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๐Explore the fascinating world of Data Analytics & Artificial Intelligence ๐ป Best AI tools, free resources, and expert advice to land your dream tech job. Admin: @coderfun Buy ads: https://telega.io/c/Data_Visual
Ko'proq ko'rsatish๐ Telegram kanali Data Analytics & AI | SQL Interviews | Power BI Resources analitikasi
Data Analytics & AI | SQL Interviews | Power BI Resources (@data_visual) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 27 189 obunachidan iborat bo'lib, Taสผlim toifasida 7 215-o'rinni va Hindiston mintaqasida 16 026-o'rinni egallagan.
๐ Auditoriya koโrsatkichlari va dinamika
ะฝะตะฒัะดะพะผะพ sanasidan buyon loyiha tez oโsib, 27 189 obunachiga ega boโldi.
12 Iyun, 2026 dagi oxirgi maโlumotlarga koโra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 230 ga, soโnggi 24 soatda esa 12 ga oโzgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
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๐ Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida taโriflaydi:
โ๐Explore the fascinating world of Data Analytics & Artificial Intelligence
๐ป Best AI tools, free resources, and expert advice to land your dream tech job.
Admin: @coderfun
Buy ads: https://telega.io/c/Data_Visualโ
Yuqori yangilanish chastotasi (oxirgi maโlumot 13 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli boโlib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Taสผlim toifasidagi muhim taโsir nuqtasiga aylantirishini koโrsatadi.
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Repost from Python Projects & Resources
๐ ๐
๐ซ๐๐ ๐๐จ๐ฎ๐๐ฎ๐๐ ๐๐๐ฌ๐จ๐ฎ๐ซ๐๐๐ฌ ๐ญ๐จ ๐๐ฎ๐ข๐ฅ๐ ๐๐ ๐๐ฎ๐ญ๐จ๐ฆ๐๐ญ๐ข๐จ๐ง๐ฌ & ๐๐ ๐๐ง๐ญ๐ฌ ๐๐ข๐ญ๐ก๐จ๐ฎ๐ญ ๐๐จ๐๐ข๐ง๐ ๐
Want to Create AI Automations & Agents Without Writing a Single Line of Code?๐งโ๐ป
These 5 free YouTube tutorials will take you from complete beginner to automation expert in record time.๐งโ๐โจ๏ธ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4lhYwhn
Just pure, actionable automation skills โ for free.โ
๏ธ
๐ ๐ฎ๐๐๐ฒ๐ฟ ๐๐๐๐ฟ๐ฒ ๐ ๐ฎ๐ฐ๐ต๐ถ๐ป๐ฒ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด ๐ณ๐ผ๐ฟ ๐๐ฟ๐ฒ๐ฒ ๐๐ถ๐๐ต ๐ง๐ต๐ฒ๐๐ฒ ๐ฏ ๐ ๐ถ๐ฐ๐ฟ๐ผ๐๐ผ๐ณ๐ ๐ ๐ผ๐ฑ๐๐น๐ฒ๐!๐
Start Mastering Azure Machine Learning โ 100% Free!๐ฅ
Want to get into AI and Machine Learning using Azure but donโt know where to begin?๐๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/45oT5r0
These official Microsoft Learn modules are all you need โ hands-on, beginner-friendly, and backed with certificates๐งโ๐๐
Repost from Artificial Intelligence
๐ฏ ๐ข๐ฝ๐ฒ๐ป-๐ฆ๐ผ๐๐ฟ๐ฐ๐ฒ ๐๐ ๐ฃ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐๐ ๐๐ผ ๐๐๐ถ๐น๐ฑ ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฑ๐
If youโve ever thought, โCan I actually build something useful with AI?โ โ the answer is yes, and you donโt need to be a genius to start.โจ๏ธ๐
These 3 open-source projects on GitHub are proof of what you can build with just basic coding knowledge and a passion for learning.๐งโ๐ป๐ฅ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/45jKiXe
Build your own AI agent that remembers conversations and gets smarter over time.โ
๏ธ
๐๐ข๐ฌ๐ญ ๐จ๐ ๐๐จ๐ฆ๐ฉ๐๐ง๐ข๐๐ฌ ๐ญ๐ก๐๐ญ ๐ก๐ข๐ซ๐ ๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ฌ๐ญ๐ฌ:
TMcKinsey & Company
Boston Consulting Group (BCG)
Bain & Company
Deloitte
PwC
Ernst & Young (EY)
KPMG
Accenture
Google
Amazon
Microsoft
IBM
Oracle
Tiger Analytics
Mu Sigma
Fractal Analytics
EXL Service
ZS Associates
Wells Fargo
Walmart
Target
LTIMindtree
Infosys
TCS (Tata Consultancy Services)
Wipro
HCL Technologies
Capgemini
Cognizant
These companies often hire data analysts to use data for making decisions and planning strategically for their clients.
๐ฑ ๐๐ฟ๐ฒ๐ฒ ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐๐ผ ๐๐ถ๐ฐ๐ธ๐๐๐ฎ๐ฟ๐ ๐ฌ๐ผ๐๐ฟ ๐๐ฎ๐๐ฎ ๐๐ฎ๐ฟ๐ฒ๐ฒ๐ฟ ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฑ (๐ก๐ผ ๐๐
๐ฝ๐ฒ๐ฟ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐ก๐ฒ๐ฒ๐ฑ๐ฒ๐ฑ!)๐
Ready to Upgrade Your Skills for a Data-Driven Career in 2025?๐
Whether youโre a student, a fresher, or someone switching to tech, these free beginner-friendly courses will help you get started in data analysis, machine learning, Python, and more๐จโ๐ป๐ฏ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4mwOACf
Best For: Beginners ready to dive into real machine learningโ
๏ธ
๐ ๐๐๐ฒ๐ฌ ๐ญ๐จ ๐๐ฉ๐ฉ๐ฅ๐ฒ ๐๐จ๐ซ ๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ฌ๐ญ ๐๐จ๐๐ฌ
๐ธ๐๐ฌ๐ ๐๐จ๐ ๐๐จ๐ซ๐ญ๐๐ฅ๐ฌ
Job boards like LinkedIn & Naukari are great portals to find jobs.
Set up job alerts using keywords like โData Analystโ so youโll get notified as soon as something new comes up.
๐ธ๐๐๐ข๐ฅ๐จ๐ซ ๐๐จ๐ฎ๐ซ ๐๐๐ฌ๐ฎ๐ฆ๐
Donโt send the same resume to every job.
Take time to highlight the skills and tools that the job description asks for, like SQL, Power BI, or Excel. It helps your resume get noticed by software that scans for keywords (ATS).
๐ธ๐๐ฌ๐ ๐๐ข๐ง๐ค๐๐๐๐ง
Connect with recruiters and employees from your target companies. Ask for referrals when any jib opening is poster
Engage with data-related content and share your own work (like project insights or dashboards).
๐ธ๐๐ก๐๐๐ค ๐๐จ๐ฆ๐ฉ๐๐ง๐ฒ ๐๐๐๐ฌ๐ข๐ญ๐๐ฌ ๐๐๐ ๐ฎ๐ฅ๐๐ซ๐ฅ๐ฒ
Most big companies post jobs directly on their websites first.
Create a list of companies youโre interested in and keep checking their careers page. Itโs a good way to find openings early before they post on job portals.
๐ธ๐
๐จ๐ฅ๐ฅ๐จ๐ฐ ๐๐ฉ ๐๐๐ญ๐๐ซ ๐๐ฉ๐ฉ๐ฅ๐ฒ๐ข๐ง๐
After applying to a job, it helps to follow up with a quick message on LinkedIn. You can send a polite note to recruiter and aks for the update on your candidature.
Repost from Artificial Intelligence
๐ง๐ผ๐ฝ ๐ฑ ๐ฌ๐ผ๐๐ง๐๐ฏ๐ฒ ๐๐ต๐ฎ๐ป๐ป๐ฒ๐น๐ ๐ณ๐ผ๐ฟ ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ๐ ๐ฎ๐๐๐ฒ๐ฟ๐๐
Want to become a Data Analyst but donโt know where to start? ๐งโ๐ปโจ๏ธ
You donโt need to spend thousands on courses. In fact, some of the best free learning resources are already on YouTube โ taught by industry professionals who break down everything step by step.๐๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/47f3UOJ
Start with just one channel, stay consistent, and within months, youโll have the confidence (and portfolio) to apply for data analyst roles.โ
๏ธ
How to master Python from scratch๐
1. Setup and Basics ๐
- Install Python ๐ฅ๏ธ: Download Python and set it up.
- Hello, World! ๐: Write your first Hello World program.
2. Basic Syntax ๐
- Variables and Data Types ๐: Learn about strings, integers, floats, and booleans.
- Control Structures ๐: Understand if-else statements, for loops, and while loops.
- Functions ๐ ๏ธ: Write reusable blocks of code.
3. Data Structures ๐
- Lists ๐: Manage collections of items.
- Dictionaries ๐: Store key-value pairs.
- Tuples ๐ฆ: Work with immutable sequences.
- Sets ๐ข: Handle collections of unique items.
4. Modules and Packages ๐ฆ
- Standard Library ๐: Explore built-in modules.
- Third-Party Packages ๐: Install and use packages with pip.
5. File Handling ๐
- Read and Write Files ๐
- CSV and JSON ๐
6. Object-Oriented Programming ๐งฉ
- Classes and Objects ๐๏ธ
- Inheritance and Polymorphism ๐จโ๐ฉโ๐ง
7. Web Development ๐
- Flask ๐ผ: Start with a micro web framework.
- Django ๐ฆ: Dive into a full-fledged web framework.
8. Data Science and Machine Learning ๐ง
- NumPy ๐: Numerical operations.
- Pandas ๐ผ: Data manipulation and analysis.
- Matplotlib ๐ and Seaborn ๐: Data visualization.
- Scikit-learn ๐ค: Machine learning.
9. Automation and Scripting ๐ค
- Automate Tasks ๐ ๏ธ: Use Python to automate repetitive tasks.
- APIs ๐: Interact with web services.
10. Testing and Debugging ๐
- Unit Testing ๐งช: Write tests for your code.
- Debugging ๐: Learn to debug efficiently.
11. Advanced Topics ๐
- Concurrency and Parallelism ๐
- Decorators ๐ and Generators โ๏ธ
- Web Scraping ๐ธ๏ธ: Extract data from websites using BeautifulSoup and Scrapy.
12. Practice Projects ๐ก
- Calculator ๐งฎ
- To-Do List App ๐
- Weather App โ๏ธ
- Personal Blog ๐
13. Community and Collaboration ๐ค
- Contribute to Open Source ๐
- Join Coding Communities ๐ฌ
- Participate in Hackathons ๐
14. Keep Learning and Improving ๐
- Read Books ๐: Like "Automate the Boring Stuff with Python".
- Watch Tutorials ๐ฅ: Follow video courses and tutorials.
- Solve Challenges ๐งฉ: On platforms like LeetCode, HackerRank, and CodeWars.
15. Teach and Share Knowledge ๐ข
- Write Blogs โ๏ธ
- Create Video Tutorials ๐น
- Mentor Others ๐จโ๐ซ
I have curated the best interview resources to crack Python Interviews ๐๐
https://topmate.io/coding/898340
Hope you'll like it
Like this post if you need more resources like this ๐โค๏ธ
Repost from Artificial Intelligence
๐ฏ ๐๐ฟ๐ฒ๐ฒ ๐ ๐ถ๐ฐ๐ฟ๐ผ๐๐ผ๐ณ๐ ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐๐ถ๐๐ต ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ฒ๐ ๐๐ผ๐ผ๐๐ ๐ฌ๐ผ๐๐ฟ ๐๐ฎ๐ฟ๐ฒ๐ฒ๐ฟ ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฑ๐
Want to earn free certificates and badges from Microsoft? ๐
These courses are your golden ticket to mastering in-demand tech skills while boosting your resume with official Microsoft credentials๐งโ๐ป๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4mlCvPu
These certifications will help you stand out in interviews and open new career opportunities in techโ
๏ธ
Common Requirements for data analyst role ๐
๐ Must be proficient in writing complex SQL Queries.
๐ Understand business requirements in BI context and design data models to transform raw data into meaningful insights.
๐ Connecting data sources, importing data, and transforming data for Business intelligence.
๐ Strong working knowledge in Excel and visualization tools like PowerBI, Tableau or QlikView
๐ Developing visual reports, KPI scorecards, and dashboards using Power BI desktop.
Nowadays, recruiters primary focus on SQL & BI skills for data analyst roles. So try practicing SQL & create some BI projects using Tableau or Power BI.
*Here are some essential WhatsApp Channels with important resources:*
โฏ Jobs โ https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J
โฏ SQL โ https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
โฏ Power BI โ https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c
โฏ Data Analysts โ https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
โฏ Python โ https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
I am planning to come up with interview series as well to share some essential questions based on my experience in data analytics field.
Like this post if you want me to start the interview series ๐โค๏ธ
Hope it helps :)
Data Analytics Interview Topics in structured way :
๐ตPython: Data Structures: Lists, tuples, dictionaries, sets Pandas: Data manipulation (DataFrame operations, merging, reshaping) NumPy: Numeric computing, arrays Visualization: Matplotlib, Seaborn for creating charts
๐ตSQL: Basic : SELECT, WHERE, JOIN, GROUP BY, ORDER BY Advanced : Subqueries, nested queries, window functions DBMS: Creating tables, altering schema, indexing Joins: Inner join, outer join, left/right join Data Manipulation: UPDATE, DELETE, INSERT statements Aggregate Functions: SUM, AVG, COUNT, MAX, MIN
๐ตExcel: Formulas & Functions: VLOOKUP, HLOOKUP, IF, SUMIF, COUNTIF Data Cleaning: Removing duplicates, handling errors, text-to-columns PivotTables Charts and Graphs What-If Analysis: Scenario Manager, Goal Seek, Solver
๐ตPower BI:
Data Modeling: Creating relationships between datasets
Transformation: Cleaning & shaping data using
Power Query Editor Visualization: Creating interactive reports and dashboards
DAX (Data Analysis Expressions): Formulas for calculated columns, measures Publishing and sharing reports, scheduling data refresh
๐ต Statistics Fundamentals: Mean, median, mode Variance, standard deviation Probability distributions Hypothesis testing, p-values, confidence intervals
๐ตData Manipulation and Cleaning: Data preprocessing techniques (handling missing values, outliers), Data normalization and standardization Data transformation Handling categorical data
๐ตData Visualization: Chart types (bar, line, scatter, histogram, boxplot) Data visualization libraries (matplotlib, seaborn, ggplot) Effective data storytelling through visualization
Also showcase these skills using data portfolio if possible
Like for more content like this ๐
๐ฒ ๐๐ฟ๐ฒ๐ฒ ๐๐๐น๐น ๐ง๐ฒ๐ฐ๐ต ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐ฌ๐ผ๐ ๐๐ฎ๐ป ๐ช๐ฎ๐๐ฐ๐ต ๐ฅ๐ถ๐ด๐ต๐ ๐ก๐ผ๐๐
Ready to level up your tech game without spending a rupee? These 6 full-length courses are beginner-friendly, 100% free, and packed with practical knowledge๐๐งโ๐
Whether you want to code in Python, hack ethically, or build your first Android app โ these videos are your shortcut to real tech skills๐ฑ๐ป
๐๐ข๐ง๐ค๐:-
https://pdlink.in/42V73k4
Save this list and start crushing your tech goals today!โ
๏ธ
5 Essential Portfolio Projects for data analysts ๐๐
1. Exploratory Data Analysis (EDA) on a Real Dataset: Choose a dataset related to your interests, perform thorough EDA, visualize trends, and draw insights. This showcases your ability to understand data and derive meaningful conclusions.
Free websites to find datasets: https://t.me/DataPortfolio/8
2. Predictive Modeling Project: Build a predictive model, such as a linear regression or classification model. Use a dataset to train and test your model, and evaluate its performance. Highlight your skills in machine learning and statistical analysis.
3. Data Cleaning and Transformation: Take a messy dataset and demonstrate your skills in cleaning and transforming data. Showcase your ability to handle missing values, outliers, and prepare data for analysis.
4. Dashboard Creation: Utilize tools like Tableau or Power BI to create an interactive dashboard. This project demonstrates your ability to present data insights in a visually appealing and user-friendly manner.
5. Time Series Analysis: Work with time-series data to forecast future trends. This could involve stock prices, weather data, or any other time-dependent dataset. Showcase your understanding of time-series concepts and forecasting techniques.
Share with credits: https://t.me/sqlspecialist
Like it if you need more posts like this ๐โค๏ธ
Hope it helps :)
๐ ๐๐ฑ๐ฉ๐ฅ๐จ๐ซ๐ข๐ง๐ ๐๐๐ญ๐ ๐๐ซ๐จ๐๐๐ฌ๐ฌ๐ข๐จ๐ง๐ฌ ๐ข๐ง ๐ญ๐ก๐ ๐๐ ๐๐ง๐๐ฎ๐ฌ๐ญ๐ซ๐ฒ ๐
The world of data is vast and diverse, and understanding the nuances between different data roles can help both professionals and organizations thrive.
This visual breakdown offers a fantastic comparison of key data roles:
๐ ๐๐๐ญ๐ ๐๐ง๐ ๐ข๐ง๐๐๐ซ โ The backbone of any data-driven team. They build robust data pipelines, manage infrastructure, and ensure data is accessible and reliable. Strong in deployment, ML-Ops, and working closely with Data Scientists.
๐ ๐๐ ๐๐ง๐ ๐ข๐ง๐๐๐ซ โ These experts bridge software engineering and data science. They focus on building and deploying machine learning models at scale, emphasizing ML Ops, experimentation, and data analysis.
โค๏ธ ๐๐๐ญ๐ ๐๐๐ข๐๐ง๐ญ๐ข๐ฌ๐ญ โ The creative problem solvers. They blend statistical analysis, machine learning, and storytelling to uncover insights and predict future trends. Skilled in experimentation, ML modeling, and storytelling.
๐ ๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ฌ๐ญ โ Their strengths lie in reporting, business insights, and visualization.
Top 10 machine Learning algorithms for beginners ๐๐
1. Linear Regression: A simple algorithm used for predicting a continuous value based on one or more input features.
2. Logistic Regression: Used for binary classification problems, where the output is a binary value (0 or 1).
3. Decision Trees: A versatile algorithm that can be used for both classification and regression tasks, based on a tree-like structure of decisions.
4. Random Forest: An ensemble learning method that combines multiple decision trees to improve the accuracy and robustness of the model.
5. Support Vector Machines (SVM): Used for both classification and regression tasks, with the goal of finding the hyperplane that best separates the classes.
6. K-Nearest Neighbors (KNN): A simple algorithm that classifies a new data point based on the majority class of its k nearest neighbors in the feature space.
7. Naive Bayes: A probabilistic algorithm based on Bayes' theorem that is commonly used for text classification and spam filtering.
8. K-Means Clustering: An unsupervised learning algorithm used for clustering data points into k distinct groups based on similarity.
9. Principal Component Analysis (PCA): A dimensionality reduction technique used to reduce the number of features in a dataset while preserving the most important information.
10. Gradient Boosting Machines (GBM): An ensemble learning method that builds a series of weak learners to create a strong predictive model through iterative optimization.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.me/datasciencefun
Like if you need similar content ๐๐
