Python for Data Analytics Online Course
Python for Data Analytics Online Course
This course is perfect for those working on machine learning projects who want to uncover patterns and insights through visualization. It covers detailed Exploratory Data Analysis (EDA) using real-world business scenarios and raw data. You’ll learn to use NumPy and Pandas for data analytics, master various charts and graphs, and explore advanced visualizations like boxplots, violin plots, and heatmaps with Seaborn. By the end, you'll confidently apply Python libraries like Matplotlib, Seaborn, and Bokeh for creating impactful visualizations in your projects.
Who is this Course for?
This course is designed for Python developers, machine learning practitioners, data scientists, data analysts, and business analysts. It is also valuable for leaders, managers, and professionals who present data visually, including developers, architects, and system analysts. While a basic understanding of Python is helpful, it is not a requirement.
What you will learn
- Visualization concepts
- Create simple plots using Matplotlib
- Marginal histograms and marginal boxplots
- Handling images using pixel metrics
- Categorical variables and histograms (with EDA)
- Data generation techniques
Course Table of Contents
Matplotlib and Seaborn – Libraries and Techniques
- Introduction
- What You Will Learn
- Visualization Concepts
- Introduction to Matplotlib
- Creating Simple Plots Using Matplotlib
- Creating Scatter Plots
- Creating Axis Limits
- Parameterizing Plots
- Creating Error Bars
- Plotting Histograms and Box Plots
- Plotting 2D Histograms
- Marginal Histograms and Marginal Boxplots
- Working with Subplots
- Stock Trend / Time Series Plot and Annotations
- Plotting Images and Clustering
- Creating 2D Contour plots for 3D Data
- Creating 3D Plots Including 3D Contours
- Stylesheets, rcParam, and Custom Stylesheets
Advanced Visualizations Using Business Applications
- Single and Multiple Bar Charts
- Area and Stacked-Area Charts
- Drawing Pie Charts
- Bubble Charts with Vectorization of Properties
- Plotting Regression Lines with OLS (ML)
- Categorical Variables and Histograms (with EDA)
- Seaborn Boxplot, Violin plot, Categorical Scatterplot
- Seaborn Slopeplots for Comparing Distributions
- Dumbbell Plot for Category-Wise Value Movement
- Creating Heatmaps
- Working with Pairplots
- Seasonal Trendcharts
- Yearplot and Calendarplot for Color-Scaled Trends
- Radarplot to Compare Scores of Multiple Parameters
Working with the Beautiful and Powerful Bokeh Library
- Introduction to Bokeh
- Creating Simple and Multiple Line Plots
- Customizing Your Plots
- Creating Bubble Plots – Vectorizing Your Plot
- Working with Layouts – Row / Column / Grid
- Using the ColumnDataSource Object
- Applying Filters – IndexFilter, BooleanFilter, GroupFilter
- Widgets – Dynamic Plot Controls
- Plotting on a Google Map Using Google Map API
- Closing Notes
