𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗙𝗥𝗘𝗘 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 ,𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 ,𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 & 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗚𝘂𝗶𝗱𝗲😍

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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 :)

𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝗔𝗽𝗽𝗿𝗼𝘃𝗲𝗱 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 😍

Whether you’re interested in AI, Data Analytics, Cybersecurity, or Cloud Computing, there’s something here for everyone.

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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).

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𝗧𝗼𝗽 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 & 𝗟𝗲𝗮𝗱𝗶𝗻𝗴 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗢𝗳𝗳𝗲𝗿𝗶𝗻𝗴 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍

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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.

𝗙𝗿𝗲𝗲 𝗔𝗜 & 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗖𝗼𝘂𝗿𝘀𝗲 𝗳𝗼𝗿 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿𝘀😍

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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.

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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👨‍💻

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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

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👨‍💻📌

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Show recruiters that you don’t just “know” tools — you use them to solve problems✅️

If I need to teach someone data analytics from the basics, here is my strategy:

1. I will first remove the fear of tools from that person

2. i will start with the excel because it looks familiar and easy to use

3. I put more emphasis on projects like at least 5 to 6 with the excel. because in industry you learn by doing things

4. I will release the person from the tutorial hell and move into a more action oriented person

5. Then I move to the sql because every job wants it , even with the ai tools you need strong understanding for it if you are going to use it daily

6. After strong understanding, I will push the person to solve 100 to 150 Sql problems from basic to advance

7. It helps the person to develop the analytical thinking

8. Then I push the person to solve 3 case studies as it helps how we pull the data in the real life

9. Then I move the person to power bi to do again 5 projects by using either sql or excel files

10. Now the fear is removed.

11. Now I push the person to solve unguided challenges and present them by video recording as it increases the problem solving, communication and data story telling skills

12. Further it helps you to clear case study round given by most of the companies

13. Now i help the person how to present them in resume and also how these tools are used in real world.

14. You know the interesting fact, all of above is present free in youtube and I also mentor the people through existing youtube videos.

15. But people stuck in the tutorial hell, loose motivation , stay confused that they are either in the right direction or not.

16. As a personal mentor , I help them to get of the tutorial hell, set them in the right direction and they stay motivated when they start to see the difference before amd after mentorship

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𝗪𝗮𝗻𝘁 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗜𝗻-𝗗𝗲𝗺𝗮𝗻𝗱 𝗧𝗲𝗰𝗵 𝗦𝗸𝗶𝗹𝗹𝘀 — 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 — 𝗗𝗶𝗿𝗲𝗰𝘁𝗹𝘆 𝗳𝗿𝗼𝗺 𝗚𝗼𝗼𝗴𝗹𝗲?😍

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Enjoy Learning ✅️

Today, lets understand Machine Learning in simplest way possible

What is Machine Learning?

Think of it like this:

Machine Learning is when you teach a computer to learn from data, so it can make decisions or predictions without being told exactly what to do step-by-step.

Real-Life Example:
Let’s say you want to teach a kid how to recognize a dog.
You show the kid a bunch of pictures of dogs.

The kid starts noticing patterns — “Oh, they have four legs, fur, floppy ears...”

Next time the kid sees a new picture, they might say, “That’s a dog!” — even if they’ve never seen that exact dog before.

That’s what machine learning does — but instead of a kid, it's a computer.

In Tech Terms (Still Simple):

You give the computer data (like pictures, numbers, or text).
You give it examples of the right answers (like “this is a dog”, “this is not a dog”).
It learns the patterns.

Later, when you give it new data, it makes a smart guess.

Few Common Uses of ML You See Every Day:

Netflix: Suggesting shows you might like.
Google Maps: Predicting traffic.
Amazon: Recommending products.
Banks: Detecting fraud in transactions.

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If I had to start learning data analyst all over again, I'd follow this:

1- Learn SQL:

---- Joins (Inner, Left, Full outer and Self)
---- Aggregate Functions (COUNT, SUM, AVG, MIN, MAX)
---- Group by and Having clause
---- CTE and Subquery
---- Windows Function (Rank, Dense Rank, Row number, Lead, Lag etc)

2- Learn Excel:

---- Mathematical (COUNT, SUM, AVG, MIN, MAX, etc)
---- Logical Functions (IF, AND, OR, NOT)
---- Lookup and Reference (VLookup, INDEX, MATCH etc)
---- Pivot Table, Filters, Slicers

3- Learn BI Tools:

---- Data Integration and ETL (Extract, Transform, Load)
---- Report Generation
---- Data Exploration and Ad-hoc Analysis
---- Dashboard Creation

4- Learn Python (Pandas) Optional:

---- Data Structures, Data Cleaning and Preparation
---- Data Manipulation
---- Merging and Joining Data (Merging and joining DataFrames -similar to SQL joins)
---- Data Visualization (Basic plotting using Matplotlib and Seaborn)

Credits: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02

Hope this helps you 😊