Data Analysis Questions
Автор: Tech - jroshan
Загружено: 2025-06-07
Просмотров: 107
🎯 Cracking Data Analytics Interviews: 25 Most Asked Questions – With Sample Answers! 📊💡
🔍 Core Concepts & Process
1️⃣ What is data analytics?
👉 Extracting insights from raw data to support decision-making.
2️⃣ Data analytics vs data science vs BI?
👉 Analytics: Finds trends,
Data Science: Builds models,
BI: Visualizes and reports.
3️⃣ Data analysis lifecycle?
👉 Define ➡ Collect ➡ Clean ➡ Analyze ➡ Visualize ➡ Report.
4️⃣ Types of analysis?
👉 Descriptive, Diagnostic, Predictive, Prescriptive.
5️⃣ ETL importance?
👉 Moves raw data from source ➡ usable format for analysis.
🧹 Data Handling & Cleaning
6️⃣ How do you handle missing data?
👉 Remove, impute (mean/median), or model-based filling.
7️⃣ Why is data cleaning crucial?
👉 Garbage in = garbage out; clean data leads to reliable insights.
8️⃣ What is normalization?
👉 Scaling data to uniform ranges for better model performance.
9️⃣ What is EDA?
👉 Exploring distributions, correlations, and outliers using plots/stats.
🔟 Correlation vs causation?
👉 Correlation = relationship;
Causation = one variable causes change in another.
📐 Statistics & Sampling
1️⃣1️⃣ Handling outliers?
👉 Detect using IQR/Z-score, treat with cap, transform, or remove.
1️⃣2️⃣ What is statistical sampling?
👉 Selecting a representative subset to analyze large datasets.
1️⃣3️⃣ Measures of central tendency?
👉 Mean, Median, Mode – summarize data’s center.
🤖 Machine Learning Basics
1️⃣4️⃣ Supervised vs Unsupervised?
👉 Supervised = labeled data (e.g., regression),
Unsupervised = pattern discovery (e.g., clustering).
1️⃣5️⃣ What is a decision tree?
👉 Tree-structured ML model for classification/regression.
1️⃣6️⃣ Real-world project example?
👉 Share your project and impact in a STAR format (Situation, Task, Action, Result).
1️⃣7️⃣ Assessing model accuracy?
👉 Metrics like RMSE, MAE, Accuracy, Precision, Recall, F1.
📊 Tools, Communication & Ethics
1️⃣8️⃣ Data visualization tools used?
👉 Tableau, Power BI, Matplotlib, Seaborn, Plotly.
1️⃣9️⃣ Why is data privacy important?
👉 Protects user rights, ensures compliance (GDPR, HIPAA).
2️⃣0️⃣ How do you stay updated?
👉 Blogs, courses (Coursera, Udemy), conferences, Kaggle.
2️⃣1️⃣ Your tech stack?
👉 Python, SQL, Excel, Pandas, Scikit-learn, R, Tableau.
2️⃣2️⃣ Challenging project example?
👉 Highlight complexity, tools used, and solution impact.
2️⃣3️⃣ What is A/B testing?
👉 Split testing to compare variants using statistical hypothesis testing.
2️⃣4️⃣ Communicating findings?
👉 Use simple visuals + storytelling tailored to non-technical stakeholders.
2️⃣5️⃣ Ethics in analytics?
👉 Consent, transparency, fairness, and avoiding algorithmic bias.
📌 TIP: Always support your answers with examples from your work!
💬 Which of these questions have you faced in your interviews? Or which one do you want help preparing for?
💬 Comment below 👇
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