Which format fits me?
☐ I finish online courses on my own                 → online formats can work
☐ I learn faster asking questions in person          → classroom or hybrid
☐ I need weekend or evening timings                  → check batch lists of both
☐ I want peers to study with                         → classroom or live cohorts
Write your answers before speaking to either provider.

Ask both providers, in writing:
1. Current data science syllabus with hours per module
2. Live vs recorded split
3. Number of projects and how they are reviewed
4. Mentoring and doubt support details
5. Career support included
6. Total fee including tax, refund policy
7. Batch timings and what happens if I miss sessions

# A job-ready course teaches you to evaluate models properly
from sklearn.datasets import load_breast_cancer
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

X, y = load_breast_cancer(return_X_y=True)
model = make_pipeline(StandardScaler(), LogisticRegression(max_iter=1000))
scores = cross_val_score(model, X, y, cv=5, scoring="recall")
print(scores.round(3), "mean recall:", scores.mean().round(3))

Data science syllabus checklist
☐ Python, NumPy, pandas
☐ Data visualisation
☐ SQL for data extraction
☐ Probability and statistics
☐ Supervised learning: regression, classification
☐ Unsupervised learning: clustering
☐ Model evaluation and cross-validation
☐ Feature engineering
☐ A deep learning introduction
☐ Capstone with presentation

A capstone you can be proud of
- Real or realistic public dataset with messy parts
- Clear business question
- EDA with 3-5 meaningful charts
- Baseline model + improved model
- Cross-validated metrics suited to the problem
- Error analysis: where the model fails
- 5-minute presentation to non-technical listeners
- Clean GitHub repo with README

Support comparison (fill from written answers)
                       Uncodemy     Edureka
Project review          ______       ______
Doubt support           ______       ______
Mock interviews         ______       ______
Resume/portfolio help   ______       ______
Batch repeat option     ______       ______

Weighted Scorecard (score 1-5 from evidence)
Criterion             Weight   Uncodemy   Edureka
Format fits me          3        ___        ___
Syllabus depth          3        ___        ___
Project review          3        ___        ___
Doubt support           2        ___        ___
Mock interviews         2        ___        ___
Schedule fit            2        ___        ___
Total cost clarity      1        ___        ___
Set weights before scoring.

Demo / Preview Notes
Provider: ______  Date: ______
Topic: ______________
Was statistics linked to ML? Y / N
Was evaluation explained beyond accuracy? Y / N
Did the trainer answer my question clearly? Y / N
Confidence after session (1-5): ___

Final Choice
Chosen: ______
Weighted total: ___ vs ___
Reasons:
1.
2.
First 2 weeks: Python + pandas practice daily
Commitment: ____ hours per week