Data Visualization with Python: Crash Course
- A desire to transform complicated data into beautiful charts
- A little bit knowledge on python programming language
- Must have Jupyter and other required packages and libraries
Data Visualization is one of the indispensable topic in Data Science and Machine learning. It is process of creating interactive visuals to understand trends, variations, and derive meaningful insights from the data. We all know that Data is the new oil. Likewise if oil is not processed it is of no use and to derive relevant insights from data for making critical business decisions, it should be in cleaned and processed.
Data visualization make this task little bit more handy and fast. With the help of visual charts and graph, we can easily find out the outliers, nulls, random values, distinct records, the format of dates, sensibility of spatial data, and string and character encoding and much more.
In this learning course, you will be learning different charts to represent different kind of data like categorical, numerical, spatial, textual and much more.
Bar Charts (Horizontal and Vertical)
Grouped Bar Chart (Horizontal and Vertical)
Segmented Bar Chart (Horizontal and Vertical)
Time and series Chart
Multiple Line Charts and so on.
Most of the time data scientists pay little attention to graphs and focuses only on the numerical calculations which at times can be misleading. Data visualization is much crucial step to follow to achieve goals either in Data Analytics or Data Science to get meaningful insights or in machine learning to build accurate model.
Who this course is for:
- Machine learning engineers
- Big Data Analyst
- Data Scientist
- Everyone who loves to play with data just like me
- 03:25Setup Environment
I am an Instructor, Devops engineer, machine learning enthusiast, cloud expert and passionate developer.
I have authored 60+ courses with over 80,000+ students worldwide across 175+ countries on wide array of technologies like containerization, machine learning, Linux, programming languages and cloud computing platforms like Microsoft Azure, Amazon Web Service and IBM Cloud.