Data Science Bundle: 180 Hands-On Projects - Course 3 of 3

¡Hola! It seems you've provided a comprehensive list of projects across various domains that are typically covered in data science and machine learning courses, including both coding-based and Power BI projects. If you're looking to create a study plan for the next 60 days and want to cover these topics, here's a structured approach based on your provided list:

Week 1-2: Python Fundamentals & Data Manipulation

1. Projects: DictBuilder, CryptoPlanner, EggCatcher, RoutineTracker, SmartCalc
2. Topics: Basic Python syntax, data types, control structures, functions, modules, and packages.
3. Libraries: NumPy, pandas, matplotlib for data manipulation and visualization.

Week 3-4: Machine Learning Basics & Model Evaluation

1. Projects: VisualIntel, HeartBeatHero, FraudGuardian, ChurnSavior
2. Topics: Introduction to machine learning, scikit-learn, model evaluation metrics.
3. Libraries: scikit-learn for basic models, EVALML or Pycaret for easy evaluation.

Week 5-6: Advanced Machine Learning & Feature Engineering

1. Projects: SkyHighForecaster, FuelProphet, HomePriceWhiz, LifeSaver, CareerPro
2. Topics: Advanced machine learning models, feature engineering, hyperparameter tuning.
3. Libraries: XGBoost, LightGBM, TPOT (Hyperparameter Optimization Tool).

Week 7-8: Time Series & Natural Language Processing

1. Projects: RainMaster, GameQuest, BikeRider, CryptoPlanner, TweetBot
2. Topics: Time series analysis, NLP basics, working with text data.
3. Libraries: statsmodels for time series, NLTK or spaCy for NLP.

Week 9-10: Computer Vision & Game Development

1. Projects: ConcreteWizard, EggCatcher (computer vision aspect), CaterpillarGame, HangmanMaster
2. Topics: Image processing, object detection, game development with Python.
3. Libraries: OpenCV for computer vision, Pygame or Tkinter for game development.

Week 11: Power BI Projects

1. Projects: Patient Summary Dashboard, Global Super Store Sales Data Analysis, Boston Housing Dataset Dashboard, Crime in Los Angeles: Yearly City Analysis, IMDB Movie Dataset Dashboard, Toy Sales Data Analysis, Netflix Stock Price Dashboard, Personal Finance Management Dashboard, A Deep Dive into Bank Customer Churn with Power BI
2. Topics: Business Intelligence with Power BI, DA (Data, Analytics, Action) approach.

Final Stretch: Capstone Projects

1. Projects: Choose 3-4 projects from the list above that you find most interesting or most challenging.
2. Topics: Combining all skills learned, advanced analytics, data storytelling.

Tips:

• Consistency: Aim to spend at least 1-2 hours per day.
• Project Workflow: Start with a simple project, gradually increasing the complexity of your projects.
• Capstone Project: Ideally, you should have a portfolio-worthy project by the end of this study plan.
• Power BI: Dedicate specific weeks to learn and master Power BI. Ensure you understand data transformation and storytelling with it.
• Code: Always test your code or model with a small dataset before applying it to large datasets.
• Review: Regularly review the concepts and ensure that you are not just coding but also understanding the underlying principles of each topic. Remember, the key to success in data science is consistent practice and real-world application. Good luck on your journey to becoming a data scientist!