Data Visualization and Analysis With Seaborn Library
Delve into data visualization and analysis with Python's Seaborn library. Learn about variable types, data cleaning, creating detailed visualizations, and applying themes for more appealing results.
I have had the privilege of gaining extensive academic and industrial experience, which has been instrumental in shaping my successful career. My unwavering dedication to excellence led me to pursue a Ph.D. at the esteemed University of Western Australia. During my studies, I focused on advancing the understanding of artificial intelligence models, culminating in a special mention on the Dean's list that recognizes exceptional academic achievement. At present, I am honored to serve as a Data Scientist at the renowned Fortescue Metal Group, where I apply my computer science skills to tackle complex problems that are intrinsic to the mining industry. Prior to this role, I dedicated myself to developing machine learning and artificial intelligence techniques for various ventures in Perth. Through my Post-Doctoral research, I had the opportunity to collaborate with the esteemed U.S. Department of Defense (DARPA) to develop cutting-edge algorithms that can deceive an unknown visual model. Additionally, my passion for programming languages is one of my defining attributes, and I continuously explore new avenues to develop and teach algorithms in Python, Java, C/C++, SQL, and Matlab.
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What You'll Learn
- The ability to perform data analysis using various statistical techniques
- The ability to use Pandas library for data extraction and cleaning
- The ability to use Seaborn plots for different data types
- The ability to use Seaborn’s distribution and regression plots to extract and visualize interesting trends in data
- The ability to use Seaborn's built-in themes, palettes, and grids to enhance the plot aesthetics
Learning Roadmap
2.
Introduction to Seaborn and Statistical Analysis
Introduction to Seaborn and Statistical Analysis
3.
Plotting Numerical Data
Plotting Numerical Data
9 Lessons
9 Lessons
5.
Plotting the Categorical Data
Plotting the Categorical Data
8 Lessons
8 Lessons
6.
Visualizing Distribution of Data
Visualizing Distribution of Data
7 Lessons
7 Lessons
7.
Visualizing Regression Models
Visualizing Regression Models
4 Lessons
4 Lessons
8.
Styling and Figure Aesthetics
Styling and Figure Aesthetics
6 Lessons
6 Lessons
9.
Multiplot Grids
Multiplot Grids
6 Lessons
6 Lessons
10.
Project
Project
2 Lessons
2 Lessons
Mohammad Amir Asim Khan Jalwana
I am a dedicated computer scientist passionate about learning and teaching.
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