Data Science & Machine Learning
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Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free For collaborations: @love_data
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Сітка:📈 Аналітичний огляд Telegram-каналу Data Science & Machine Learning
Канал Data Science & Machine Learning (@datasciencefun) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 77 282 підписників, посідаючи 2 004 місце в категорії Освіта та 4 033 місце у регіоні Індія.
📊 Показники аудиторії та динаміка
З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 77 282 підписників.
За останніми даними від 28 серпня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 347, а за останні 24 години на 6, загальне охоплення залишається високим.
- Статус верифікації: Не верифікований
- Рівень залученості (ER): Середній показник залученості аудиторії становить 2.66%. Протягом перших 24 годин після публікації контент зазвичай збирає 1.12% реакцій від загальної кількості підписників.
- Охоплення публікацій: В середньому кожен допис отримує 2 057 переглядів. Протягом першої доби публікація в середньому набирає 866 переглядів.
- Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 3.
- Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як learning, accuracy, distribution, panda, dataset.
📝 Опис та контентна політика
Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free
For collaborations: @love_data”
Завдяки високій частоті оновлень (останні дані отримано 29 серпня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Освіта.
77 282
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✅ Tableau Calculated Fields
What are Calculated Fields?
Custom formulas you create in Tableau to do calculations beyond default aggregations. Think: Excel formulas inside Tableau.
Why use them?
1. Create new metrics like Profit Margin, YoY Growth
2. Categorize data: High/Medium/Low sales
3. Date math: Days between orders, fiscal periods
4. Conditional logic: IF/THEN/ELSE rules
Basic Syntax
IF [Sales] > 10000 THEN "High" ELSE "Low" END
[Profit] / [Sales] → Profit Ratio
DATEDIFF('day', [Order Date], [Ship Date])
Key Functions
Logical: IF, IIF, CASE
Math: ROUND, ABS, SQRT
Date: YEAR, MONTH, DATEDIFF
String: LEFT, RIGHT, CONTAINS
Pro Tips
1. Calculated fields compute row-level or aggregate depending on formula
2. Use ATTR() to avoid aggregation errors
3. Name fields clearly: Profit Margin % not Calc1
4. Test with a few rows before using in dashboards
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What type of data is considered a Dimension in Tableau?
Which of the following is a Measure in Tableau?
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👉 Tableau is one of the most popular Data Visualization and Business Intelligence (BI) tools.
It helps transform raw data into:
✔ Interactive dashboards
✔ Visual reports
✔ Business insights
🔹 1. What is Tableau?
Tableau is a drag-and-drop data visualization tool used by:
✔ Data Analysts
✔ Business Analysts
✔ Data Scientists
✔ Managers & Executives
👉 It allows users to analyze data without extensive coding.
🔥 2. Why Tableau is Popular?
✔ Easy to learn
✔ Interactive dashboards
✔ Fast visualization creation
✔ Connects to multiple data sources
🔹 3. Tableau Products
✅ Tableau Desktop: Used to create visualizations and dashboards.
✅ Tableau Server: Used to publish and share dashboards.
✅ Tableau Public: Free version for learning and sharing public dashboards.
🔹 4. Connecting Data Sources ⭐
Tableau can connect to:
✔ Excel files
✔ CSV files
✔ SQL Databases
✔ Cloud platforms
✔ APIs
🔹 5. Tableau Interface
Main areas:
✅ Data Pane: Contains fields and tables.
✅ Shelves: Used to build charts.
✅ Marks Card: Controls color, size, labels, and details.
✅ Worksheet: Area where visualizations are created.
🔥 6. Dimensions vs Measures ⭐
Dimensions: Categorical data.
Examples: ✔ Region, ✔ Product, ✔ Customer Name
Measures: Numerical data.
Examples: ✔ Sales, ✔ Profit, ✔ Quantity
🔹 7. Common Charts in Tableau
✔ Bar Chart
✔ Line Chart
✔ Pie Chart
✔ Map
✔ Scatter Plot
✔ Heat Map
🔹 8. Filters in Tableau
Filters help users focus on specific data.
Example:
✔ View sales for only one region,
✔ Show data for a selected year
🔹 9. Dashboards in Tableau ⭐
A dashboard combines multiple charts into one screen.
Example:
📊 Sales Trend,
📈 Profit Analysis,
🌍 Regional Performance
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