𝗖𝗜𝗦𝗖𝗢 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍
- Data Analytics
- Data Science
- Python
- Javanoscript
- Cybersecurity
𝐋𝐢𝐧𝐤 👇:-
https://pdlink.in/4fYr1xO
Enroll For FREE & Get Certified🎓
- Data Analytics
- Data Science
- Python
- Javanoscript
- Cybersecurity
𝐋𝐢𝐧𝐤 👇:-
https://pdlink.in/4fYr1xO
Enroll For FREE & Get Certified🎓
❤1
Forwarded from Python Projects & Resources
𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗙𝗥𝗘𝗘 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 ,𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 ,𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 & 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗚𝘂𝗶𝗱𝗲😍
Roadmap:- https://pdlink.in/41c1Kei
Certifications:- https://pdlink.in/3Fq7E4p
Projects:- https://pdlink.in/3ZkXetO
Interview Q/A :- https://pdlink.in/4jLOJ2a
Enroll For FREE & Become a Certified Data Analyst In 2025🎓
Roadmap:- https://pdlink.in/41c1Kei
Certifications:- https://pdlink.in/3Fq7E4p
Projects:- https://pdlink.in/3ZkXetO
Interview Q/A :- https://pdlink.in/4jLOJ2a
Enroll For FREE & Become a Certified Data Analyst In 2025🎓
Hi Guys,
Here are some of the telegram channels which may help you in data analytics journey 👇👇
SQL: https://news.1rj.ru/str/sqlanalyst
Power BI & Tableau: https://news.1rj.ru/str/PowerBI_analyst
Excel: https://news.1rj.ru/str/excel_analyst
Python: https://news.1rj.ru/str/dsabooks
Jobs: https://news.1rj.ru/str/datasciencej
Data Science: https://news.1rj.ru/str/datasciencefree
Artificial intelligence: https://news.1rj.ru/str/aiindi
Data Analysts: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
Here are some of the telegram channels which may help you in data analytics journey 👇👇
SQL: https://news.1rj.ru/str/sqlanalyst
Power BI & Tableau: https://news.1rj.ru/str/PowerBI_analyst
Excel: https://news.1rj.ru/str/excel_analyst
Python: https://news.1rj.ru/str/dsabooks
Jobs: https://news.1rj.ru/str/datasciencej
Data Science: https://news.1rj.ru/str/datasciencefree
Artificial intelligence: https://news.1rj.ru/str/aiindi
Data Analysts: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
❤2
Forwarded from Artificial Intelligence
𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝗔𝗽𝗽𝗿𝗼𝘃𝗲𝗱 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 😍
Whether you’re interested in AI, Data Analytics, Cybersecurity, or Cloud Computing, there’s something here for everyone.
✅ 100% Free Courses
✅ Govt. Incentives on Completion
✅ Self-paced Learning
✅ Certificates to Showcase on LinkedIn & Resume
✅ Mock Assessments to Test Your Skills
𝐋𝐢𝐧𝐤 👇:-
https://pdlink.in/447coEk
Enroll for FREE & Get Certified 🎓
Whether you’re interested in AI, Data Analytics, Cybersecurity, or Cloud Computing, there’s something here for everyone.
✅ 100% Free Courses
✅ Govt. Incentives on Completion
✅ Self-paced Learning
✅ Certificates to Showcase on LinkedIn & Resume
✅ Mock Assessments to Test Your Skills
𝐋𝐢𝐧𝐤 👇:-
https://pdlink.in/447coEk
Enroll for FREE & Get Certified 🎓
Machine Learning – Essential Concepts 🚀
1️⃣ Types of Machine Learning
Supervised Learning – Uses labeled data to train models.
Examples: Linear Regression, Decision Trees, Random Forest, SVM
Unsupervised Learning – Identifies patterns in unlabeled data.
Examples: Clustering (K-Means, DBSCAN), PCA
Reinforcement Learning – Models learn through rewards and penalties.
Examples: Q-Learning, Deep Q Networks
2️⃣ Key Algorithms
Regression – Predicts continuous values (Linear Regression, Ridge, Lasso).
Classification – Categorizes data into classes (Logistic Regression, Decision Tree, SVM, Naïve Bayes).
Clustering – Groups similar data points (K-Means, Hierarchical Clustering, DBSCAN).
Dimensionality Reduction – Reduces the number of features (PCA, t-SNE, LDA).
3️⃣ Model Training & Evaluation
Train-Test Split – Dividing data into training and testing sets.
Cross-Validation – Splitting data multiple times for better accuracy.
Metrics – Evaluating models with RMSE, Accuracy, Precision, Recall, F1-Score, ROC-AUC.
4️⃣ Feature Engineering
Handling missing data (mean imputation, dropna()).
Encoding categorical variables (One-Hot Encoding, Label Encoding).
Feature Scaling (Normalization, Standardization).
5️⃣ Overfitting & Underfitting
Overfitting – Model learns noise, performs well on training but poorly on test data.
Underfitting – Model is too simple and fails to capture patterns.
Solution: Regularization (L1, L2), Hyperparameter Tuning.
6️⃣ Ensemble Learning
Combining multiple models to improve performance.
Bagging (Random Forest)
Boosting (XGBoost, Gradient Boosting, AdaBoost)
7️⃣ Deep Learning Basics
Neural Networks (ANN, CNN, RNN).
Activation Functions (ReLU, Sigmoid, Tanh).
Backpropagation & Gradient Descent.
8️⃣ Model Deployment
Deploy models using Flask, FastAPI, or Streamlit.
Model versioning with MLflow.
Cloud deployment (AWS SageMaker, Google Vertex AI).
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
1️⃣ Types of Machine Learning
Supervised Learning – Uses labeled data to train models.
Examples: Linear Regression, Decision Trees, Random Forest, SVM
Unsupervised Learning – Identifies patterns in unlabeled data.
Examples: Clustering (K-Means, DBSCAN), PCA
Reinforcement Learning – Models learn through rewards and penalties.
Examples: Q-Learning, Deep Q Networks
2️⃣ Key Algorithms
Regression – Predicts continuous values (Linear Regression, Ridge, Lasso).
Classification – Categorizes data into classes (Logistic Regression, Decision Tree, SVM, Naïve Bayes).
Clustering – Groups similar data points (K-Means, Hierarchical Clustering, DBSCAN).
Dimensionality Reduction – Reduces the number of features (PCA, t-SNE, LDA).
3️⃣ Model Training & Evaluation
Train-Test Split – Dividing data into training and testing sets.
Cross-Validation – Splitting data multiple times for better accuracy.
Metrics – Evaluating models with RMSE, Accuracy, Precision, Recall, F1-Score, ROC-AUC.
4️⃣ Feature Engineering
Handling missing data (mean imputation, dropna()).
Encoding categorical variables (One-Hot Encoding, Label Encoding).
Feature Scaling (Normalization, Standardization).
5️⃣ Overfitting & Underfitting
Overfitting – Model learns noise, performs well on training but poorly on test data.
Underfitting – Model is too simple and fails to capture patterns.
Solution: Regularization (L1, L2), Hyperparameter Tuning.
6️⃣ Ensemble Learning
Combining multiple models to improve performance.
Bagging (Random Forest)
Boosting (XGBoost, Gradient Boosting, AdaBoost)
7️⃣ Deep Learning Basics
Neural Networks (ANN, CNN, RNN).
Activation Functions (ReLU, Sigmoid, Tanh).
Backpropagation & Gradient Descent.
8️⃣ Model Deployment
Deploy models using Flask, FastAPI, or Streamlit.
Model versioning with MLflow.
Cloud deployment (AWS SageMaker, Google Vertex AI).
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
❤3
Forwarded from Python Projects & Resources
𝗧𝗼𝗽 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 & 𝗟𝗲𝗮𝗱𝗶𝗻𝗴 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗢𝗳𝗳𝗲𝗿𝗶𝗻𝗴 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍
Harward :- https://pdlink.in/4kmYOn1
MIT :- https://pdlink.in/45cvR95
HP :- https://pdlink.in/45ci02k
Google :- https://pdlink.in/3YsujTV
Microsoft :- https://pdlink.in/441GCKF
Standford :- https://pdlink.in/3ThPwNw
IIM :- https://pdlink.in/4nfXDrV
Enroll for FREE & Get Certified 🎓
Harward :- https://pdlink.in/4kmYOn1
MIT :- https://pdlink.in/45cvR95
HP :- https://pdlink.in/45ci02k
Google :- https://pdlink.in/3YsujTV
Microsoft :- https://pdlink.in/441GCKF
Standford :- https://pdlink.in/3ThPwNw
IIM :- https://pdlink.in/4nfXDrV
Enroll for FREE & Get Certified 🎓
Forwarded from Artificial Intelligence
𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭 𝐅𝐑𝐄𝐄 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞𝐬!🚀💻
Supercharge your career with 5 FREE Microsoft certification courses designed to boost your data analytics skills!
𝐄𝐧𝐫𝐨𝐥𝐥 𝐅𝐨𝐫 𝐅𝐑𝐄𝐄👇 :-
https://bit.ly/3Vlixcq
- Earn certifications to showcase your skills
Don’t wait—start your journey to success today! ✨
Supercharge your career with 5 FREE Microsoft certification courses designed to boost your data analytics skills!
𝐄𝐧𝐫𝐨𝐥𝐥 𝐅𝐨𝐫 𝐅𝐑𝐄𝐄👇 :-
https://bit.ly/3Vlixcq
- Earn certifications to showcase your skills
Don’t wait—start your journey to success today! ✨
❤1
20 essential Python libraries for data science:
🔹 pandas: Data manipulation and analysis. Essential for handling DataFrames.
🔹 numpy: Numerical computing. Perfect for working with arrays and mathematical functions.
🔹 scikit-learn: Machine learning. Comprehensive tools for predictive data analysis.
🔹 matplotlib: Data visualization. Great for creating static, animated, and interactive plots.
🔹 seaborn: Statistical data visualization. Makes complex plots easy and beautiful.
Data Science
🔹 scipy: Scientific computing. Provides algorithms for optimization, integration, and more.
🔹 statsmodels: Statistical modeling. Ideal for conducting statistical tests and data exploration.
🔹 tensorflow: Deep learning. End-to-end open-source platform for machine learning.
🔹 keras: High-level neural networks API. Simplifies building and training deep learning models.
🔹 pytorch: Deep learning. A flexible and easy-to-use deep learning library.
🔹 mlflow: Machine learning lifecycle. Manages the machine learning lifecycle, including experimentation, reproducibility, and deployment.
🔹 pydantic: Data validation. Provides data validation and settings management using Python type annotations.
🔹 xgboost: Gradient boosting. An optimized distributed gradient boosting library.
🔹 lightgbm: Gradient boosting. A fast, distributed, high-performance gradient boosting framework.
🔹 pandas: Data manipulation and analysis. Essential for handling DataFrames.
🔹 numpy: Numerical computing. Perfect for working with arrays and mathematical functions.
🔹 scikit-learn: Machine learning. Comprehensive tools for predictive data analysis.
🔹 matplotlib: Data visualization. Great for creating static, animated, and interactive plots.
🔹 seaborn: Statistical data visualization. Makes complex plots easy and beautiful.
Data Science
🔹 scipy: Scientific computing. Provides algorithms for optimization, integration, and more.
🔹 statsmodels: Statistical modeling. Ideal for conducting statistical tests and data exploration.
🔹 tensorflow: Deep learning. End-to-end open-source platform for machine learning.
🔹 keras: High-level neural networks API. Simplifies building and training deep learning models.
🔹 pytorch: Deep learning. A flexible and easy-to-use deep learning library.
🔹 mlflow: Machine learning lifecycle. Manages the machine learning lifecycle, including experimentation, reproducibility, and deployment.
🔹 pydantic: Data validation. Provides data validation and settings management using Python type annotations.
🔹 xgboost: Gradient boosting. An optimized distributed gradient boosting library.
🔹 lightgbm: Gradient boosting. A fast, distributed, high-performance gradient boosting framework.
❤1
Forwarded from Artificial Intelligence
𝗙𝗿𝗲𝗲 𝗔𝗜 & 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗖𝗼𝘂𝗿𝘀𝗲 𝗳𝗼𝗿 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿𝘀😍
Want to explore AI & Machine Learning but don’t know where to start — or don’t want to spend ₹₹₹ on it?👨💻
Learn the foundations of AI, machine learning basics, data handling, and real-world use cases in just a few hours.📊📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/401SWry
This 100% FREE course is designed just for beginners — whether you’re a student, fresher, or career switcher✅️
Want to explore AI & Machine Learning but don’t know where to start — or don’t want to spend ₹₹₹ on it?👨💻
Learn the foundations of AI, machine learning basics, data handling, and real-world use cases in just a few hours.📊📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/401SWry
This 100% FREE course is designed just for beginners — whether you’re a student, fresher, or career switcher✅️
❤1
Preparing for a data science interview can be challenging, but with the right approach, you can increase your chances of success. Here are some tips to help you prepare for your next data science interview:
👉 1. Review the Fundamentals: Make sure you have a thorough understanding of the fundamentals of statistics, probability, and linear algebra. You should also be familiar with data structures, algorithms, and programming languages like Python, R, and SQL.
👉 2. Brush up on Machine Learning: Machine learning is a key aspect of data science. Make sure you have a solid understanding of different types of machine learning algorithms like supervised, unsupervised, and reinforcement learning.
👉 3. Practice Coding: Practice coding questions related to data structures, algorithms, and data science problems. You can use online resources like HackerRank, LeetCode, and Kaggle to practice.
👉 4. Build a Portfolio: Create a portfolio of projects that demonstrate your data science skills. This can include data cleaning, data wrangling, exploratory data analysis, and machine learning projects.
👉 5. Practice Communication: Data scientists are expected to effectively communicate complex technical concepts to non-technical stakeholders. Practice explaining your projects and technical concepts in simple terms.
👉 6. Research the Company: Research the company you are interviewing with and their industry. Understand how they use data and what data science problems they are trying to solve.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍
👉 1. Review the Fundamentals: Make sure you have a thorough understanding of the fundamentals of statistics, probability, and linear algebra. You should also be familiar with data structures, algorithms, and programming languages like Python, R, and SQL.
👉 2. Brush up on Machine Learning: Machine learning is a key aspect of data science. Make sure you have a solid understanding of different types of machine learning algorithms like supervised, unsupervised, and reinforcement learning.
👉 3. Practice Coding: Practice coding questions related to data structures, algorithms, and data science problems. You can use online resources like HackerRank, LeetCode, and Kaggle to practice.
👉 4. Build a Portfolio: Create a portfolio of projects that demonstrate your data science skills. This can include data cleaning, data wrangling, exploratory data analysis, and machine learning projects.
👉 5. Practice Communication: Data scientists are expected to effectively communicate complex technical concepts to non-technical stakeholders. Practice explaining your projects and technical concepts in simple terms.
👉 6. Research the Company: Research the company you are interviewing with and their industry. Understand how they use data and what data science problems they are trying to solve.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍
❤2
Forwarded from Artificial Intelligence
𝗣𝗿𝗲𝗽𝗮𝗿𝗶𝗻𝗴 𝗳𝗼𝗿 𝗧𝗲𝗰𝗵 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀 𝗶𝗻 𝟮𝟬𝟮𝟱? 𝗛𝗲𝗿𝗲’𝘀 𝗬𝗼𝘂𝗿 𝗦𝘁𝗲𝗽-𝗯𝘆-𝗦𝘁𝗲𝗽 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝘁𝗼 𝗖𝗿𝗮𝗰𝗸 𝗣𝗿𝗼𝗱𝘂𝗰𝘁-𝗕𝗮𝘀𝗲𝗱 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀!😍
Landing your dream tech job takes more than just writing code — it requires structured preparation across key areas👨💻
This roadmap will guide you from zero to offer letter! 💼🚀
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3GdfTS2
This plan works if you stay consistent💪✅️
Landing your dream tech job takes more than just writing code — it requires structured preparation across key areas👨💻
This roadmap will guide you from zero to offer letter! 💼🚀
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3GdfTS2
This plan works if you stay consistent💪✅️
Building Your Personal Brand as a Data Analyst 🚀
A strong personal brand can help you land better job opportunities, attract freelance clients, and position you as a thought leader in data analytics.
Here’s how to build and grow your brand effectively:
1️⃣ Optimize Your LinkedIn Profile 🔍
Use a clear, professional profile picture and a compelling headline (e.g., Data Analyst | SQL | Power BI | Python Enthusiast).
Write an engaging "About" section showcasing your skills, experience, and passion for data analytics.
Share projects, case studies, and insights to demonstrate expertise.
Engage with industry leaders, recruiters, and fellow analysts.
2️⃣ Share Valuable Content Consistently ✍️
Post insightful LinkedIn posts, Medium articles, or Twitter threads on SQL, Power BI, Python, and industry trends.
Write about real-world case studies, common mistakes, and career advice.
Share data visualization tips, SQL tricks, or step-by-step tutorials.
3️⃣ Contribute to Open-Source & GitHub 💻
Publish SQL queries, Python noscripts, Jupyter notebooks, and dashboards.
Share projects with real datasets to showcase your hands-on skills.
Collaborate on open-source data analytics projects to gain exposure.
4️⃣ Engage in Online Data Analytics Communities 🌍
Join and contribute to Reddit (r/dataanalysis, r/SQL), Stack Overflow, and Data Science Discord groups.
Participate in Kaggle competitions to gain practical experience.
Answer questions on Quora, LinkedIn, or Twitter to establish credibility.
5️⃣ Speak at Webinars & Meetups 🎤
Host or participate in webinars on LinkedIn, YouTube, or data conferences.
Join local meetups or online communities like DataCamp and Tableau User Groups.
Share insights on career growth, best practices, and analytics trends.
6️⃣ Create a Portfolio Website 🌐
Build a personal website showcasing your projects, resume, and blog.
Include interactive dashboards, case studies, and problem-solving examples.
Use Wix, WordPress, or GitHub Pages to get started.
7️⃣ Network & Collaborate 🤝
Connect with hiring managers, recruiters, and senior analysts.
Collaborate on guest blog posts, podcasts, or YouTube interviews.
Attend data science and analytics conferences to expand your reach.
8️⃣ Start a YouTube Channel or Podcast 🎥
Share short tutorials on SQL, Power BI, Python, and Excel.
Interview industry experts and discuss data analytics career paths.
Offer career guidance, resume tips, and interview prep content.
9️⃣ Offer Free Value Before Monetizing 💡
Give away free e-books, templates, or mini-courses to attract an audience.
Provide LinkedIn Live Q&A sessions, career guidance, or free tutorials.
Once you build trust, you can monetize through consulting, courses, and coaching.
🔟 Stay Consistent & Keep Learning
Building a brand takes time—stay consistent with content creation and engagement.
Keep learning new skills and sharing your journey to stay relevant.
Follow industry leaders, subscribe to analytics blogs, and attend workshops.
A strong personal brand in data analytics can open unlimited opportunities—from job offers to freelance gigs and consulting projects.
Start small, be consistent, and showcase your expertise! 🔥
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
#dataanalyst
A strong personal brand can help you land better job opportunities, attract freelance clients, and position you as a thought leader in data analytics.
Here’s how to build and grow your brand effectively:
1️⃣ Optimize Your LinkedIn Profile 🔍
Use a clear, professional profile picture and a compelling headline (e.g., Data Analyst | SQL | Power BI | Python Enthusiast).
Write an engaging "About" section showcasing your skills, experience, and passion for data analytics.
Share projects, case studies, and insights to demonstrate expertise.
Engage with industry leaders, recruiters, and fellow analysts.
2️⃣ Share Valuable Content Consistently ✍️
Post insightful LinkedIn posts, Medium articles, or Twitter threads on SQL, Power BI, Python, and industry trends.
Write about real-world case studies, common mistakes, and career advice.
Share data visualization tips, SQL tricks, or step-by-step tutorials.
3️⃣ Contribute to Open-Source & GitHub 💻
Publish SQL queries, Python noscripts, Jupyter notebooks, and dashboards.
Share projects with real datasets to showcase your hands-on skills.
Collaborate on open-source data analytics projects to gain exposure.
4️⃣ Engage in Online Data Analytics Communities 🌍
Join and contribute to Reddit (r/dataanalysis, r/SQL), Stack Overflow, and Data Science Discord groups.
Participate in Kaggle competitions to gain practical experience.
Answer questions on Quora, LinkedIn, or Twitter to establish credibility.
5️⃣ Speak at Webinars & Meetups 🎤
Host or participate in webinars on LinkedIn, YouTube, or data conferences.
Join local meetups or online communities like DataCamp and Tableau User Groups.
Share insights on career growth, best practices, and analytics trends.
6️⃣ Create a Portfolio Website 🌐
Build a personal website showcasing your projects, resume, and blog.
Include interactive dashboards, case studies, and problem-solving examples.
Use Wix, WordPress, or GitHub Pages to get started.
7️⃣ Network & Collaborate 🤝
Connect with hiring managers, recruiters, and senior analysts.
Collaborate on guest blog posts, podcasts, or YouTube interviews.
Attend data science and analytics conferences to expand your reach.
8️⃣ Start a YouTube Channel or Podcast 🎥
Share short tutorials on SQL, Power BI, Python, and Excel.
Interview industry experts and discuss data analytics career paths.
Offer career guidance, resume tips, and interview prep content.
9️⃣ Offer Free Value Before Monetizing 💡
Give away free e-books, templates, or mini-courses to attract an audience.
Provide LinkedIn Live Q&A sessions, career guidance, or free tutorials.
Once you build trust, you can monetize through consulting, courses, and coaching.
🔟 Stay Consistent & Keep Learning
Building a brand takes time—stay consistent with content creation and engagement.
Keep learning new skills and sharing your journey to stay relevant.
Follow industry leaders, subscribe to analytics blogs, and attend workshops.
A strong personal brand in data analytics can open unlimited opportunities—from job offers to freelance gigs and consulting projects.
Start small, be consistent, and showcase your expertise! 🔥
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
#dataanalyst
❤2
Forwarded from Python Projects & Resources
𝗪𝗮𝗻𝘁 𝘁𝗼 𝗕𝘂𝗶𝗹𝗱 𝗮 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 𝗧𝗵𝗮𝘁 𝗚𝗲𝘁𝘀 𝗬𝗼𝘂 𝗛𝗶𝗿𝗲𝗱?😍
If you’re just starting out in data analytics and wondering how to stand out — real-world projects are the key📊
No recruiter is impressed by “just theory.” What they want to see? Actionable proof of your skills👨💻📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4ezeIc9
Show recruiters that you don’t just “know” tools — you use them to solve problems✅️
If you’re just starting out in data analytics and wondering how to stand out — real-world projects are the key📊
No recruiter is impressed by “just theory.” What they want to see? Actionable proof of your skills👨💻📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4ezeIc9
Show recruiters that you don’t just “know” tools — you use them to solve problems✅️