📊 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.
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