Data Science Career Options for Students in Gurugram

Score 1-5:
I enjoy explaining numbers to people          __  -> analyst / BI
I enjoy apps and how users behave              __  -> product analyst
I enjoy statistics and experiments             __  -> data scientist
I enjoy coding and building systems            __  -> ML engineer
Your highest scores suggest where to start.

Every data role needs:
- SQL: SELECT, JOIN, GROUP BY, window functions
- Excel: formulas, pivots, charts
- Python: pandas basics
- Statistics: averages, spread, distributions
- Communication: clear charts and plain-English insights

-- Running total of revenue by month (common interview SQL)
SELECT month,
       revenue,
       SUM(revenue) OVER (ORDER BY month) AS running_total,
       revenue - LAG(revenue) OVER (ORDER BY month) AS change_vs_prev
FROM monthly_revenue
ORDER BY month;

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import roc_auc_score

df = pd.read_csv("telecom_churn.csv")
X = pd.get_dummies(df.drop(columns=["customer_id", "churn"]), drop_first=True)
y = df["churn"]
X_tr, X_te, y_tr, y_te = train_test_split(X, y, test_size=0.2, random_state=1, stratify=y)
model = RandomForestClassifier(n_estimators=300, random_state=1).fit(X_tr, y_tr)
print("ROC AUC:", roc_auc_score(y_te, model.predict_proba(X_te)[:, 1]))

# Telecom Churn Analysis and Prediction
Question: Which customers are likely to leave, and why?
Data: public telecom churn dataset
Steps: cleaning, EDA, features, random forest, evaluation
Results: ROC AUC ___ on held-out data
Top drivers: contract type, tenure, support calls
Recommendation: retention offers for month-to-month users
Limits: data from one period; check before real use

Hi <Name>,
I'm a <year> student in Gurugram learning data science.
I built <project> (link) using SQL and Python, which
<one-line result>. I'd love to contribute as a data
intern on your team. Thank you for your time!
<Your name>

Year 1: Excel, basic statistics, Python basics
Year 2: SQL in depth, pandas, first analysis project
Year 3: Choose a path; 2 domain projects; internship
Year 4: Advanced path skills, final project, placements
Every year: one competition or hackathon

Mon/Wed: SQL practice (45 min)
Tue/Thu: Python + statistics (45 min)
Fri:     Read one data case study (30 min)
Sat:     Project work (3 hrs)
Sun:     Write-up and plan next week (45 min)

Skill                      Learned   Used in project
SQL joins + windows        [ ]       [ ]
pandas cleaning            [ ]       [ ]
Dashboards (Power BI)      [ ]       [ ]
Statistics basics          [ ]       [ ]
scikit-learn modelling     [ ]       [ ]
Presenting insights        [ ]       [ ]