MSDN Learn Paths

Introduction to Pandas

Get started with the most popular Python library for data analysis.

• Module 1: Setting up your Environment
• Module 2: Pandas DataFrames and Series (Completed)
• Module 3: Reading and Writing Data
• Module 4: Data Selection and Indexing

Data Cleaning and Preparation

Learn techniques to handle missing values, outliers, and inconsistent data.

• Module 5: Handling Missing Data
• Module 6: Data Transformation and Cleaning
• Module 7: Dealing with Duplicates

import pandas as pd
import numpy as np

data = {'col1': [1, 2, np.nan, 4, 5],
        'col2': [np.nan, 'b', 'c', 'd', 'e']}
df = pd.DataFrame(data)

# Fill missing values
df_filled = df.fillna(0)
print(df_filled)

# Drop rows with any missing values
df_dropped = df.dropna()
print(df_dropped)
                    

Data Merging and Joining

Combine datasets from different sources effectively.

• Module 8: Merging DataFrames (Inner, Outer, Left, Right)
• Module 9: Concatenating DataFrames

Data Aggregation and Grouping

Summarize and analyze data by groups.

• Module 10: GroupBy Operations
• Module 11: Aggregation Functions

Introduction to NumPy

Explore the fundamental package for scientific computing in Python.

• Module 12: NumPy Arrays
• Module 13: Array Operations
• Module 14: Broadcasting

Project: Analyze Sales Data

Apply your learned skills to a real-world dataset.

• Module 15: Project Setup and Data Loading
• Module 16: Data Cleaning and Exploration
• Module 17: Aggregation and Reporting
• Module 18: Final Project Submission

Overall Progress: