Data Science 90-Day Study Roadmap
Week 1-2: Foundations of Data Science
- Days 1-3: Introduction to Data Science and Its Applications
- Days 4-6: Setting Up Your Data Science Environment (Python, Jupyter Notebooks)
- Days 7-14: Python Programming Fundamentals and Best Practices
Week 3-4: Data Manipulation and Analysis
- Days 15-21: Advanced Pandas Techniques for Data Manipulation
- Days 22-28: Advanced NumPy Concepts and Techniques
- Day 29-30: Real-world Data Manipulation Projects
Week 5-6: Data Visualization Mastery
- Days 31-38: Advanced Matplotlib for Data Visualization
- Days 39-46: Mastering Seaborn and Plotly for Interactive Visualizations
- Days 47-48: Real-world Data Visualization Projects
Week 7-8: Statistics and Exploratory Data Analysis (EDA)
- Days 49-56: Statistical Concepts for Data Science
- Days 57-64: Exploratory Data Analysis (EDA) Techniques
- Days 65-66: Real-world EDA Projects
Week 9-10: Machine Learning Fundamentals
- Days 67-74: Understanding Machine Learning Algorithms
- Days 75-82: Introduction to scikit-learn and Model Evaluation
- Days 83-84: Real-world Machine Learning Projects
Week 11-12: Deep Learning Basics
- Days 85-90: Introduction to Neural Networks and Deep Learning
- Days 91-94: TensorFlow and Keras for Deep Learning
- Days 95-96: Real-world Deep Learning Projects
Week 13-14: Advanced Machine Learning and Model Deployment
- Days 97-104: Advanced Machine Learning Concepts (Ensemble Methods, Hyperparameter Tuning)
- Days 105-112: Model Deployment Basics (Flask, Docker)
- Days 113-114: Real-world Advanced ML Projects
Week 15-16: Big Data and Distributed Computing
- Days 115-120: Introduction to Big Data (Hadoop, Spark)
- Days 121-128: Distributed Computing with Apache Spark
- Days 129-130: Real-world Big Data Projects
Week 17-18: Natural Language Processing (NLP) and Text Mining
- Days 131-138: Basics of NLP and Text Preprocessing
- Days 139-146: Advanced NLP Techniques (Word Embeddings, Transformers)
- Days 147-148: Real-world NLP Projects
Week 19-20: Time Series Analysis and Forecasting
- Days 149-156: Time Series Analysis Fundamentals
- Days 157-164: Time Series Forecasting Models
- Days 165-166: Real-world Time Series Projects
Week 21-22: Feature Engineering and Model Interpretability
- Days 167-174: Advanced Feature Engineering Techniques
- Days 175-182: Interpretable Machine Learning
- Days 183-184: Real-world Feature Engineering Projects
Week 23-24: Advanced Data Science Tools and Frameworks
- Days 185-192: Advanced Git and Version Control
- Days 193-200: Containerization with Docker and Orchestration with Kubernetes
- Days 201-202: Real-world Projects using Advanced Tools
Week 25-26: Data Science Ethics and Bias
- Days 203-210: Ethics in Data Science
- Days 211-218: Addressing Bias in Machine Learning
- Days 219-220: Real-world Projects with Ethical Considerations
Week 27-28: Cloud Computing for Data Science
- Days 221-228: Introduction to Cloud Platforms (AWS, Google Cloud, Azure)
- Days 229-236: Cloud-based Data Science and Machine Learning
- Days 237-238: Real-world Data Science Projects on the Cloud
Week 29-30: Capstone Project
- Days 239-250: Designing and Implementing a Comprehensive Capstone Project
- Days 251-260: Iterative Development and Improvement of the Capstone Project
- Days 261-270: Finalizing and Presenting the Capstone Project
Remember to regularly practice coding, work on real-world projects, and participate in online communities. Adjust the pace based on your understanding and comfort level with each topic. Continuously seek new challenges and stay updated with the latest developments in the field.
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