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📝 Description: This is a repo with links to everything you'd ever want to learn about data engineering

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📖 Readme: https://github.com/DataExpert-io/data-engineer-handbook#readme

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Data Science Interview Prep Guide

1️⃣ Core Data Science Concepts
• What is Data Science vs Data Analytics vs ML
• Descriptive, diagnostic, predictive, prescriptive analytics
• Structured vs unstructured data
• Data-driven decision making
• Business problem framing

2️⃣ Statistics  Probability (Non-Negotiable)
• Mean, median, variance, standard deviation
• Probability distributions (normal, binomial, Poisson)
• Hypothesis testing  p-values
• Confidence intervals
• Correlation vs causation
• Sampling  bias

3️⃣ Data Cleaning  EDA
• Handling missing values  outliers
• Data normalization  scaling
• Feature engineering
• Exploratory data analysis (EDA)
• Data leakage detection
• Data quality validation

4️⃣ Python  SQL for Data Science
• Python (NumPy, Pandas)
• Data manipulation  transformations
• Vectorization  performance optimization
• SQL joins, CTEs, window functions
• Writing business-ready queries

5️⃣ Machine Learning Essentials
• Supervised vs unsupervised learning
• Regression vs classification
• Model selection  baseline models
• Overfitting, underfitting
• Bias–variance tradeoff
• Hyperparameter tuning

6️⃣ Model Evaluation  Metrics
• Accuracy, precision, recall, F1
• ROC  AUC
• Confusion matrix
• RMSE, MAE, log loss
• Metrics for imbalanced data
• Linking ML metrics to business KPIs

7️⃣ Real-World  Deployment Knowledge
• Feature stores
• Model deployment (batch vs real-time)
• Model monitoring  drift
• Experiment tracking
• Data  model versioning
• Model explainability (business-friendly)

8️⃣ Must-Have Projects
• Customer churn prediction
• Fraud detection
• Sales or demand forecasting
• Recommendation system
• End-to-end ML pipeline
• Business-focused case study

9️⃣ Common Interview Questions
• Walk me through an end-to-end DS project
• How do you choose evaluation metrics?
• How do you handle imbalanced data?
• How do you explain a model to leadership?
• How do you improve a failing model?

🔟 Pro Tips
✔️ Always connect answers to business impact 
✔️ Explain why, not just how 
✔️ Be clear about trade-offs 
✔️ Discuss failures  learnings 
✔️ Show structured thinking 

https://t.iss.one/DataScienceN

If you’re prepping for a data science role, this guide has EVERYTHING you need. Check it out! 💡

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==================================
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One day or Day one. You decide.

Data Science edition.

𝗢𝗻𝗲 𝗗𝗮𝘆 : I will learn SQL.
𝗗𝗮𝘆 𝗢𝗻𝗲: Download mySQL Workbench.

𝗢𝗻𝗲 𝗗𝗮𝘆: I will build my projects for my portfolio.
𝗗𝗮𝘆 𝗢𝗻𝗲: Look on Kaggle for a dataset to work on.

𝗢𝗻𝗲 𝗗𝗮𝘆: I will master statistics.
𝗗𝗮𝘆 𝗢𝗻𝗲: Start the free Khan Academy Statistics and Probability course.

𝗢𝗻𝗲 𝗗𝗮𝘆: I will learn to tell stories with data.
𝗗𝗮𝘆 𝗢𝗻𝗲: Install Tableau Public and create my first chart.

𝗢𝗻𝗲 𝗗𝗮𝘆: I will become a Data Scientist.
𝗗𝗮𝘆 𝗢𝗻𝗲: Update my resume and apply to some Data Science job postings.


https://t.iss.one/DataScienceN

Don’t wait for the perfect moment. Start today

Install that tool, pick that dataset, take that course. Every big goal begins with Day One 💪

Here is a powerful 𝗜𝗡𝗧𝗘𝗥𝗩𝗜𝗘𝗪 𝗧𝗜𝗣 to help you land a job!

Most people who are skilled enough would be able to clear technical rounds with ease.

But when it comes to 𝗯𝗲𝗵𝗮𝘃𝗶𝗼𝗿𝗮𝗹/𝗰𝘂𝗹𝘁𝘂𝗿𝗲 𝗳𝗶𝘁 rounds, some folks may falter and lose the potential offer.

Many companies schedule a behavioral round with a top-level manager in the organization to understand the culture fit (except for freshers).

One needs to clear this round to reach the salary negotiation round.

Here are some tips to clear such rounds:

1️⃣ Once the HR schedules the interview, try to find the LinkedIn profile of the interviewer using the name in their email ID.

2️⃣ Learn more about his/her past experiences and try to strike up a conversation on that during the interview.

3️⃣ This shows that you have done good research and also helps strike a personal connection.

4️⃣ Also, this is the round not just to evaluate if you're a fit for the company, but also to assess if the company is a right fit for you.

5️⃣ Hence, feel free to ask many questions about your role and company to get a clear understanding before taking the offer. This shows that you really care about the role you're getting into.

💡 𝗕𝗼𝗻𝘂𝘀 𝘁𝗶𝗽 - Be polite yet assertive in such interviews. It impresses a lot of senior folks.


https://t.iss.one/DataScienceN

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==================================
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==================================
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==================================
🧠 By: https://t.iss.one/DataScienceM

🔹 DATA SCIENCE – INTERVIEW REVISION SHEET*

*1️⃣ What is Data Science?*
> “Data science is the process of using data, statistics, and machine learning to extract insights and build predictive or decision-making models.”

Difference from Data Analytics:
- Data Analytics → past & present (what/why)
- Data Science → future & automation (what will happen)

*2️⃣ Data Science Lifecycle (Very Important)*
1. Business problem understanding
2. Data collection
3. Data cleaning & preprocessing
4. Exploratory Data Analysis (EDA)
5. Feature engineering
6. Model building
7. Model evaluation
8. Deployment & monitoring
Interview line:
> “I always start from business understanding, not the model.”

*3️⃣ Data Types*
- Structured → tables, SQL
- Semi-structured → JSON, logs
- Unstructured → text, images

*4️⃣ Statistics You MUST Know*
- Central tendency: Mean, Median (use when outliers exist)
- Spread: Variance, Standard deviation
- Correlation ≠ causation
- Normal distribution
- Skewness (income → right skewed)

*5️⃣ Data Cleaning & Preprocessing*
Steps you should say in interviews:
1. Handle missing values
2. Remove duplicates
3. Treat outliers
4. Encode categorical variables
5. Scale numerical data
Scaling:
- Min-Max → bounded range
- Standardization → normal distribution

*6️⃣ Feature Engineering (Interview Favorite)*
> “Feature engineering is creating meaningful input variables that improve model performance.”
Examples:
- Extract month from date
- Create customer lifetime value
- Binning age groups

*7️⃣ Machine Learning Basics*
- Supervised learning: Regression, Classification
- Unsupervised learning: Clustering, Dimensionality reduction

*8️⃣ Common Algorithms (Know WHEN to use)*
- Regression: Linear regression → continuous output
- Classification: Logistic regression, Decision tree, Random forest, SVM
- Unsupervised: K-Means → segmentation, PCA → dimensionality reduction

*9️⃣ Overfitting vs Underfitting*
- Overfitting → model memorizes training data
- Underfitting → model too simple
Fixes:
- Regularization
- More data
- Cross-validation

*🔟 Model Evaluation Metrics*
- Classification: Accuracy, Precision, Recall, F1 score, ROC-AUC
- Regression: MAE, RMSE
Interview line:
> “Metric selection depends on business problem.”

*1️⃣1️⃣ Imbalanced Data Techniques*
- Class weighting
- Oversampling / undersampling
- SMOTE
- Metric preference: Precision, Recall, F1, ROC-AUC

*1️⃣2️⃣ Python for Data Science*
Core libraries:
- NumPy
- Pandas
- Matplotlib / Seaborn
- Scikit-learn
Must know:
- loc vs iloc
- Groupby
- Vectorization

*1️⃣3️⃣ Model Deployment (Basic Understanding)*
- Batch prediction
- Real-time prediction
- Model monitoring
- Model drift
Interview line:
> “Models must be monitored because data changes over time.”

*1️⃣4️⃣ Explain Your Project (Template)*
> “The goal was _. I cleaned the data using _. I performed EDA to identify _. I built _ model and evaluated using _. The final outcome was _.”

*1️⃣5️⃣ HR-Style Data Science Answers*
Why data science?
> “I enjoy solving complex problems using data and building models that automate decisions.”
Biggest challenge:
“Handling messy real-world data.”
Strength:
“Strong foundation in statistics and ML.”

*🔥 LAST-DAY INTERVIEW TIPS*
- Explain intuition, not math
- Don’t jump to algorithms immediately
- Always connect model → business value
- Say assumptions clearly