Data Science Roadmap, Cheat Sheet aur Interview Questions

Lesson 26 of 26

Badhai ho! 🎉 Aapne Data Science ki poori buniyaad cover kar li hai — Python, NumPy, Pandas, cleaning, EDA, visualization, statistics aur Machine Learning. Is aakhri lesson me dekhte hain ki aage kya kariye.

Roadmap: aage kya seekhein

Stage

Kya seekhein

Kyun

1. Mazboot buniyaad

SQL (joins, window functions), Pandas practice, statistics

Har Data Science job me SQL aur Pandas roz lagta hai

2. ML gehraai se

Gradient Boosting (XGBoost, LightGBM), feature engineering, imbalanced data

Tabular data pe industry me sabse zyada use

3. Projects

3-4 end-to-end projects, GitHub pe

Portfolio hi aapka resume hai

4. Deep Learning

Neural networks, PyTorch/TensorFlow, CNN, Transformers

Images, text aur audio ke liye

5. GenAI aur LLMs

LLMs, prompt engineering, RAG, vector databases

Aaj ki sabse tez badhti field

6. Deployment

FastAPI/Flask, Streamlit, Docker, cloud basics

Model ko kaam ka banana

Is site pe aage padhne ke liye: SQL Tutorial, Deep Learning & Neural Networks, What is LLM, What is RAG aur What is Vector Database.

Pandas cheat sheet

Kaam

Code

CSV padhna

pd.read_csv("file.csv")

Overview

df.head(), df.info(), df.describe()

Missing values

df.isnull().sum(), df.fillna(), df.dropna()

Filter

df[df["col"] > 10], df.query("...")

Select

df.loc[rows, cols], df.iloc[i, j]

Group

df.groupby("col").agg(...)

Join

df.merge(other, on="key", how="left")

Pivot

df.pivot_table(values, index, columns, aggfunc)

Sort

df.sort_values("col", ascending=False)

Duplicates

df.drop_duplicates()

scikit-learn cheat sheet

Kaam

Code

Split

train_test_split(X, y, test_size=0.2, stratify=y)

Scale

StandardScaler(), MinMaxScaler()

Encode

OneHotEncoder(handle_unknown="ignore")

Missing

SimpleImputer(strategy="median")

Regression

LinearRegression(), RandomForestRegressor()

Classification

LogisticRegression(), RandomForestClassifier()

Clustering

KMeans(n_clusters=k, n_init=10)

Pipeline

Pipeline([("prep", ...), ("model", ...)])

Evaluate

classification_report(), r2_score(), roc_auc_score()

Tune

GridSearchCV(model, param_grid, cv=5)

Portfolio project ideas

  1. House price prediction — regression, feature engineering
  2. Customer churn — classification, business insights (pichhla lesson)
  3. Customer segmentation — K-Means, marketing strategy
  4. Sales dashboard + forecasting — Pandas, time series, Power BI/Streamlit
  5. Movie review sentiment — text data, NLP basics
  6. Resume ya PDF Q&A bot — LLM + RAG

Har project me: problem statement, EDA ke charts, model comparison, final result aur business insights — ek saaf README ke saath GitHub pe daaliye.

Top interview questions

1. Supervised aur unsupervised learning me kya farq hai?

Supervised me data ke saath sahi jawab (label) hota hai aur model use predict karna seekhta hai (regression, classification). Unsupervised me label nahi hota, model khud patterns/groups dhoondhta hai (clustering).

2. Overfitting kya hai aur kaise rokein?

Jab model training data ka ratta maar le aur naye data pe fail ho. Rokne ke tareeke: zyada data, simple model, regularization (L1/L2), cross-validation, tree ki depth limit karna, feature kam karna.

3. Bias-variance tradeoff kya hai?

High bias = model bahut simple, underfit. High variance = model bahut complex, overfit. Accha model dono ke beech balance dhoondhta hai.

4. Precision aur recall me kya farq hai? Kab kaunsa?

Precision: jinhe positive bola unme kitne sahi. Recall: saare positives me se kitne pakde. Cancer/fraud detection me recall, spam filter me precision zyada important hai.

5. Missing values kaise handle karte hain?

Pehle samjho kyun missing hain. Phir: kam ho to drop, numbers me median/mean, categories me mode, time series me ffill/interpolate, ya model-based imputation. Missing hone ka flag column bhi bana sakte hain.

6. Mean aur median me kab kya use karein?

Symmetric data me mean. Skewed data ya outliers ho (salary, property price) to median, kyunki us pe outliers ka asar nahi padta.

7. p-value kya hai?

Agar null hypothesis sach hota, to itna ya isse extreme result sirf chance se aane ki probability. p < 0.05 ho to aam taur pe null hypothesis reject karte hain.

8. Data leakage kya hai?

Jab training me aisi information aa jaaye jo prediction ke waqt asal me available nahi hogi — jaise poore data pe scaler fit karna, ya target se bana feature. Isse score jhootha accha aata hai. Pipeline aur sahi split se bachte hain.

9. Random Forest ek Decision Tree se behtar kyun hai?

Random Forest kai trees banata hai, har ek alag random data aur features pe, aur unki voting leta hai. Isse variance aur overfitting kam hoti hai.

10. Imbalanced dataset ke saath kya karein?

Accuracy ki jagah precision, recall, F1, ROC-AUC dekho. class_weight="balanced", oversampling (SMOTE) ya undersampling, aur threshold tuning try karo. Split me stratify use karo.

11. Correlation aur causation me farq?

Correlation sirf batata hai ki do cheezein saath badalti hain. Causation matlab ek doosre ki wajah hai. Causation sabit karne ke liye controlled experiment (A/B test) chahiye.

12. Feature scaling kab zaroori hai?

Distance ya gradient pe chalne wale models me — KNN, K-Means, SVM, Logistic/Linear Regression (regularization ke saath), Neural Networks. Tree-based models (Decision Tree, Random Forest) ko zaroorat nahi.

Aakhri baat

Data Science ek din me nahi aata — roz thoda practice kariye. Ek dataset uthaiye, sawaal poochiye, aur code se jawab dhoondhiye. Is course ke kisi bhi lesson me doubt ho to comment me poochiye. All the best! 🚀

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