02/07/2024
100-day plan to learn data science. This plan is broken down into four phases: Foundations, Intermediate Concepts, Advanced Topics, and Projects & Practice. Each phase will include daily tasks and goals.
# # # **Phase 1: Foundations (Days 1-30)**
**Goal:** Learn the basics of programming, mathematics, and data manipulation.
# # # # Days 1-10: Python Basics
- **Day 1-3:** Learn Python syntax and basic programming concepts.
- Resource: Codecademy Python course.
- **Day 4-7:** Learn about data structures (lists, dictionaries, tuples, sets).
- Resource: "Automate the Boring Stuff with Python" (Chapters 2-4).
- **Day 8-10:** Practice with small projects.
- Resource: Project ideas from Codecademy or "Automate the Boring Stuff with Python".
# # # # Days 11-15: Mathematics & Statistics Basics
- **Day 11-13:** Learn basic statistics (mean, median, mode, variance, standard deviation).
- Resource: Khan Academy Statistics and Probability.
- **Day 14-15:** Learn basic probability concepts.
- Resource: Khan Academy Probability and Statistics.
# # # # Days 16-20: Data Manipulation with Pandas
- **Day 16-18:** Learn the basics of Pandas (Series, DataFrame, basic operations).
- Resource: Kaggle’s Pandas tutorial.
- **Day 19-20:** Practice data manipulation exercises.
- Resource: Kaggle datasets.
# # # # Days 21-25: Data Visualization with Matplotlib and Seaborn
- **Day 21-23:** Learn basic plotting with Matplotlib.
- Resource: "Python Data Science Handbook" (Chapter 4).
- **Day 24-25:** Learn data visualization with Seaborn.
- Resource: Seaborn documentation and tutorials.
# # # # Days 26-30: Introduction to Machine Learning
- **Day 26-28:** Learn the basics of machine learning concepts (supervised vs. unsupervised learning).
- Resource: Coursera Machine Learning by Andrew Ng (Week 1).
- **Day 29-30:** Learn about regression and classification algorithms.
- Resource: Coursera Machine Learning by Andrew Ng (Week 2).
# # # **Phase 2: Intermediate Concepts (Days 31-60)**
**Goal:** Dive deeper into machine learning algorithms and techniques.
# # # # Days 31-35: Linear Regression and Logistic Regression
- **Day 31-33:** Learn about linear regression.
- Resource: Coursera Machine Learning by Andrew Ng (Week 2-3).
- **Day 34-35:** Learn about logistic regression.
- Resource: Coursera Machine Learning by Andrew Ng (Week 3-4).
# # # # Days 36-40: Decision Trees and Ensemble Methods
- **Day 36-38:** Learn about decision trees.
- Resource: "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" (Chapter 6).
- **Day 39-40:** Learn about ensemble methods (random forests, gradient boosting).
- Resource: "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" (Chapter 7).
# # # # Days 41-45: Clustering and Dimensionality Reduction
- **Day 41-43:** Learn about K-means clustering.
- Resource: "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" (Chapter 9).
- **Day 44-45:** Learn about dimensionality reduction techniques (PCA, t-SNE).
- Resource: "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" (Chapter 😎.
# # # # Days 46-50: Model Evaluation and Hyperparameter Tuning
- **Day 46-48:** Learn about model evaluation metrics (accuracy, precision, recall, F1 score).
- Resource: Coursera Machine Learning by Andrew Ng (Week 5).
- **Day 49-50:** Learn about hyperparameter tuning (grid search, random search).
- Resource: "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" (Chapter 6).
# # # # Days 51-55: Introduction to Neural Networks
- **Day 51-53:** Learn the basics of neural networks.
- Resource: Coursera Deep Learning Specialization by Andrew Ng (Course 1, Week 1-2).
- **Day 54-55:** Implement a simple neural network with TensorFlow/Keras.
- Resource: TensorFlow documentation and tutorials.
# # # # Days 56-60: Natural Language Processing (NLP)
- **Day 56-58:** Learn the basics of NLP (tokenization, stemming, lemmatization).
- Resource: Coursera Natural Language Processing Specialization by deeplearning.ai (Course 1, Week 1-2).
- **Day 59-60:** Implement basic NLP tasks with NLTK or spaCy.
- Resource: NLTK or spaCy documentation and tutorials.
# # # **Phase 3: Advanced Topics (Days 61-80)**
**Goal:** Explore advanced data science topics and techniques.
# # # # Days 61-65: Advanced Neural Networks and Deep Learning
- **Day 61-63:** Learn about convolutional neural networks (CNNs).
- Resource: Coursera Deep Learning Specialization by Andrew Ng (Course 4, Week 1-2).
# # # # Days 64-65:** Learn about recurrent neural networks (RNNs) and LSTMs.
- **Resource:** Coursera Deep Learning Specialization by Andrew Ng (Course 5, Week 1-2).
# # # # Days 66-70: Time Series Analysis
- **Day 66-68:** Learn the basics of time series analysis (trend, seasonality, ARIMA).
- **Resource:** "Introduction to Time Series and Forecasting" by Peter J. Brockwell and Richard A. Davis.
- **Day 69-70:** Implement time series forecasting models.
- **Resource:** Python libraries like statsmodels and Prophet.
# # # # Days 71-75: Advanced Data Visualization
- **Day 71-73:** Learn advanced techniques in Matplotlib and Seaborn.
- **Resource:** "Python Data Science Handbook" by Jake VanderPlas.
- **Day 74-75:** Explore interactive visualizations with Plotly and Bokeh.
- **Resource:** Plotly and Bokeh documentation.
# # # # Days 76-80: Big Data Technologies
- **Day 76-78:** Introduction to Big Data and Hadoop.
- **Resource:** "Hadoop: The Definitive Guide" by Tom White.
- **Day 79-80:** Learn about Spark for big data processing.
- **Resource:** "Learning Spark: Lightning-Fast Big Data Analysis" by Holden Karau.
# # # **Phase 4: Projects & Practice (Days 81-100)**
**Goal:** Apply the knowledge you've gained through hands-on projects and practice.
# # # # Days 81-85: Kaggle Competitions
- **Day 81-83:** Choose a Kaggle competition and start exploring the dataset.
- **Resource:** Kaggle competition page.
- **Day 84-85:** Build and refine your models based on the competition requirements.
- **Resource:** Kaggle tutorials and forums.
# # # # Days 86-90: Personal Project 1
- **Day 86-87:** Choose a personal project related to a topic of your interest (e.g., NLP, computer vision).
- **Day 88-90:** Collect data, perform analysis, and build a predictive model.
# # # # Days 91-95: Personal Project 2
- **Day 91-92:** Choose another personal project focusing on a different area (e.g., time series analysis, recommendation systems).
- **Day 93-95:** Implement the project and document your findings.
# # # # Days 96-100: Review and Showcase
- **Day 96-98:** Review all the projects and topics you've covered.
- **Day 99-100:** Create a portfolio showcasing your projects. Share your work on GitHub and LinkedIn.
# # # **Additional Tips:**
- **Consistency is Key:** Dedicate at least 1-2 hours every day to studying and practicing.
- **Join a Community:** Participate in online forums, attend meetups, and network with other data scientists.
- **Stay Updated:** Follow blogs, podcasts, and news related to data science to stay current with industry trends.
