📊 Data Science Tips for Beginners — Part 2

In Data Science, knowing tools is important—but knowing how to think about data is even more important. These tips will help you develop that mindset.

📌 1. Start With the Business Problem

Don't begin by asking: "Which Machine Learning algorithm should I use?"

First ask: "What problem are we trying to solve?"

A clear problem makes it easier to determine what data, analysis, and model you actually need.

📌 2. Identify the Target Variable

If you're building a predictive model, clearly identify what you're trying to predict.

For example:

Customer Data → Predict Customer Churn → Churn = Target

Everything else should be evaluated as a potential input or explanatory variable.

📌 3. Understand Your Data Before Modeling

Before applying any algorithm, investigate:

• Number of rows

• Number of columns

• Data types

• Missing values

• Duplicate records

• Unique values

• Distributions

• Outliers

Never treat a dataset as a black box.

📌 4. Don't Assume Correlation Means Causation

If two variables are correlated, it doesn't automatically mean one causes the other.

For example: Ice cream sales and swimming activity may both increase during summer. The relationship doesn't mean ice cream causes people to swim.

📌 5. Check the Distribution of Your Data

Understand how your variables are distributed. Look for:

• Normal distribution

• Skewness

• Heavy tails

• Outliers

• Zero-inflated data

Distribution can influence preprocessing, statistical tests, and model selection.

📌 6. Don't Automatically Remove Outliers

An outlier isn't necessarily an error. It could represent:

• A data-entry mistake

• A rare event

• A legitimate extreme value

• An important business case

Investigate first. Remove only when justified.

📌 7. Be Careful With Missing Values

Don't automatically replace every missing value with the mean. First understand: Why is the data missing?

The missingness itself can sometimes contain useful information.

📌 8. Separate Training and Testing Data Properly

Never allow your test data to influence model training or preprocessing decisions. The test set should represent unseen data.

This gives you a more realistic estimate of how the model will perform.

📌 9. Watch Out for Data Leakage

Always ask: Could this information actually be available when the prediction is made?

If not, using it can create data leakage and produce misleadingly high performance.

📌 10. Build a Simple Baseline First

Before creating a complex model, establish a simple baseline.

Baseline → Simple Model → Advanced Model

Then compare whether the additional complexity actually provides meaningful improvement.

📌 11. Don't Optimize Only for Accuracy

A model with higher accuracy isn't necessarily better. Depending on the problem, you may care more about: Precision, Recall, F1-score, ROC-AUC, MAE, RMSE, Business cost

Choose the metric based on the actual objective.

📌 12. Understand the Trade-Off Between Precision and Recall

Increasing precision can sometimes reduce recall, and vice versa.

Ask: Is a false positive more expensive, or is a false negative more expensive?

The answer can determine which metric and classification threshold you prioritize.