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✅ *Top Data Analytics Concepts for Interviews* 📊💼
*1️⃣ What is Data Analytics?*
🔹 The process of inspecting, cleaning, and modeling data to extract insights and support decision-making.
*2️⃣ Types of Data Analytics*
- *Descriptive:* What happened? (e.g., sales reports)
- *Diagnostic:* Why did it happen? (e.g., root cause analysis)
- *Predictive:* What will happen? (e.g., forecasting)
- *Prescriptive:* What should we do? (e.g., optimization strategies)
*3️⃣ Key Tools & Languages*
✅ Excel / Google Sheets – Pivot tables & basic analysis
✅ SQL – Database querying
✅ Python / R – Advanced analysis & visualization
✅ Power BI / Tableau – Dashboards & reporting
*4️⃣ Handling Missing Data*
- Remove rows/columns if minimal impact
- Impute values (mean, median, mode)
- Flag missing data for awareness
*5️⃣ Normalization vs Standardization*
🔹 *Normalization:* Rescale to 0–1
🔹 *Standardization:* Center to mean 0, std deviation 1
*6️⃣ KPI & Metrics*
📊 Key Performance Indicator – Measures business objectives
*Example:* Revenue growth, customer churn rate, website conversion
*7️⃣ Exploratory Data Analysis (EDA)*
- Summary statistics & data types
- Identify missing values & outliers
- Visualize with histograms, scatterplots, boxplots
- Detect correlations & trends
*8️⃣ SQL Joins Basics*
- *INNER JOIN:* Only matching rows
- *LEFT JOIN:* All left table rows + matches
- *RIGHT JOIN:* All right table rows + matches
*9️⃣ Correlation vs Causation*
🔹 Correlation: Variables move together
🔹 Causation: One variable directly affects another
*🔟 Reporting & Visualization Tips*
- Clear, concise charts
- Highlight insights, not just data
- Tailor dashboards for audience
💬 *Tap ❤️ for more!*
*1️⃣ What is Data Analytics?*
🔹 The process of inspecting, cleaning, and modeling data to extract insights and support decision-making.
*2️⃣ Types of Data Analytics*
- *Descriptive:* What happened? (e.g., sales reports)
- *Diagnostic:* Why did it happen? (e.g., root cause analysis)
- *Predictive:* What will happen? (e.g., forecasting)
- *Prescriptive:* What should we do? (e.g., optimization strategies)
*3️⃣ Key Tools & Languages*
✅ Excel / Google Sheets – Pivot tables & basic analysis
✅ SQL – Database querying
✅ Python / R – Advanced analysis & visualization
✅ Power BI / Tableau – Dashboards & reporting
*4️⃣ Handling Missing Data*
- Remove rows/columns if minimal impact
- Impute values (mean, median, mode)
- Flag missing data for awareness
*5️⃣ Normalization vs Standardization*
🔹 *Normalization:* Rescale to 0–1
🔹 *Standardization:* Center to mean 0, std deviation 1
*6️⃣ KPI & Metrics*
📊 Key Performance Indicator – Measures business objectives
*Example:* Revenue growth, customer churn rate, website conversion
*7️⃣ Exploratory Data Analysis (EDA)*
- Summary statistics & data types
- Identify missing values & outliers
- Visualize with histograms, scatterplots, boxplots
- Detect correlations & trends
*8️⃣ SQL Joins Basics*
- *INNER JOIN:* Only matching rows
- *LEFT JOIN:* All left table rows + matches
- *RIGHT JOIN:* All right table rows + matches
*9️⃣ Correlation vs Causation*
🔹 Correlation: Variables move together
🔹 Causation: One variable directly affects another
*🔟 Reporting & Visualization Tips*
- Clear, concise charts
- Highlight insights, not just data
- Tailor dashboards for audience
💬 *Tap ❤️ for more!*
Multithreading and concurrency in java.pdf
9.4 MB
Multithreading and Concurrency in Java 🚀
React ❤️ For More
React ❤️ For More
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Coding padhana chhod du kya?😂
Coding padhana chhod du kya?😂
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NVIDIA FREE Certification😭😔
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coding🧠👩💻👨💻
#coding #coder #java #javascript #programming #programmer #laptop #computerscience…
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coding🧠👩💻👨💻
#coding #coder #java #javascript #programming #programmer #laptop #computerscience…
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1. Generative AI Explained
https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-FX-07+V1
2. LLM with RAG Model
https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-FX-16+V1
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6. Introduction to CUDA
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7. Building RAG Agents with LLMs
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