This roadmap is designed to take you from a beginner in data analysis to an expert in data science and a professional data engineer. It covers essential topics, tools, resources, projects, and career preparation strategies.
- Python basics: variables, loops, functions, OOP concepts
- Working with files (CSV, JSON, Excel, TXT)
- Exception handling and logging
- Python 3
- Jupyter Notebook / VS Code
- Pandas, NumPy
- Data cleaning and transformation on CSV files
- JSON data processor
- Pandas: dataframes, filtering, merging
- NumPy: array operations, broadcasting
- Data visualization using Matplotlib and Seaborn
- Exploratory data analysis (EDA) on Titanic dataset
- Customer segmentation using visualization
- Basic SQL commands: SELECT, WHERE, GROUP BY
- Joins and subqueries
- Window functions and indexing
- PostgreSQL / MySQL
- SQLite / BigQuery
- Analyzing an e-commerce sales database
- Handling missing values and outliers
- Data transformation and feature engineering
- Business insights extraction
- Housing price prediction: EDA and feature selection
- Customer churn analysis
- Descriptive vs. inferential statistics
- Probability distributions
- Hypothesis testing
- A/B testing on marketing data
- Customer segmentation using statistical models
- Regression (linear, logistic)
- Classification (decision trees, SVM)
- Clustering (K-Means, DBSCAN)
- Feature selection and model evaluation
- Scikit-Learn
- XGBoost, LightGBM
- Predicting house prices
- Customer churn prediction
- Neural networks and activation functions
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs, LSTMs)
- TensorFlow
- PyTorch
- Image classification with CNN
- Sentiment analysis using LSTMs
- SQL performance optimization
- NoSQL databases (MongoDB, Cassandra)
- Data warehousing (BigQuery, Snowflake)
- ETL pipeline for structured and unstructured data
- Batch vs. real-time data processing
- Apache Airflow for workflow automation
- Apache Kafka for real-time data streaming
- Real-time streaming pipeline with Apache Kafka
- AWS services (S3, Lambda, Glue)
- Docker and Kubernetes for containerization
- CI/CD pipelines for data workflows
- Deploying a data pipeline on AWS
- Showcase 3-5 well-documented projects on GitHub
- Write case studies or blog posts
- Contribute to open-source projects
- Participate in Kaggle competitions
- Join LinkedIn groups & Slack communities
- Engage in data hackathons & meetups
- Google Professional Data Engineer
- AWS Certified Data Analytics - Specialty
- Databricks Certified Data Engineer Associate
- SQL query optimization, business case studies
- Machine learning model evaluation, feature selection techniques
- System design for large-scale data pipelines, cloud-based infrastructure
📚 Resources:
✅ Build a full-stack project integrating data engineering, data science, and visualization ✅ Apply for internships, freelance gigs, or open-source contributions ✅ Stay updated with new technologies like MLOps, DataOps, and Serverless Data Engineering
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