Data Science Course Syllabus Full Curriculum Breakdown

from scipy import stats
import numpy as np

# Did a new page design change average time on page?
old = np.array([52, 48, 55, 50, 47, 53, 49, 51])
new = np.array([58, 54, 60, 57, 55, 59, 56, 61])

t_stat, p_value = stats.ttest_ind(new, old, equal_var=False)
print(f"t = {t_stat:.2f}, p = {p_value:.4f}")

if p_value < 0.05:
    print("Difference is statistically significant at 5%")
else:
    print("No significant difference found")

-- Rank customers by spend within each city
SELECT
  city,
  customer_id,
  SUM(amount) AS total_spend,
  RANK() OVER (PARTITION BY city ORDER BY SUM(amount) DESC) AS city_rank
FROM orders
GROUP BY city, customer_id
ORDER BY city, city_rank;

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report

df = pd.read_csv("churn.csv")          # your course dataset
X = df.drop(columns=["churned"])
y = df["churned"]

num = X.select_dtypes("number").columns
cat = X.select_dtypes("object").columns
pre = ColumnTransformer([
    ("num", StandardScaler(), num),
    ("cat", OneHotEncoder(handle_unknown="ignore"), cat),
])
model = Pipeline([("pre", pre), ("rf", RandomForestClassifier(random_state=0))])

X_tr, X_te, y_tr, y_te = train_test_split(X, y, test_size=0.2, stratify=y, random_state=0)
model.fit(X_tr, y_tr)
print(classification_report(y_te, model.predict(X_te)))

from sklearn.model_selection import GridSearchCV

# 'model', X_tr and y_tr come from the pipeline tab
params = {
    "rf__n_estimators": [100, 300],
    "rf__max_depth": [None, 8, 16],
    "rf__min_samples_leaf": [1, 5],
}
search = GridSearchCV(model, params, cv=5, scoring="f1", n_jobs=-1)
search.fit(X_tr, y_tr)

print("best params:", search.best_params_)
print("best CV f1:", round(search.best_score_, 3))

import joblib

# after training the pipeline from the ML module
joblib.dump(model, "churn_model.joblib")

# later, or in another script
loaded = joblib.load("churn_model.joblib")
print(loaded.predict(X_te.head(3)))

# run with: uvicorn serve_model:app --reload
import joblib
import pandas as pd
from fastapi import FastAPI

app = FastAPI()
model = joblib.load("churn_model.joblib")

@app.post("/predict")
def predict(customer: dict):
    row = pd.DataFrame([customer])
    prob = model.predict_proba(row)[0, 1]
    return {"churn_probability": round(float(prob), 3)}

Does the syllabus include...                  Yes / No
Python programming (not just notebooks)       ___
SQL with joins and window functions           ___
Statistics and hypothesis testing             ___
EDA and visual storytelling                   ___
Regression and classification                 ___
Unsupervised learning                         ___
Cross-validation and tuning                   ___
Deep learning or NLP introduction             ___
Deployment of at least one model              ___
Capstone on real data with review             ___

Ask the course team:

1. How many hours are hands-on coding vs lectures?
2. How many projects do students build, and with what data?
3. Is code reviewed by a mentor?
4. Are assignments graded with feedback?
5. Can I see a sample student project or repo?
6. Is there interview preparation for each module?
7. What happens if I miss classes - recordings, repeats?

                    | Course A | Course B | Course C
--------------------|----------|----------|---------
Modules covered /10 |          |          |
Hands-on share      |          |          |
Projects + review   |          |          |
Mentor access       |          |          |
Batch flexibility   |          |          |
Fees (ask directly) |          |          |
Placement support   |          |          |
My final pick:      ______________________