Python programming for Data Science

PYTHON ROADMAP FOR DATA SCIENCE

BEGINNER LEVEL (Python + Math Foundations)

1. Python Basics (Data-Oriented)

What to learn

• Python syntax & variables
• Data types (int, float, str, bool)
• Input/Output
• Control flow (if, for, while)
• Functions

Data Science Focus

• Writing clean functions for calculations
• Understanding numerical operations

Practice Projects

• Simple statistics calculator (mean, median, variance)
• CSV file reader

 

2. Core Data Structures

What to Learn

• Lists, tuples, sets
• Dictionaries
• Indexing & Slicing

Data Science Focus

• Storing tabular data
• Mapping features to values

Practice Projects

• Student score analysis
• Word frequency counter

 

 

 

 

3. File Handling & Data Formats

What to Learn

• Reading/Writing text files
• CSV, JSON, Excel
• with statement

Libraries

• csv, json, openpyxl

Practice Projects

• Load and clean CSV dataset
• Convert JSON à CSV

 

4. Math & Statistics Basics

Math Topics

• Mean, median, mode
• Variance & standard deviation
• Probability basics
• Correlation
• Normal distribution

Libraries

• math
• statistics

Practice

• Implement statistical formulas manually
• Analyze a dataset’s distribution.

 

 

 

 

INTERMEDIATE LEVEL (Core Data Science Stack)

5. Numpy (Numerical Computing)

What to Learn

• Arrays vs lists
• Vectorized operations
• Broadcasting
• Indexing & slicing
• Linear algebra basics

Practice Projects

• Matrix operations
• Simulation experiments (dice, coin toss)

 

6. Pandas (Data Analysis Backbone)

What to Learn

• Series & Data Frames
• Data loading (read_csv, read_excel)
• Data cleaning
  • Missing values
  • Duplicates
  • Type conversions
• Filtering & grouping
• Aggregation

 

Practice Projects

• Sales data analysis
• COVID / stock dataset analysis

 

7. Data Visualization

Libraries

• Matplotlib
• Seaborn

What to Learn

• Line, bar, histogram, box plots
• Heatmaps
• Pair plots
• Visualization best practices

 

Practice Projects

• Exploratory Data Analysis (EDA) report
• Visual storytelling project

 

8. Exploratory Data Analysis (EDA)

What to Learn

• Summary statistics
• Feature distributions
• Outlier detection
• Correlation analysis
• Hypothesis formulation

 

Practice Projects

• Complete EDA on Kaggle dataset
• Business insights report

 

ADVANCED LEVEL (Machine Learning & Modeling)

9. Scikit-learn (Machine Learning Core)

What to learn

• Train-test split
• Supervised learning
  • Linear regression
  • Logistic regression
  • KNN
  • Decision trees
• Unsupervised learning
  • K-means
  • Hierarchical clustering
• Model evaluation
  • Accuracy, precision, recall, F1
  • ROC-AUC

 

Practice Projects

• House price prediction
• Customer churn prediction

 

10. Feature Engineering

What to learn

• Encoding categorical variables
• Feature scaling
• Feature selection
• Handling imbalanced data

Practice Projects

• Improve ML model accuracy
• Kaggle competitions

11. Advanced Machine Learning

What to learn

• Ensemble methods
  • Random Forest
  • Gradient Boosting
  • XGBoost / LightGBM
• Cross-validation
• Hyperparameter tuning

Practice Projects

• Fraud detection
• Credit scoring model

EXPERT LEVEL (Professional Data Scientist)

12. Statistics for Data Science

What to learn

• Hypothesis testing
• Confidence intervals
• A/B testing
• Bayesian statistics (introduction)

 

Practice Projects

• A/B test analysis
• Experiment design project

 

13. SQL for Data Science

What to learn

• SELECT, WHERE, JOIN
• GROUP BY, HAVING
• Window functions
• Subqueries

 

Practice Projects

• SQL + Python analytics project
• Business KPI dashboard backend

 

14. Time Series Analysis

What to learn

• Trend & seasonality
• ARIMA / SARIMA
• Forest evaluation

Libraries

• statsmodels
• prophet

 

Practice Projects

• Sales forecasting
• Stock price analysis

 

15. Deep Learning

What to learn

• Neural network basics
• TensorFlow / PyTorch
• CNNs / LSTMs (time series)

 

Practice Projects

• Image classification
• Forecasting model

 

16. Big Data & Deployment

What to learn

• Spark (PySpark)
• Cloud basics (AWS/GCP/Azure)
• Model deployment (FastAPI)
• ML pipelines

Practice Projects

• End-to-end ML system
• Deployed prediction API