What Skills Do You Need to Become a Data Scientist

import numpy as np
from scipy import stats

group_a = np.array([12.1, 11.8, 12.5, 13.0, 12.2, 11.9])
group_b = np.array([12.9, 13.4, 13.1, 12.8, 13.6, 13.2])

t_stat, p_value = stats.ttest_ind(group_a, group_b)
print(f"t = {t_stat:.2f}, p = {p_value:.4f}")
if p_value < 0.05:
    print("Difference is statistically significant at 5%")

-- Rank customers by spend inside each city
SELECT customer_id,
       city,
       total_spend,
       RANK() OVER (PARTITION BY city ORDER BY total_spend DESC) AS city_rank
FROM customer_summary;

from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report

X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42, stratify=y)
model = LogisticRegression(max_iter=5000)
model.fit(X_train, y_train)
print(classification_report(y_test, model.predict(X_test)))

from sklearn.model_selection import cross_val_score
from sklearn.ensemble import RandomForestClassifier

rf = RandomForestClassifier(n_estimators=200, random_state=42)
scores = cross_val_score(rf, X, y, cv=5, scoring="f1")
print("F1 per fold:", scores.round(3))
print("Mean F1:", scores.mean().round(3))

Problem brief (fill before writing code)
Business question: Which customers are likely to stop buying in 90 days?
Decision it supports: Who gets a retention offer
Success measure: More customers retained than offers cost
Data available: Orders, support tickets, app logins
Deadline and audience: 3 weeks, marketing head

Result summary (one page)
What we found: Customers with no login for 30 days and a recent
               complaint are the most likely to leave.
What we recommend: Call this group first.
How confident we are: Tested on data the model never saw.
Limits: New customers have too little history to score well.

Skill self-rating (0 = none, 3 = job-ready)
Statistics & probability ........ _
Python (Pandas, NumPy) .......... _
SQL (joins, windows) ............ _
Machine learning & evaluation ... _
Data visualisation .............. _
Problem framing ................. _
Communication ................... _
Lowest two scores = next month's focus

Gap plan
Skill gap: ______________________
Resource I will use: ______________________
Weekly hours: ____
Small project to prove it: ______________________
Review date: ________

Data science portfolio checklist
[ ] One exploratory analysis with clear charts
[ ] One SQL-heavy business question answered
[ ] One supervised model with proper evaluation
[ ] One project explained for a non-technical reader
[ ] Each project has a README: problem, data, method, result