How to Write a Data Scientist Resume With Zero Experience

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.dummy import DummyClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, roc_auc_score

df = pd.read_csv("churn.csv")
X = pd.get_dummies(df.drop(columns=["churned"]), drop_first=True)
y = df["churned"]
X_tr, X_te, y_tr, y_te = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42)

base = DummyClassifier(strategy="most_frequent").fit(X_tr, y_tr)
rf = RandomForestClassifier(n_estimators=300, random_state=42).fit(X_tr, y_tr)

print("Baseline AUC:", roc_auc_score(y_te, base.predict_proba(X_te)[:, 1]))
print("Model AUC   :", roc_auc_score(y_te, rf.predict_proba(X_te)[:, 1]))
print(classification_report(y_te, rf.predict(X_te)))

from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler

pipe = make_pipeline(StandardScaler(), LogisticRegression(max_iter=1000))
scores = cross_val_score(pipe, X, y, cv=5, scoring="roc_auc")
print("ROC-AUC per fold:", scores.round(3))
print("Mean:", scores.mean().round(3), "+/-", scores.std().round(3))

SKILLS
Statistics : descriptive stats, hypothesis tests, A/B testing,
             confidence intervals
ML         : linear/logistic regression, random forest,
             gradient boosting, k-means, cross-validation
Python     : pandas, NumPy, scikit-learn, matplotlib
Data       : SQL (joins, window functions), data cleaning
Tools      : Git, Jupyter, Streamlit (basics)

from scipy import stats

# conversions out of visitors for two page versions
conv_a, n_a = 210, 4000
conv_b, n_b = 255, 4000

table = [[conv_a, n_a - conv_a], [conv_b, n_b - conv_b]]
chi2, p, dof, expected = stats.chi2_contingency(table)
print(f"A: {conv_a/n_a:.2%}  B: {conv_b/n_b:.2%}  p-value: {p:.4f}")

Fresher data scientist with projects in churn
prediction and demand forecasting using Python,
SQL and scikit-learn. Focused on clear baselines,
honest evaluation and simple explanations.

Finance professional moving into data science.
Built credit-risk and expense-forecasting projects
in Python and scikit-learn, combining domain
knowledge with statistical modelling.

YOUR NAME
Data Scientist (Fresher) | City | phone | email
linkedin.com/in/you | github.com/you

SUMMARY
Fresher data scientist with projects in classification
and forecasting; strong in statistics, Python and SQL.

PROJECTS
Subscriber Churn Prediction | Python, scikit-learn
- Framed churn as a classification problem and built a
  majority-class baseline before modelling
- Compared logistic regression and random forest with
  5-fold cross-validation using ROC-AUC
- Explained top drivers with feature importance and
  listed limitations of the public dataset
github.com/you/churn-prediction

Weekly Demand Forecast | pandas, statsmodels
- Built seasonal naive and Holt-Winters forecasts and
  compared MAE on the last 8 weeks

SKILLS
Statistics · Python (pandas, NumPy, scikit-learn) · SQL
Model evaluation · Data visualisation · Git

EDUCATION
Degree — College — Year
Relevant coursework: Statistics, Linear Algebra

CERTIFICATIONS (optional, completed only)