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.
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
- 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