Open Source Ecosystems

Week 1: Understanding Data Science and Pandas
- What is Data Science?
- Introduction to Pandas: History and Evolution
- The Role of Pandas in Data Analysis
Week 2: Setting Up the Environment
- Installing Pandas via Anaconda and pip
- Overview of Jupyter Notebooks
- Importing Pandas and Common Libraries
```
import pandas as pd
```

Week 3: Introduction to Pandas Series
- What is a Pandas Series?
- Creating a Series from a List
```
s = pd.Series([1, 2, 3, 4])
```
- Creating a Series from a Dictionary
```
s = pd.Series({'a': 1, 'b': 2, 'c': 3})
```
Week 4: Accessing and Modifying Series
- Accessing Elements by Index
  - Positional Indexing
  - Label Indexing
- Modifying Series Data
  - Changing Values and Index
```
s[0] = 10
```

Week 5: Understanding Series Attributes
- Key Attributes: index, values, dtype, name
- Using len() to Get Series Length
- Data Types in Series
Week 6: Common Series Methods
- Summary Statistics: .sum(), .mean(), .min(), .max()
- Using .describe() for Quick Overview
- String Methods: .str Accessor
```
s = pd.Series(['apple', 'banana', 'cherry'])
s.str.upper()
```

Week 7: Handling Missing Data in Series
- Identifying Missing Values: isnull(), notnull()
- Filling Missing Values: fillna()
- Dropping Missing Values: dropna()
Week 8: Vectorized Operations and Functions
- Applying Functions: apply(), map(), applymap()
- Common Mathematical Operations
- Using Numpy Functions with Series

Week 9: What is a DataFrame?
- Understanding the Structure of a DataFrame
- Creating DataFrames from Various Data Structures (Lists, Dictionaries, Numpy Arrays)
```
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': ['a', 'b', 'c']
})
```
Week 10: DataFrame Attributes and Basic Operations
- Key Attributes: shape, columns, index, dtypes
- Basic Operations: .head(), .tail(), .info(), .describe()
- Accessing and Modifying Columns
```
df['C'] = [4, 5, 6]
```

Week 11: Basic Indexing and Slicing
- Selecting Columns and Rows
- Label-Based Indexing with .loc[]
- Position-Based Indexing with .iloc[]
Week 12: Advanced DataFrame Selection
- Conditional Selection and Filtering
- Using Boolean Indexing
- Chaining Conditions

Week 13: Handling Missing Data in DataFrames
- Identifying Missing Data
- Imputing and Filling Missing Values
- Dropping Rows and Columns with Missing Data
Week 14: Data Type Conversion and Transformation
- Converting Data Types: astype()
- DataFrame Transformation Methods
- String Manipulations: Using .str Methods in DataFrames

Week 15: Grouping DataFrames
- Introduction to groupby()
- Aggregating Data: Mean, Sum, Count
- Custom Aggregation Functions
Week 16: Merging and Joining DataFrames
- Different Types of Joins: Inner, Outer, Left, Right
- Using merge() and join() Methods
- Concatenating DataFrames: concat()

Week 17: Time Series Basics
- Creating DateTime Index
- Resampling Time Series Data
- Handling Time Zones
Week 18: Time Series Operations
- Rolling and Expanding Windows
- Time Series Visualization Techniques
- Date Arithmetic

Week 19: Introduction to Visualization
- Basic Plotting with Pandas
- Customizing Plots: Titles, Labels, Legends
- Plot Types: Line, Bar, Histogram, Boxplot
Week 20: Advanced Visualization Techniques
- Creating Subplots and Multiple Plots
- Integrating with Matplotlib for Custom Visuals
- Saving and Exporting Plots

Week 21: Performance Optimization Techniques
- Profiling DataFrames: Memory Usage
- Using Vectorization for Speed
- Best Practices for Handling Large Datasets
Week 22: Writing Clean and Maintainable Code
- Code Readability and Documentation
- Error Handling Techniques
- Developing Reusable Functions

Recommended Books: Python for Data Analysis by Wes McKinney, Pandas Cookbook by Theodore Petrou
Online Courses: DataCamp, Coursera, edX
Practice Datasets: Kaggle, Open Data Sources