1. Introduction to Data Science
Understand what Data Science is and career opportunities
Learning Path
- DS vs DA vs ML Engineer
- Data Science workflow
- Install setup
print("Welcome to Data Science!")
2. Mathematics Foundation
Linear Algebra, Statistics, Probability, Calculus
Key Concepts
- Vectors & Matrices
- Mean, Median, Variance
- Probability Distributions
- Derivatives
3. Python for Data Science
Master Python programming for data manipulation
Core Libraries
- Pandas (DataFrames)
- NumPy (Arrays)
- Matplotlib
- Requests (APIs)
Example
import pandas as pd
df = pd.read_csv('data.csv')
print(df.head())
4. Data Cleaning & EDA
Handle missing values, outliers, feature engineering
df.isnull().sum()
df.fillna(method='ffill')
df.drop_duplicates()
5. Data Visualization
Matplotlib, Seaborn, Plotly, Bokeh
Libraries
- Matplotlib (static)
- Seaborn (statistical)
- Plotly (interactive)
Chart Types
- Bar/Line Charts
- Heatmaps
- Box Plots
- Pair Plots
6. SQL & Databases
Query large datasets efficiently
SELECT * FROM customers WHERE age > 30
JOIN sales ON customers.id = sales.customer_id
GROUP BY region HAVING COUNT(*) > 10
7. Machine Learning
Supervised & Unsupervised Learning
Supervised
- Linear/Logistic Regression
- Decision Trees
- Random Forest
- SVM, KNN
Unsupervised
- K-Means Clustering
- PCA
- Hierarchical Clustering
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier()
model.fit(X_train, y_train)
8. Deep Learning Basics
Neural Networks, TensorFlow, Keras
from tensorflow.keras.models import Sequential
model = Sequential([
Dense(64, activation='relu'),
Dense(1, activation='sigmoid')
])
9. Real World Projects
- Titanic Survival Prediction
- House Price Regression
- Sales Forecasting
- Customer Segmentation
- Fraud Detection
- Stock Price Prediction
10. Interview Preparation
200+ common questions with solutions
Technical
- SQL queries
- ML algorithms
- System Design
Behavioral
- Project Discussion
- Experience Questions