RMSE).
Day 12: Logistic Regression
- Concept, implementation, and evaluation metrics (confusion matrix, ROC-AUC).
Day 13: Regularization Techniques
- Lasso and Ridge regression, preventing overfitting.
Day 14: Model Evaluation and Validation
- Cross-validation, bias-variance tradeoff, train-test split.
### Week 3: Supervised Learning
Day 15: Decision Trees
- Concept, implementation, advantages, and disadvantages.
Day 16: Random Forest
- Ensemble learning, bagging, and random forest implementation.
Day 17: Gradient Boosting
- Boosting, Gradient Boosting Machines (GBM), and implementation.
Day 18: Support Vector Machines (SVM)
- Concept, kernel trick, implementation, and tuning.
Day 19: k-Nearest Neighbors (k-NN)
- Concept, distance metrics, implementation, and tuning.
Day 20: Naive Bayes
- Concept, assumptions, implementation, and applications.
Day 21: Model Tuning and Hyperparameter Optimization
- Grid search, random search, and Bayesian optimization.
### Week 4: Unsupervised Learning and Advanced Topics
Day 22: Clustering with k-Means
- Concept, algorithm, implementation, and evaluation metrics (silhouette score).
Day 23: Hierarchical Clustering
- Agglomerative clustering, dendrograms, and implementation.
Day 24: Principal Component Analysis (PCA)
- Dimensionality reduction, variance explanation, and implementation.
Day 25: Association Rule Learning
- Apriori algorithm, market basket analysis, and implementation.
Day 26: Natural Language Processing (NLP) Basics
- Text preprocessing, tokenization, and basic NLP tasks.
Day 27: Time Series Analysis
- Time series decomposition, ARIMA model, and forecasting.
Day 28: Introduction to Deep Learning
- Neural networks, perceptron, backpropagation, and implementation.
Day 29: Convolutional Neural Networks (CNNs)
- Concept, architecture, and applications in image processing.
Day 30: Recurrent Neural Networks (RNNs)
- Concept, LSTM, GRU, and applications in sequential data.
Best Resources to learn Data Science ๐๐
kaggle.com/learn
t.iss.one/datasciencefun
developers.google.com/machine-learning/crash-course
topmate.io/coding/914624
t.iss.one/pythonspecialist
freecodecamp.org/learn/machine-learning-with-python/
Join @free4unow_backup for more free courses
Like for more โค๏ธ
ENJOY LEARNING๐๐
โค1๐ฅ1
๐ฒ ๐๐ฟ๐ฒ๐ฒ ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐๐ผ ๐๐ฒ๐ฎ๐ฟ๐ป ๐๐ต๐ฒ ๐ ๐ผ๐๐ ๐๐ป-๐๐ฒ๐บ๐ฎ๐ป๐ฑ ๐ง๐ฒ๐ฐ๐ต ๐ฆ๐ธ๐ถ๐น๐น๐๐
๐ Want to future-proof your career without spending a single rupee?๐ต
These 6 free online courses from top institutions like Google, Harvard, IBM, Stanford, and Cisco will help you master high-demand tech skills in 2025 โ from Data Analytics to Machine Learning๐๐งโ๐ป
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4fbDejW
Each course is beginner-friendly, comes with certification, and helps you build your resume or switch careersโ ๏ธ
๐ Want to future-proof your career without spending a single rupee?๐ต
These 6 free online courses from top institutions like Google, Harvard, IBM, Stanford, and Cisco will help you master high-demand tech skills in 2025 โ from Data Analytics to Machine Learning๐๐งโ๐ป
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4fbDejW
Each course is beginner-friendly, comes with certification, and helps you build your resume or switch careersโ ๏ธ
โค2
7 Free Kaggle Micro-Courses for Data Science Beginners with Certification
Python
https://www.kaggle.com/learn/python
Pandas
https://www.kaggle.com/learn/pandas
Data visualization
https://www.kaggle.com/learn/data-visualization
Intro to sql
https://www.kaggle.com/learn/intro-to-sql
Advanced Sql
https://www.kaggle.com/learn/advanced-sql
Intro to ML
https://www.kaggle.com/learn/intro-to-machine-learning
Advanced ML
https://www.kaggle.com/learn/intermediate-machine-learning
Python
https://www.kaggle.com/learn/python
Pandas
https://www.kaggle.com/learn/pandas
Data visualization
https://www.kaggle.com/learn/data-visualization
Intro to sql
https://www.kaggle.com/learn/intro-to-sql
Advanced Sql
https://www.kaggle.com/learn/advanced-sql
Intro to ML
https://www.kaggle.com/learn/intro-to-machine-learning
Advanced ML
https://www.kaggle.com/learn/intermediate-machine-learning
โค2๐ฅ1
Forwarded from Artificial Intelligence
๐๐ง๐ผ๐ฝ ๐ฏ ๐๐ฟ๐ฒ๐ฒ ๐๐ผ๐ผ๐ด๐น๐ฒ-๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฒ๐ฑ ๐ฃ๐๐๐ต๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐ฎ๐ฌ๐ฎ๐ฑ๐
Want to boost your tech career? Learn Python for FREE with Google-certified courses!
Perfect for beginnersโno expensive bootcamps needed.
๐ฅ Learn Python for AI, Data, Automation & More!
๐๐ฆ๐๐ฎ๐ฟ๐ ๐ก๐ผ๐๐
https://pdlink.in/42okGqG
โ Future You Will Thank You!
Want to boost your tech career? Learn Python for FREE with Google-certified courses!
Perfect for beginnersโno expensive bootcamps needed.
๐ฅ Learn Python for AI, Data, Automation & More!
๐๐ฆ๐๐ฎ๐ฟ๐ ๐ก๐ผ๐๐
https://pdlink.in/42okGqG
โ Future You Will Thank You!
โค1
โEssential Data Science Concepts Everyone Should Know:
1. Data Types and Structures:
โข Categorical: Nominal (unordered, e.g., colors) and Ordinal (ordered, e.g., education levels)
โข Numerical: Discrete (countable, e.g., number of children) and Continuous (measurable, e.g., height)
โข Data Structures: Arrays, Lists, Dictionaries, DataFrames (for organizing and manipulating data)
2. Descriptive Statistics:
โข Measures of Central Tendency: Mean, Median, Mode (describing the typical value)
โข Measures of Dispersion: Variance, Standard Deviation, Range (describing the spread of data)
โข Visualizations: Histograms, Boxplots, Scatterplots (for understanding data distribution)
3. Probability and Statistics:
โข Probability Distributions: Normal, Binomial, Poisson (modeling data patterns)
โข Hypothesis Testing: Formulating and testing claims about data (e.g., A/B testing)
โข Confidence Intervals: Estimating the range of plausible values for a population parameter
4. Machine Learning:
โข Supervised Learning: Regression (predicting continuous values) and Classification (predicting categories)
โข Unsupervised Learning: Clustering (grouping similar data points) and Dimensionality Reduction (simplifying data)
โข Model Evaluation: Accuracy, Precision, Recall, F1-score (assessing model performance)
5. Data Cleaning and Preprocessing:
โข Missing Value Handling: Imputation, Deletion (dealing with incomplete data)
โข Outlier Detection and Removal: Identifying and addressing extreme values
โข Feature Engineering: Creating new features from existing ones (e.g., combining variables)
6. Data Visualization:
โข Types of Charts: Bar charts, Line charts, Pie charts, Heatmaps (for communicating insights visually)
โข Principles of Effective Visualization: Clarity, Accuracy, Aesthetics (for conveying information effectively)
7. Ethical Considerations in Data Science:
โข Data Privacy and Security: Protecting sensitive information
โข Bias and Fairness: Ensuring algorithms are unbiased and fair
8. Programming Languages and Tools:
โข Python: Popular for data science with libraries like NumPy, Pandas, Scikit-learn
โข R: Statistical programming language with strong visualization capabilities
โข SQL: For querying and manipulating data in databases
9. Big Data and Cloud Computing:
โข Hadoop and Spark: Frameworks for processing massive datasets
โข Cloud Platforms: AWS, Azure, Google Cloud (for storing and analyzing data)
10. Domain Expertise:
โข Understanding the Data: Knowing the context and meaning of data is crucial for effective analysis
โข Problem Framing: Defining the right questions and objectives for data-driven decision making
Bonus:
โข 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 ๐๐
1. Data Types and Structures:
โข Categorical: Nominal (unordered, e.g., colors) and Ordinal (ordered, e.g., education levels)
โข Numerical: Discrete (countable, e.g., number of children) and Continuous (measurable, e.g., height)
โข Data Structures: Arrays, Lists, Dictionaries, DataFrames (for organizing and manipulating data)
2. Descriptive Statistics:
โข Measures of Central Tendency: Mean, Median, Mode (describing the typical value)
โข Measures of Dispersion: Variance, Standard Deviation, Range (describing the spread of data)
โข Visualizations: Histograms, Boxplots, Scatterplots (for understanding data distribution)
3. Probability and Statistics:
โข Probability Distributions: Normal, Binomial, Poisson (modeling data patterns)
โข Hypothesis Testing: Formulating and testing claims about data (e.g., A/B testing)
โข Confidence Intervals: Estimating the range of plausible values for a population parameter
4. Machine Learning:
โข Supervised Learning: Regression (predicting continuous values) and Classification (predicting categories)
โข Unsupervised Learning: Clustering (grouping similar data points) and Dimensionality Reduction (simplifying data)
โข Model Evaluation: Accuracy, Precision, Recall, F1-score (assessing model performance)
5. Data Cleaning and Preprocessing:
โข Missing Value Handling: Imputation, Deletion (dealing with incomplete data)
โข Outlier Detection and Removal: Identifying and addressing extreme values
โข Feature Engineering: Creating new features from existing ones (e.g., combining variables)
6. Data Visualization:
โข Types of Charts: Bar charts, Line charts, Pie charts, Heatmaps (for communicating insights visually)
โข Principles of Effective Visualization: Clarity, Accuracy, Aesthetics (for conveying information effectively)
7. Ethical Considerations in Data Science:
โข Data Privacy and Security: Protecting sensitive information
โข Bias and Fairness: Ensuring algorithms are unbiased and fair
8. Programming Languages and Tools:
โข Python: Popular for data science with libraries like NumPy, Pandas, Scikit-learn
โข R: Statistical programming language with strong visualization capabilities
โข SQL: For querying and manipulating data in databases
9. Big Data and Cloud Computing:
โข Hadoop and Spark: Frameworks for processing massive datasets
โข Cloud Platforms: AWS, Azure, Google Cloud (for storing and analyzing data)
10. Domain Expertise:
โข Understanding the Data: Knowing the context and meaning of data is crucial for effective analysis
โข Problem Framing: Defining the right questions and objectives for data-driven decision making
Bonus:
โข 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 ๐๐
โค2
Forwarded from Artificial Intelligence
๐๐ฅ๐๐ ๐ง๐๐ง๐ ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ๐ฉ๐ถ๐ฟ๐๐๐ฎ๐น ๐๐ป๐๐ฒ๐ฟ๐ป๐๐ต๐ถ๐ฝ ๐ณ๐ผ๐ฟ ๐๐ฒ๐ด๐ถ๐ป๐ป๐ฒ๐ฟ๐ (๐ช๐ถ๐๐ต ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ฒ)๐
๐ฏ Gain Real-World Data Analytics Experience with TATA โ 100% Free!๐โจ๏ธ
Want to boost your resume and build real-world experience as a beginner? This free TATA Data Analytics Virtual Internship on Forage lets you step into the shoes of a data analyst โ no experience required!๐งโ๐๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3FyjDgp
No application or selection process โ just sign up and start learning instantly!โ ๏ธ
๐ฏ Gain Real-World Data Analytics Experience with TATA โ 100% Free!๐โจ๏ธ
Want to boost your resume and build real-world experience as a beginner? This free TATA Data Analytics Virtual Internship on Forage lets you step into the shoes of a data analyst โ no experience required!๐งโ๐๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3FyjDgp
No application or selection process โ just sign up and start learning instantly!โ ๏ธ
โค2
Data Science Learning Plan
Step 1: Mathematics for Data Science (Statistics, Probability, Linear Algebra)
Step 2: Python for Data Science (Basics and Libraries)
Step 3: Data Manipulation and Analysis (Pandas, NumPy)
Step 4: Data Visualization (Matplotlib, Seaborn, Plotly)
Step 5: Databases and SQL for Data Retrieval
Step 6: Introduction to Machine Learning (Supervised and Unsupervised Learning)
Step 7: Data Cleaning and Preprocessing
Step 8: Feature Engineering and Selection
Step 9: Model Evaluation and Tuning
Step 10: Deep Learning (Neural Networks, TensorFlow, Keras)
Step 11: Working with Big Data (Hadoop, Spark)
Step 12: Building Data Science Projects and Portfolio
Step 1: Mathematics for Data Science (Statistics, Probability, Linear Algebra)
Step 2: Python for Data Science (Basics and Libraries)
Step 3: Data Manipulation and Analysis (Pandas, NumPy)
Step 4: Data Visualization (Matplotlib, Seaborn, Plotly)
Step 5: Databases and SQL for Data Retrieval
Step 6: Introduction to Machine Learning (Supervised and Unsupervised Learning)
Step 7: Data Cleaning and Preprocessing
Step 8: Feature Engineering and Selection
Step 9: Model Evaluation and Tuning
Step 10: Deep Learning (Neural Networks, TensorFlow, Keras)
Step 11: Working with Big Data (Hadoop, Spark)
Step 12: Building Data Science Projects and Portfolio
โค4
Forwarded from Python Projects & Resources
๐ณ ๐ ๐๐๐-๐๐ป๐ผ๐ ๐ฆ๐ค๐ ๐๐ผ๐ป๐ฐ๐ฒ๐ฝ๐๐ ๐๐๐ฒ๐ฟ๐ ๐๐๐ฝ๐ถ๐ฟ๐ถ๐ป๐ด ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ ๐ฆ๐ต๐ผ๐๐น๐ฑ ๐ ๐ฎ๐๐๐ฒ๐ฟ๐
If youโre serious about becoming a data analyst, thereโs no skipping SQL. Itโs not just another technical skill โ itโs the core language for data analytics.๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/44S3Xi5
This guide covers 7 key SQL concepts that every beginner must learnโ ๏ธ
If youโre serious about becoming a data analyst, thereโs no skipping SQL. Itโs not just another technical skill โ itโs the core language for data analytics.๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/44S3Xi5
This guide covers 7 key SQL concepts that every beginner must learnโ ๏ธ
โค1
Forwarded from Artificial Intelligence
๐๐ฐ๐ฒ ๐ฌ๐ผ๐๐ฟ ๐ฆ๐ค๐ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐ ๐๐ถ๐๐ต ๐ง๐ต๐ฒ๐๐ฒ ๐ฏ๐ฌ ๐ ๐ผ๐๐-๐๐๐ธ๐ฒ๐ฑ ๐ค๐๐ฒ๐๐๐ถ๐ผ๐ป๐! ๐
๐คฆ๐ปโโ๏ธStruggling with SQL interviews? Not anymore!๐
SQL interviews can be challenging, but preparation is the key to success. Whether youโre aiming for a data analytics role or just brushing up, this resource has got your back!๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4olhd6z
Letโs crack that interview together!โ ๏ธ
๐คฆ๐ปโโ๏ธStruggling with SQL interviews? Not anymore!๐
SQL interviews can be challenging, but preparation is the key to success. Whether youโre aiming for a data analytics role or just brushing up, this resource has got your back!๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4olhd6z
Letโs crack that interview together!โ ๏ธ
โค2
NETWORK_SCIENCE___PYTHON.pdf
24.1 MB
Network Science with Python
David Knickerbocker, 2023
David Knickerbocker, 2023
Python Handwritten Notes PDF Guide.pdf
32.3 MB
The Ultimate Python Handwritten Notes ๐ ๐
React โค๏ธ for more
React โค๏ธ for more
โค3
Top 10 Alteryx Interview Questions and Answers ๐๐
1. Question: What is Alteryx, and how does it differ from traditional ETL tools?
Answer: Alteryx is a self-service data preparation and analytics platform. Unlike traditional ETL tools, it empowers users with a user-friendly interface, allowing them to blend, cleanse, and analyze data without extensive coding.
2. Question: Explain the purpose of the Input Data tool in Alteryx.
Answer: The Input Data tool is used to connect to and bring in data from various sources. It supports a wide range of file formats and databases.
3. Question: How does the Summarize tool differ from the Cross Tab tool in Alteryx?
Answer: The Summarize tool aggregates and summarizes data, while the Cross Tab tool pivots data, transforming rows into columns and vice versa.
4. Question: What is the purpose of the Browse tool in Alteryx?
Answer: The Browse tool is used for data inspection. It allows users to view and understand the structure and content of their data at different points in the workflow.
5. Question: How can you handle missing or null values in Alteryx?
Answer: Use the Imputation tool to fill in missing values or the Filter tool to exclude records with null values. Alteryx provides several tools for data cleansing and handling missing data.
6. Question: Explain the role of the Formula tool in Alteryx.
Answer: The Formula tool is used for creating new fields and performing calculations on existing data. It supports a variety of functions and expressions.
7. Question: What is the purpose of the Output Data tool in Alteryx?
Answer: The Output Data tool is used to save or output the results of an Alteryx workflow to different file formats or databases.
8. Question: How does Alteryx handle spatial data, and what tools are available for spatial analysis?
Answer: Alteryx supports spatial data processing through tools like the Spatial Info, Spatial Match, and the Create Points tools. These tools enable users to perform spatial analytics.
9. Question: Explain the concept of Iterative Macros in Alteryx.
Answer: Iterative Macros in Alteryx allow users to create workflows that iterate over a set of data multiple times, enabling more complex and dynamic data processing.
10. Question: How can you schedule and automate workflows in Alteryx?
Answer: Alteryx provides the Scheduler and the Gallery platform for scheduling and automating workflows. Users can publish workflows to the Gallery and set up schedules for execution.
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
1. Question: What is Alteryx, and how does it differ from traditional ETL tools?
Answer: Alteryx is a self-service data preparation and analytics platform. Unlike traditional ETL tools, it empowers users with a user-friendly interface, allowing them to blend, cleanse, and analyze data without extensive coding.
2. Question: Explain the purpose of the Input Data tool in Alteryx.
Answer: The Input Data tool is used to connect to and bring in data from various sources. It supports a wide range of file formats and databases.
3. Question: How does the Summarize tool differ from the Cross Tab tool in Alteryx?
Answer: The Summarize tool aggregates and summarizes data, while the Cross Tab tool pivots data, transforming rows into columns and vice versa.
4. Question: What is the purpose of the Browse tool in Alteryx?
Answer: The Browse tool is used for data inspection. It allows users to view and understand the structure and content of their data at different points in the workflow.
5. Question: How can you handle missing or null values in Alteryx?
Answer: Use the Imputation tool to fill in missing values or the Filter tool to exclude records with null values. Alteryx provides several tools for data cleansing and handling missing data.
6. Question: Explain the role of the Formula tool in Alteryx.
Answer: The Formula tool is used for creating new fields and performing calculations on existing data. It supports a variety of functions and expressions.
7. Question: What is the purpose of the Output Data tool in Alteryx?
Answer: The Output Data tool is used to save or output the results of an Alteryx workflow to different file formats or databases.
8. Question: How does Alteryx handle spatial data, and what tools are available for spatial analysis?
Answer: Alteryx supports spatial data processing through tools like the Spatial Info, Spatial Match, and the Create Points tools. These tools enable users to perform spatial analytics.
9. Question: Explain the concept of Iterative Macros in Alteryx.
Answer: Iterative Macros in Alteryx allow users to create workflows that iterate over a set of data multiple times, enabling more complex and dynamic data processing.
10. Question: How can you schedule and automate workflows in Alteryx?
Answer: Alteryx provides the Scheduler and the Gallery platform for scheduling and automating workflows. Users can publish workflows to the Gallery and set up schedules for execution.
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค2