Data Visualization in Python Training (100% Job oriented)

1.1 Understanding the importance of data visualization in Python

1.2 Exploring different types of visualizations

1.3 Introduction to libraries for data visualization

2.1 Introduction to NumPy arrays

2.2 Basic array operations and manipulations

2.3 Working with multi-dimensional arrays

3.1 Introduction to Pandas dataframes

3.2 Data indexing and selection

3.3 Data cleaning and manipulation techniques

4.1 Overview of Matplotlib library

4.2 Setting up Matplotlib for plotting

4.3 Basic plotting functions and concepts

5.1 Creating line plots to visualize trends

5.2 Generating scatter plots for relationship exploration

5.3 Adding labels, titles, and legends to plots

6.1 Understanding frequency distributions with histograms

6.2 Visualizing categorical data using bar plots

6.3 Customizing bin sizes and bar widths

7.1 Styling plots with colors, markers, and line styles

7.2 Adjusting plot size, axis limits, and tick marks

7.3 Adding annotations and text to plots

8.1 Exploratory data analysis (EDA) methods

8.2 Visualizing distributions and correlations

8.3 Identifying outliers and anomalies

9.1 Applying data visualization in Python techniques to real-world datasets

9.2 Working on guided projects to reinforce learning

9.3 Solving data analysis challenges using visualization

10.1 Principles of effective data visualization

10.2 Designing clear and informative visualizations

10.3 Communicating insights effectively through visualization