Day |
Focus Area |
Tasks |
1 | Introduction to Data Science Concepts | Research data scientist role, set long-term goals |
2-4 | Programming Basics | Refresh programming skills (choose Python or R) |
5-7 | Data Manipulation with Pandas | Learn Pandas for data manipulation |
8-10 | Data Visualization | Learn Matplotlib or Seaborn for data visualization |
11 | Statistical Fundamentals | Review basic statistics and probability concepts |
12-14 | Machine Learning Basics | Introduction to scikit-learn for machine learning |
15-17 | Advanced Machine Learning | Learn advanced ML: ensemble methods, model evaluation |
18-20 | Basics of Deep Learning | Introduction to deep learning, choose TensorFlow or PyTorch |
21-23 | Big Data Tools and Technologies | Basics of Apache Spark for big data processing |
24 | Cloud Computing for Data Science | Introduction to cloud platforms (e.g., AWS, Azure) |
25-27 | SQL and Database Management | Learn SQL for data retrieval and management |
28-30 | Feature Engineering and Dimensionality Reduction | Techniques for improving model performance |
31-33 | Model Deployment and Production | Deploy models using platforms like Flask or FastAPI |
34-36 | Time Series Analysis | Understand and analyze time-dependent data |
37-39 | Natural Language Processing (NLP) | Introduction to processing and analyzing text data |
40-42 | Reinforcement Learning | Basics of reinforcement learning |
43-45 | Model Interpretability and Explainability | Techniques for understanding and explaining models |
46 | A/B Testing | Introduction to experimental design and analysis |
47-49 | Data Ethics and Bias in AI | Understand ethical considerations in data science |
50-52 | Real-world Data Science Projects | Work on larger, more complex projects |
53-55 | Review and Optimization | Review concepts, optimize code, and workflows |
56-58 | Job Search and Interview Preparation | Polish resume, create a portfolio, practice interviews |
59-60 | Continuous Learning and Future Goals | Plan for ongoing learning and set future goals |
Day |
Focus Area |
Tasks |
1 | Introduction to Data Science Concepts | Research data scientist role, set long-term goals |
2-4 | Programming Basics | Refresh programming skills (choose Python or R) |
5-7 | Data Manipulation with Pandas | Learn Pandas for data manipulation |
8-10 | Data Visualization | Learn Matplotlib or Seaborn for data visualization |
11 | Statistical Fundamentals | Review basic statistics and probability concepts |
12-14 | Machine Learning Basics | Introduction to scikit-learn for machine learning |
15-17 | Advanced Machine Learning | Learn advanced ML: ensemble methods, model evaluation |
18-20 | Basics of Deep Learning | Introduction to deep learning, choose TensorFlow or PyTorch |
21-23 | Big Data Tools and Technologies | Basics of Apache Spark for big data processing |
24 | Cloud Computing for Data Science | Introduction to cloud platforms (e.g., AWS, Azure) |
25-27 | SQL and Database Management | Learn SQL for data retrieval and management |
28-30 | Feature Engineering and Dimensionality Reduction | Techniques for improving model performance |
31-33 | Model Deployment and Production | Deploy models using platforms like Flask or FastAPI |
34-36 | Time Series Analysis | Understand and analyze time-dependent data |
37-39 | Natural Language Processing (NLP) | Introduction to processing and analyzing text data |
40-42 | Reinforcement Learning | Basics of reinforcement learning |
43-45 | Model Interpretability and Explainability | Techniques for understanding and explaining models |
46 | A/B Testing | Introduction to experimental design and analysis |
47-49 | Data Ethics and Bias in AI | Understand ethical considerations in data science |
50-52 | Real-world Data Science Projects | Work on larger, more complex projects |
53-55 | Review and Optimization | Review concepts, optimize code, and workflows |
56-58 | Job Search and Interview Preparation | Polish resume, create a portfolio, practice interviews |
59-61 | Continuous Learning and Specialization | Deepen skills in a specific area (e.g., computer vision, NLP) |
62-64 | Advanced Topics and Research Papers | Explore research papers in specialized areas |
65-67 | Advanced Statistical Modeling | Dive deeper into statistical models and methods |
68-70 | Advanced Machine Learning Techniques | Explore advanced ML algorithms and methods |
71-73 | Advanced Deep Learning | Dive deeper into neural networks and architectures |
74-76 | Advanced Big Data Tools and Technologies | Explore advanced big data tools and frameworks |
77-79 | Specialized Topics (e.g., Bayesian Statistics) | Explore specific areas of interest in-depth |
80-82 | Capstone Project | Work on a comprehensive data science project |
83-85 | Portfolio Development and Refinement | Enhance your data science portfolio |
86-88 | Networking and Conferences | Attend conferences, network with professionals |
89-90 | Reflection, Goal Setting, and Future Planning | Reflect on achievements, set new goals for the future |