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@nika_ritshel: Як створюються круті фото 📸 Балетна фотосесія #ballerina #ballet #balletphotography #art #dancephotographer

Nika_Ritshel
Nika_Ritshel
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Saturday 22 April 2023 08:28:01 GMT
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miss.velvetviper
C H E Z N E A 😘 :
what type of camera, need to know?
2024-01-18 23:06:41
0
userwia2fdf97i
Sever :
как всегда🔥🔥🔥
2023-04-22 17:23:55
0
vala0098
😝LLL😝 :
😠😤🤑
2024-01-25 16:14:09
0
janicecfraser
Janice Fraser 🇨🇦 :
🥰🥰
2023-05-02 20:34:32
0
bonibonita888
Boni- Bonita🐶😍🐶 :
🥰🥰🥰🥰🥰
2023-05-28 03:00:25
0
vivi.lameilleuremaman
Vivi :
magnifique 🤩🤩
2023-04-23 07:59:03
0
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Data Analytics is not just Excel + SQL + dashboards. A real analytics workflow can look like this: Data Sources → Ingestion → Storage → Transformation → Analysis → Visualization → Insights → Decisions Here’s the stack broken down 👇 1️⃣ DATA SOURCES Where does the data come from? 🗄️ Databases 📄 CSV / Excel 🌐 APIs ☁️ Cloud platforms 📱 Applications 📊 Business systems 🔗 Web data Goal: Get access to useful raw data. ⸻ 2️⃣ DATA INGESTION Move data from different sources into your analytics environment. Common tools: 🔹 Python 🔹 APIs 🔹 Airflow 🔹 Fivetran 🔹 dbt 🔹 Cloud data pipelines Flow: Source → Pipeline → Data Platform ⸻ 3️⃣ DATA STORAGE Store data so it can be queried and analyzed. Databases: 🐘 PostgreSQL 🐬 MySQL 🗃️ SQL Server Warehouses: ☁️ Snowflake ☁️ BigQuery ☁️ Amazon Redshift Lake / Lakehouse: 🪣 Data Lake 🏞️ Delta Lake ⚡ Databricks ⸻ 4️⃣ DATA TRANSFORMATION Raw data is rarely ready for analysis. You need to: 🧹 Clean 🔄 Transform 🔗 Join 📐 Aggregate ✅ Validate 🧩 Create analytical tables Popular tools: SQL + Python + dbt ⸻ 5️⃣ DATA ANALYSIS Now you start asking questions. 📈 What happened? 🔎 Why did it happen? 📊 What patterns exist? 🎯 Which customers behave differently? 💰 Where are we losing revenue? Tools: 🐍 Python 🗄️ SQL 📊 Excel 📐 Statistics ⸻ 6️⃣ EXPLORATORY DATA ANALYSIS Before building fancy dashboards, understand the data. Check: • Missing values • Duplicates • Outliers • Distributions • Correlations • Trends • Relationships Python stack: Pandas + NumPy + Matplotlib + Seaborn ⸻ 7️⃣ DATA VISUALIZATION Turn numbers into something humans can understand. 📊 Bar charts → Comparisons 📈 Line charts → Trends 🔵 Scatter plots → Relationships 📦 Box plots → Distribution & outliers 🔥 Heatmaps → Patterns/correlation Tools: Power BI | Tableau | Looker | Python ⸻ 8️⃣ BUSINESS INTELLIGENCE Now connect analysis to business decisions. Track: 💰 Revenue 📈 Growth 👥 Customers 🛒 Conversion 📦 Inventory 📣 Marketing 💵 Profitability Build: Dashboards → KPIs → Reports → Decision Support ⸻ 9️⃣ ADVANCED ANALYTICS Analytics can go beyond describing what happened. You can use: 🔮 Predictive Analytics 🤖 Machine Learning 📈 Forecasting 👥 Customer Segmentation 🚨 Anomaly Detection 🎯 Recommendation Systems This is where Data Analytics starts connecting with Data Science. ⸻ 🔟 AUTOMATION & PRODUCTION A mature analytics system shouldn’t depend on someone manually running everything. Automate: 🔄 Data pipelines 📊 Dashboard refreshes 🚨 Alerts 📧 Reports 🧪 Data-quality checks 📈 Scheduled analysis Tools can include: Airflow + Python + SQL + Cloud + BI ⸻ 🧠 THE COMPLETE ANALYTICS FLOW DATA SOURCES ↓ INGESTION ↓ STORAGE ↓ TRANSFORMATION ↓ DATA QUALITY ↓ SQL / PYTHON ANALYSIS ↓ STATISTICS + EDA ↓ VISUALIZATION ↓ BI + DASHBOARDS ↓ INSIGHTS ↓ BUSINESS DECISIONS ↓ AUTOMATION ⸻ 🎯 WHAT SHOULD YOU LEARN? Beginner: Excel → SQL → Statistics → Visualization Intermediate: Python → Pandas → EDA → Power BI/Tableau → Projects Advanced: Data Warehousing → dbt → ETL/ELT → Cloud → Analytics Engineering Data Science path: Analytics → Statistics → Machine Learning → Predictive Modeling Data Engineering path: SQL → Python → Pipelines → Warehouses → Cloud → Distributed Systems 💡 You don’t need to learn the entire stack at once. Master the foundation first, then specialize. 📌 Save this as your Data Analytics roadmap. #DataAnalytics #DataAnalyst #DataScience #SQL #Python
Data Analytics is not just Excel + SQL + dashboards. A real analytics workflow can look like this: Data Sources → Ingestion → Storage → Transformation → Analysis → Visualization → Insights → Decisions Here’s the stack broken down 👇 1️⃣ DATA SOURCES Where does the data come from? 🗄️ Databases 📄 CSV / Excel 🌐 APIs ☁️ Cloud platforms 📱 Applications 📊 Business systems 🔗 Web data Goal: Get access to useful raw data. ⸻ 2️⃣ DATA INGESTION Move data from different sources into your analytics environment. Common tools: 🔹 Python 🔹 APIs 🔹 Airflow 🔹 Fivetran 🔹 dbt 🔹 Cloud data pipelines Flow: Source → Pipeline → Data Platform ⸻ 3️⃣ DATA STORAGE Store data so it can be queried and analyzed. Databases: 🐘 PostgreSQL 🐬 MySQL 🗃️ SQL Server Warehouses: ☁️ Snowflake ☁️ BigQuery ☁️ Amazon Redshift Lake / Lakehouse: 🪣 Data Lake 🏞️ Delta Lake ⚡ Databricks ⸻ 4️⃣ DATA TRANSFORMATION Raw data is rarely ready for analysis. You need to: 🧹 Clean 🔄 Transform 🔗 Join 📐 Aggregate ✅ Validate 🧩 Create analytical tables Popular tools: SQL + Python + dbt ⸻ 5️⃣ DATA ANALYSIS Now you start asking questions. 📈 What happened? 🔎 Why did it happen? 📊 What patterns exist? 🎯 Which customers behave differently? 💰 Where are we losing revenue? Tools: 🐍 Python 🗄️ SQL 📊 Excel 📐 Statistics ⸻ 6️⃣ EXPLORATORY DATA ANALYSIS Before building fancy dashboards, understand the data. Check: • Missing values • Duplicates • Outliers • Distributions • Correlations • Trends • Relationships Python stack: Pandas + NumPy + Matplotlib + Seaborn ⸻ 7️⃣ DATA VISUALIZATION Turn numbers into something humans can understand. 📊 Bar charts → Comparisons 📈 Line charts → Trends 🔵 Scatter plots → Relationships 📦 Box plots → Distribution & outliers 🔥 Heatmaps → Patterns/correlation Tools: Power BI | Tableau | Looker | Python ⸻ 8️⃣ BUSINESS INTELLIGENCE Now connect analysis to business decisions. Track: 💰 Revenue 📈 Growth 👥 Customers 🛒 Conversion 📦 Inventory 📣 Marketing 💵 Profitability Build: Dashboards → KPIs → Reports → Decision Support ⸻ 9️⃣ ADVANCED ANALYTICS Analytics can go beyond describing what happened. You can use: 🔮 Predictive Analytics 🤖 Machine Learning 📈 Forecasting 👥 Customer Segmentation 🚨 Anomaly Detection 🎯 Recommendation Systems This is where Data Analytics starts connecting with Data Science. ⸻ 🔟 AUTOMATION & PRODUCTION A mature analytics system shouldn’t depend on someone manually running everything. Automate: 🔄 Data pipelines 📊 Dashboard refreshes 🚨 Alerts 📧 Reports 🧪 Data-quality checks 📈 Scheduled analysis Tools can include: Airflow + Python + SQL + Cloud + BI ⸻ 🧠 THE COMPLETE ANALYTICS FLOW DATA SOURCES ↓ INGESTION ↓ STORAGE ↓ TRANSFORMATION ↓ DATA QUALITY ↓ SQL / PYTHON ANALYSIS ↓ STATISTICS + EDA ↓ VISUALIZATION ↓ BI + DASHBOARDS ↓ INSIGHTS ↓ BUSINESS DECISIONS ↓ AUTOMATION ⸻ 🎯 WHAT SHOULD YOU LEARN? Beginner: Excel → SQL → Statistics → Visualization Intermediate: Python → Pandas → EDA → Power BI/Tableau → Projects Advanced: Data Warehousing → dbt → ETL/ELT → Cloud → Analytics Engineering Data Science path: Analytics → Statistics → Machine Learning → Predictive Modeling Data Engineering path: SQL → Python → Pipelines → Warehouses → Cloud → Distributed Systems 💡 You don’t need to learn the entire stack at once. Master the foundation first, then specialize. 📌 Save this as your Data Analytics roadmap. #DataAnalytics #DataAnalyst #DataScience #SQL #Python

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