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Data Analytics & AI | SQL Interviews | Power BI Resources

Data Analytics & AI | SQL Interviews | Power BI Resources

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🔓Explore the fascinating world of Data Analytics & Artificial Intelligence 💻 Best AI tools, free resources, and expert advice to land your dream tech job. Admin: @coderfun Buy ads: https://telega.io/c/Data_Visual

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📈 تحلیل کانال تلگرام Data Analytics & AI | SQL Interviews | Power BI Resources

کانال Data Analytics & AI | SQL Interviews | Power BI Resources (@data_visual) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 27 213 مشترک است و جایگاه 7 206 را در دسته آموزش و رتبه 15 981 را در منطقه الهند دارد.

📊 شاخص‌های مخاطب و پویایی

از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 27 213 مشترک جذب کرده است.

بر اساس آخرین داده‌ها در تاریخ 14 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 255 و در ۲۴ ساعت گذشته برابر 26 بوده و همچنان دسترسی گسترده‌ای حفظ شده است.

  • وضعیت تأیید: تأیید نشده
  • نرخ تعامل (ER): میانگین تعامل مخاطب 3.99% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 0.72% واکنش نسبت به کل مشترکان کسب می‌کند.
  • دسترسی پست‌ها: هر پست به طور میانگین 0 بازدید دریافت می‌کند. در اولین روز معمولاً 197 بازدید جمع‌آوری می‌شود.
  • واکنش‌ها و تعامل: مخاطبان به‌طور فعال حمایت می‌کنند؛ میانگین واکنش به هر پست 0 است.
  • علایق موضوعی: محتوا بر موضوعات کلیدی مانند |--, sql, learning, analytic, visualization تمرکز دارد.

📝 توضیح و سیاست محتوایی

نویسنده این فضا را محل بیان دیدگاه‌های شخصی توصیف می‌کند:
🔓Explore the fascinating world of Data Analytics & Artificial Intelligence 💻 Best AI tools, free resources, and expert advice to land your dream tech job. Admin: @coderfun Buy ads: https://telega.io/c/Data_Visual

به لطف به‌روزرسانی‌های پرتکرار (آخرین داده در تاریخ 15 ژوئن, 2026)، کانال همواره به‌روز و دارای دسترسی بالاست. تحلیل‌ها نشان می‌دهد مخاطبان به‌طور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته آموزش تبدیل کرده‌اند.

27 213
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+2624 ساعت
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+25530 روز
آرشیو پست ها
Starting your journey as a data analyst is an amazing start for your career. As you progress, you might find new areas that pique your interest: • Data Science: If you enjoy diving deep into statistics, predictive modeling, and machine learning, this could be your next challenge. • Data Engineering: If building and optimizing data pipelines excites you, this might be the path for you. • Business Analysis: If you're passionate about translating data into strategic business insights, consider transitioning to a business analyst role. But remember, even if you stick with data analysis, there's always room for growth, especially with the evolving landscape of AI. No matter where your path leads, the key is to start now.

CHATGPT Ultimate Guide
CHATGPT Ultimate Guide

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💻 String Functions in SQL If you're working with databases, string manipulation is a must have! Here is a quick overview of common SQL string functions 👇 --- 📝 1. CONCAT() - Description: Concatenates two or more strings. - Syntax: SELECT CONCAT(string1, string2, ...) AS concatenated_string - Example: SELECT CONCAT(first_name, ' ', last_name) AS full_name --- 📝 2. SUBSTRING()/SUBSTR() - Description: Extracts a substring from a string. - Syntax: SELECT SUBSTRING(string FROM start_position FOR length) AS substring - Example: SELECT SUBSTRING(product_name FROM 1 FOR 5) AS short_name --- 📝 3. CHAR_LENGTH()/LENGTH() - Description: Returns the length of a string. - Syntax: SELECT CHAR_LENGTH(string) AS length - Example: SELECT CHAR_LENGTH(product_name) AS product_name_length --- 📝 4. UPPER() - Description: Converts all characters to uppercase. - Syntax: SELECT UPPER(string) AS uppercase_string - Example: SELECT UPPER(first_name) AS upper_name --- 📝 5. LOWER() - Description: Converts all characters to lowercase. - Syntax: SELECT LOWER(string) AS lowercase_string - Example: SELECT LOWER(last_name) AS lower_name --- 📝 6. TRIM() - Description: Removes specified prefixes/suffixes or whitespace from a string. - Syntax: SELECT TRIM([LEADING | TRAILING | BOTH] characters FROM string) AS trimmed_string - Example: SELECT TRIM(TRAILING ' ' FROM full_name) AS trimmed_name --- 📝 7. LEFT() - Description: Returns a specified number of characters from the left of a string. - Syntax: SELECT LEFT(string, num_characters) AS left_string - Example: SELECT LEFT(product_name, 5) AS left_product_name --- 📝 8. RIGHT() - Description: Returns a specified number of characters from the right of a string. - Syntax: SELECT RIGHT(string, num_characters) AS right_string - Example: SELECT RIGHT(order_number, 4) AS right_order_number --- 📝 9. REPLACE() - Description: Replaces occurrences of a substring within a string. - Syntax: SELECT REPLACE(string, old_substring, new_substring) AS replaced_string - Example: SELECT REPLACE(description, 'old', 'new') AS updated_description I have curated essential SQL Interview Resources👇 https://topmate.io/analyst/864764 Like this post if you need more 👍❤️ Hope it helps :)

Roadmap to Becoming a Python Developer 🚀 1. Basics 🌱 - Learn programming fundamentals and Python syntax. 2. Core Python 🧠 - Master data structures, functions, and OOP. 3. Advanced Python 📈 - Explore modules, file handling, and exceptions. 4. Web Development 🌐 - Use Django or Flask; build REST APIs. 5. Data Science 📊 - Learn NumPy, pandas, and Matplotlib. 6. Projects & Practice💡 - Build projects, contribute to open-source, join communities. Python Interview Q&A: https://topmate.io/analyst/907371 Like for more ❤️ ENJOY LEARNING 👍👍

Practical Machine Learning and Image Processing.pdf4.80 MB

Complete Syllabus for Data Analytics interview: SQL: 1. Basic    - SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING    - Basic JOINS (INNER, LEFT, RIGHT, FULL)    - Creating and using simple databases and tables 2. Intermediate    - Aggregate functions (COUNT, SUM, AVG, MAX, MIN)    - Subqueries and nested queries - Common Table Expressions (WITH clause)    - CASE statements for conditional logic in queries 3. Advanced    - Advanced JOIN techniques (self-join, non-equi join)    - Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)    - optimization with indexing    - Data manipulation (INSERT, UPDATE, DELETE) Python: 1. Basic    - Syntax, variables, data types (integers, floats, strings, booleans)    - Control structures (if-else, for and while loops)    - Basic data structures (lists, dictionaries, sets, tuples)    - Functions, lambda functions, error handling (try-except)    - Modules and packages 2. Pandas & Numpy    - Creating and manipulating DataFrames and Series    - Indexing, selecting, and filtering data    - Handling missing data (fillna, dropna)    - Data aggregation with groupby, summarizing data    - Merging, joining, and concatenating datasets 3. Basic Visualization    - Basic plotting with Matplotlib (line plots, bar plots, histograms)    - Visualization with Seaborn (scatter plots, box plots, pair plots)    - Customizing plots (sizes, labels, legends, color palettes)    - Introduction to interactive visualizations (e.g., Plotly) Excel: 1. Basic    - Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)    - Introduction to charts and basic data visualization    - Data sorting and filtering    - Conditional formatting 2. Intermediate    - Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)    - PivotTables and PivotCharts for summarizing data    - Data validation tools    - What-if analysis tools (Data Tables, Goal Seek) 3. Advanced    - Array formulas and advanced functions    - Data Model & Power Pivot - Advanced Filter - Slicers and Timelines in Pivot Tables    - Dynamic charts and interactive dashboards Power BI: 1. Data Modeling    - Importing data from various sources    - Creating and managing relationships between different datasets    - Data modeling basics (star schema, snowflake schema) 2. Data Transformation    - Using Power Query for data cleaning and transformation    - Advanced data shaping techniques    - Calculated columns and measures using DAX 3. Data Visualization and Reporting   - Creating interactive reports and dashboards    - Visualizations (bar, line, pie charts, maps)    - Publishing and sharing reports, scheduling data refreshes Statistics Fundamentals: Mean, Median, Mode, Standard Deviation, Variance, Probability Distributions, Hypothesis Testing, P-values, Confidence Intervals, Correlation, Simple Linear Regression, Normal Distribution, Binomial Distribution, Poisson Distribution. Like for more 😄❤️

SQL Functions For Data Analysis .pdf2.35 KB

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Artificial Intelligence for Robotics Francis X. Govers, 2018

Bayesian Data Analysis
Bayesian Data Analysis

Any person learning deep learning or artificial intelligence in particular, know that there are ultimately two paths that the
Any person learning deep learning or artificial intelligence in particular, know that there are ultimately two paths that they can go: 1. Computer vision 2. Natural language processing. I outlined a roadmap for computer vision I believe many beginners will find helpful. Artificial Intelligence

Visualization-1.pdf6.00 KB

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Pandas cheatsheet.pdf5.97 KB

Data Analyst Interview Questions

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Scientific Visualisation Nicolai P. Rougier, 2021

Data Visualization Kieran Healy, 2019