Interview Preparation Course for Data Science

📌 3-Hour Python Crash Course for Data Science Interviews

This course is fully Python-focused, covering coding, data manipulation, statistics, and machine learning to help you ace data science interviews.

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⏳ Hour 1: Python Essentials for Data Science

🔹 Core Python (30 min)

Data Types (list, dict, tuple, set)

Loops (for, while), Conditional Statements (if-else)

Functions & Lambda Functions

List Comprehension

🔹 Data Manipulation with Pandas & NumPy (30 min)

Reading CSV & Excel (pd.read_csv(), pd.read_excel())

Handling Missing Data (dropna(), fillna())

Data Filtering & Sorting (query(), sort_values())

Aggregation & Grouping (groupby(), pivot_table())

NumPy Arrays, Indexing, Broadcasting

📝 Interview Focus: Writing efficient Python scripts, handling real-world datasets.

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⏳ Hour 2: Statistics & Probability with Python

🔹 Descriptive Statistics (15 min)

Mean, Median, Mode (np.mean(), np.median(), stats.mode())

Variance & Standard Deviation (np.var(), np.std())

Skewness & Kurtosis (scipy.stats.skew(), scipy.stats.kurtosis())

🔹 Probability & Distributions (15 min)

Normal, Binomial, Poisson Distributions (np.random.normal(), np.random.binomial())

Probability Density & Mass Functions

🔹 Hypothesis Testing (30 min)

t-tests, Chi-Square Test, ANOVA (scipy.stats.ttest_ind(), scipy.stats.chi2_contingency())

p-value & Confidence Intervals

📝 Interview Focus: Understanding statistical concepts, applying them in Python.

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⏳ Hour 3: Machine Learning with Python

🔹 Supervised Learning (30 min)

Linear & Logistic Regression (sklearn.linear_model.LinearRegression, LogisticRegression)

Decision Trees & Random Forest (sklearn.ensemble.RandomForestClassifier)

🔹 Unsupervised Learning (15 min)

Clustering (KMeans, DBSCAN)

PCA for Dimensionality Reduction

🔹 Model Evaluation & Tuning (15 min)

Train-Test Split (train_test_split)

Cross-Validation (cross_val_score)

Metrics: Accuracy, Precision, Recall, F1-score (classification_report, roc_auc_score)

📝 Interview Focus: Hands-on coding, understanding ML algorithms, optimizing models.

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💡 Final Notes:

Live coding practice after every module.

Mock interview questions with hands-on Python solutions.

Real-world dataset challenge at the end.