Python Data Analysis and Visualization

Description

In this course, you will :

• How to analyse and visualise data using Python. Stop using Excel and start leveraging Python's power.
• How to analyse and visualise data using Python, NumPy, SciPy, Pandas, and Seaborn.
• You'll look at the four most important steps in any data analysis project: reading, describing, cleaning, and visualising data.
• You will use the most common and popular tools that data analysts use on a daily basis.
• Able to confidently extract knowledge and answers from data by the end of the course.

Syllabus :

1. What is Analytics

• Introduction to Analytics
• Analytics in Python

 

2. Python Basics for Analytics

• Getting Started
• Data Structures
• Control Flow and Built-in Functions
• Numpy an External Library
• Scipy an External Library

 

3. Reading Data

• Comma Separated Files
• JSON Files
• Raw Files

 

4. Describing Data

• Statistics and Counts
• Reshaping the Data

 

5. Cleaning Data

• Missing Data
• Outliers
• Scaling
• Categorical Data

 

6. Visualizing Data

• Introduction to Visualization
• Visualization with Scatter Plots
• Visualization with Bar Plots
• Visualization with Distributions
• Visualization with Line Graphs
• Visualization with Heat Maps
• Visualization with Multi-Plot Grids

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