Data Science Roadmap 2026: Beginner Learning Path

Understand the role of the learning path in the data-science workflow

A realistic six-month plan might spend the first month on Python and SQL, the second on statistics and EDA, months three and four on machine learning, month five on two end-to-end projects, and month six on interview practice plus a specialization.

• A sensible learning path moves from Python and SQL into statistics, exploratory analysis, machine learning and finally portfolio projects.
• Do not wait to finish every theory topic before building; alternate short study blocks with small projects so concepts become usable skills.
• SQL and data cleaning are often more important in entry-level work than advanced neural networks because most real datasets are messy and relational.
• A portfolio is stronger when each project explains the question, data quality, baseline, metric, error analysis and final limitation.

Connect the idea to a concrete decision

For the learning path, predict one visible result before each snippet runs—a row count, shape, statistic, ranking or error pattern—then compare it with the printed checkpoint.

# the learning path · example 1
skills = ['Python','SQL','Statistics','EDA','Machine Learning','Projects']
for week, skill in enumerate(skills, start=1):
    print(f'Weeks {2*week-1}-{2*week}: {skill}')

the learning path checkpoint 1: Run the cell and inspect the printed values before changing anything.

# the learning path · example 2
import pandas as pd

df = pd.read_csv('practice.csv')
print(df.shape)
print(df.dtypes)
print(df.head(3).to_string(index=False))

the learning path checkpoint 2: (30, 5)
Column types are printed, followed by the first three rows.

# the learning path · example 3
missing = df.isna().sum().sort_values(ascending=False)
print(missing[missing.gt(0)])

the learning path checkpoint 3: Only columns with at least one missing value are listed.

# the learning path · example 4
summary = df.select_dtypes('number').agg(['count','mean','median','min','max']).T
print(summary.round(2))

the learning path checkpoint 4: A compact numeric summary appears with one row per numeric column.

# the learning path · example 5
test = df.copy()
if len(test):
    test.iloc[0, 0] = test.iloc[0, 0]
print('rows:', len(test), 'duplicates:', test.duplicated().sum())

the learning path checkpoint 5: The row count and duplicate count make the check visible instead of implicit.

Use a small reproducible example

Self-check with answers

Check your understanding with a changed input