Here's a suggested 4-day, 2-hours-per-day step-by-step plan for revising Python for data analysis:
Day 1: Python Basics and Data Structures (2 hours)
1. Review Python basics (30 minutes):
- Variables, data types, operators
- Control structures (if/else, for loops, while loops)
- Functions
2. Data Structures (45 minutes):
- Lists: indexing, slicing, methods (append, extend, sort)
- Tuples: creation, indexing, slicing
- Dictionaries: creation, key-value pairs, methods (get, items, keys)
- Sets: creation, operations (union, intersection, difference)
3. Practice exercises (45 minutes):
- LeetCode, HackerRank, or (link unavailable) exercises
Day 2: NumPy, Pandas, and Data Manipulation (2 hours)
1. NumPy (45 minutes):
- Arrays: creation, indexing, slicing, operations (basic math, aggregation)
- Array methods (reshape, transpose, concatenate)
2. Pandas (45 minutes):
- DataFrames: creation, indexing, selecting data
- Data manipulation: filtering, grouping, sorting
- Merging and joining DataFrames
3. Practice exercises (30 minutes):
- Pandas tutorials, DataCamp exercises
Day 3: Data Visualization and File Handling (2 hours)
1. Data Visualization (45 minutes):
- Matplotlib: plotting basics, customizing plots
- Seaborn: visualization library, plotting distributions
2. File Handling (45 minutes):
- Reading and writing CSV files (pandas)
- Reading and writing Excel files (pandas)
- Handling JSON files (json library)
3. Practice exercises (30 minutes):
- Visualization exercises, DataCamp projects
Day 4: Data Analysis and Machine Learning (2 hours)
1. Data Analysis (45 minutes):
- Descriptive statistics (mean, median, mode)
- Inferential statistics (hypothesis testing)
- Data correlation and regression
2. Machine Learning (45 minutes):
- Scikit-learn: introduction, basic algorithms (linear regression, decision trees)
- Model evaluation metrics (accuracy, precision, recall)
3. Practice exercises (30 minutes):
- Scikit-learn tutorials, Kaggle competitions
Additional Tips:
- Practice with real-world datasets (e.g., UCI Machine Learning Repository)
- Use Jupyter Notebooks or Google Colab for interactive coding
- Review documentation and official tutorials for each library
- Join online communities (e.g., Reddit's r/learnpython, r/dataanalysis) for support
Resources:
- w3school
- DataCamp
- Kaggle
- UCI Machine Learning Repository
Stay focused, and you'll be well-prepared for data analysis with Python!
Would you like me to suggest specific exercises or projects?
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