Understanding the Data Science Workflow

The Data Science Lifecycle

Real data science follows a structured process from question to insight. Here are the key stages.

1. Define the Problem

Start with a clear business question: "Why are customers churning?"

2. Collect Data

Gather from databases, APIs, files, or web scraping.

3. Clean Data (80% of the work)

df.dropna()                    # remove missing
df.drop_duplicates()           # remove duplicates
df["age"].fillna(df["age"].mean())  # fill gaps

4. Explore (EDA)

df.describe()       # statistics
df.corr()           # correlations
# plot distributions, find patterns

5. Model

model.fit(X_train, y_train)
predictions = model.predict(X_test)

6. Evaluate & Communicate

Measure accuracy, then present findings with clear visualizations to stakeholders.

FAQs

Which step takes longest?

Data cleaning — often 70-80% of the time. More in our Data Science section.

What is EDA?

Exploratory Data Analysis — investigating data to find patterns before modeling.