Forwarded from Artificial Intelligence
𝟱 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗧𝗲𝗰𝗵 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘁𝗼 𝗕𝘂𝗶𝗹𝗱 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲 – 𝗪𝗶𝘁𝗵 𝗙𝘂𝗹𝗹 𝗧𝘂𝘁𝗼𝗿𝗶𝗮𝗹𝘀!😍
Are you ready to build real-world tech projects that don’t just look good on your resume, but actually teach you practical, job-ready skills?🧑💻📌
Here’s a curated list of 5 high-value development tutorials — covering everything from full-stack development and real-time chat apps to AI form builders and reinforcement learning✨️💻
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3UtCSLO
They’re real, portfolio-worthy projects you can start today✅️
Are you ready to build real-world tech projects that don’t just look good on your resume, but actually teach you practical, job-ready skills?🧑💻📌
Here’s a curated list of 5 high-value development tutorials — covering everything from full-stack development and real-time chat apps to AI form builders and reinforcement learning✨️💻
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3UtCSLO
They’re real, portfolio-worthy projects you can start today✅️
❤1
Forwarded from Artificial Intelligence
𝟯 𝗙𝗿𝗲𝗲 𝗦𝗤𝗟 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗣𝗹𝗮𝘆𝗹𝗶𝘀𝘁𝘀 𝗧𝗵𝗮𝘁 𝗪𝗶𝗹𝗹 𝗠𝗮𝗸𝗲 𝗬𝗼𝘂 𝗮 𝗤𝘂𝗲𝗿𝘆 𝗣𝗿𝗼 𝗶𝗻 𝟮𝟬𝟮𝟱😍
Still stuck Googling “What is SQL?” every time you start a new project?💵
You’re not alone. Many beginners bounce between tutorials without ever feeling confident writing SQL queries on their own.👨💻✨️
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4f1F6LU
Let’s dive into the ones that are actually worth your time✅️
Still stuck Googling “What is SQL?” every time you start a new project?💵
You’re not alone. Many beginners bounce between tutorials without ever feeling confident writing SQL queries on their own.👨💻✨️
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4f1F6LU
Let’s dive into the ones that are actually worth your time✅️
❤1
Top 10 Data Science Concepts You Should Know 🧠
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://news.1rj.ru/str/datasciencefun
Like if you need similar content 😄👍
Hope this helps you 😊
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://news.1rj.ru/str/datasciencefun
Like if you need similar content 😄👍
Hope this helps you 😊
❤2😱1
Forwarded from Python Projects & Resources
🎓𝟱 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗧𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗧𝗲𝗰𝗵 𝗖𝗮𝗿𝗲𝗲𝗿! 🚀
Upgrade your skills and earn industry-recognized certificates — 100% FREE!
✅ Big Data Analytics – https://pdlink.in/4nzRoza
✅ AI & ML – https://pdlink.in/401SWry
✅ Cloud Computing – https://pdlink.in/3U2sMkR
✅ Cyber Security – https://pdlink.in/4nzQaDQ
✅ Other Tech Courses – https://pdlink.in/4lIN673
🎯 Enroll Now & Get Certified for FREE
Upgrade your skills and earn industry-recognized certificates — 100% FREE!
✅ Big Data Analytics – https://pdlink.in/4nzRoza
✅ AI & ML – https://pdlink.in/401SWry
✅ Cloud Computing – https://pdlink.in/3U2sMkR
✅ Cyber Security – https://pdlink.in/4nzQaDQ
✅ Other Tech Courses – https://pdlink.in/4lIN673
🎯 Enroll Now & Get Certified for FREE
30-days learning plan to cover data science fundamental algorithms, important concepts, and practical applications 👇👇
### 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
- Denoscriptive 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 👇👇
kaggle.com/learn
t.me/datasciencefun
developers.google.com/machine-learning/crash-course
topmate.io/coding/914624
t.me/pythonspecialist
freecodecamp.org/learn/machine-learning-with-python/
Join @free4unow_backup for more free courses
Like for more ❤️
ENJOY LEARNING👍👍
### 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
- Denoscriptive 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 👇👇
kaggle.com/learn
t.me/datasciencefun
developers.google.com/machine-learning/crash-course
topmate.io/coding/914624
t.me/pythonspecialist
freecodecamp.org/learn/machine-learning-with-python/
Join @free4unow_backup for more free courses
Like for more ❤️
ENJOY LEARNING👍👍
❤1🔥1
𝟲 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝘁𝗵𝗲 𝗠𝗼𝘀𝘁 𝗜𝗻-𝗗𝗲𝗺𝗮𝗻𝗱 𝗧𝗲𝗰𝗵 𝗦𝗸𝗶𝗹𝗹𝘀😍
🚀 Want to future-proof your career without spending a single rupee?💵
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 — from Data Analytics to Machine Learning📊🧑💻
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4fbDejW
Each course is beginner-friendly, comes with certification, and helps you build your resume or switch careers✅️
🚀 Want to future-proof your career without spending a single rupee?💵
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 — from Data Analytics to Machine Learning📊🧑💻
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4fbDejW
Each course is beginner-friendly, comes with certification, and helps you build your resume or switch careers✅️
❤2
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
❤2🔥1
Forwarded from Artificial Intelligence
🚀𝗧𝗼𝗽 𝟯 𝗙𝗿𝗲𝗲 𝗚𝗼𝗼𝗴𝗹𝗲-𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗲𝗱 𝗣𝘆𝘁𝗵𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝟮𝟬𝟮𝟱😍
Want to boost your tech career? Learn Python for FREE with Google-certified courses!
Perfect for beginners—no expensive bootcamps needed.
🔥 Learn Python for AI, Data, Automation & More!
📍𝗦𝘁𝗮𝗿𝘁 𝗡𝗼𝘄👇
https://pdlink.in/42okGqG
✅ Future You Will Thank You!
Want to boost your tech career? Learn Python for FREE with Google-certified courses!
Perfect for beginners—no expensive bootcamps needed.
🔥 Learn Python for AI, Data, Automation & More!
📍𝗦𝘁𝗮𝗿𝘁 𝗡𝗼𝘄👇
https://pdlink.in/42okGqG
✅ Future You Will Thank You!
❤1
▎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 👍👍
❤2
Forwarded from Artificial Intelligence
𝗙𝗥𝗘𝗘 𝗧𝗔𝗧𝗔 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽 𝗳𝗼𝗿 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿𝘀 (𝗪𝗶𝘁𝗵 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗲)😍
🎯 Gain Real-World Data Analytics Experience with TATA – 100% Free!📊✨️
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 — no experience required!🧑🎓📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3FyjDgp
No application or selection process — just sign up and start learning instantly!✅️
🎯 Gain Real-World Data Analytics Experience with TATA – 100% Free!📊✨️
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 — no experience required!🧑🎓📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3FyjDgp
No application or selection process — just sign up and start learning instantly!✅️
❤2
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
❤4
Forwarded from Python Projects & Resources
𝟳 𝗠𝘂𝘀𝘁-𝗞𝗻𝗼𝘄 𝗦𝗤𝗟 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀 𝗘𝘃𝗲𝗿𝘆 𝗔𝘀𝗽𝗶𝗿𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗦𝗵𝗼𝘂𝗹𝗱 𝗠𝗮𝘀𝘁𝗲𝗿😍
If you’re serious about becoming a data analyst, there’s no skipping SQL. It’s not just another technical skill — it’s the core language for data analytics.📊
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/44S3Xi5
This guide covers 7 key SQL concepts that every beginner must learn✅️
If you’re serious about becoming a data analyst, there’s no skipping SQL. It’s not just another technical skill — it’s the core language for data analytics.📊
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/44S3Xi5
This guide covers 7 key SQL concepts that every beginner must learn✅️
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Forwarded from Artificial Intelligence
𝗔𝗰𝗲 𝗬𝗼𝘂𝗿 𝗦𝗤𝗟 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝘄𝗶𝘁𝗵 𝗧𝗵𝗲𝘀𝗲 𝟯𝟬 𝗠𝗼𝘀𝘁-𝗔𝘀𝗸𝗲𝗱 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀! 😍
🤦🏻♀️Struggling with SQL interviews? Not anymore!📍
SQL interviews can be challenging, but preparation is the key to success. Whether you’re aiming for a data analytics role or just brushing up, this resource has got your back!🎊
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4olhd6z
Let’s crack that interview together!✅️
🤦🏻♀️Struggling with SQL interviews? Not anymore!📍
SQL interviews can be challenging, but preparation is the key to success. Whether you’re aiming for a data analytics role or just brushing up, this resource has got your back!🎊
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4olhd6z
Let’s crack that interview together!✅️
❤2
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 📝 🚀
React ❤️ for more
React ❤️ for more
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Top 10 Alteryx Interview Questions and Answers 😄👇
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://news.1rj.ru/str/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://news.1rj.ru/str/sqlspecialist
Hope it helps :)
❤3