Data Visualization with Python

Unlock the power of data visualization with this course. It covers the fundamentals of data visualization, understanding statistical charts, creating and modifying charts using Python libraries. Equip yourself to effectively communicate complex data through impactful visualizations and enhance your analytical capabilities.

Duration

2 Hrs

Course level

Intermediate

Modules

Certificate

Included

Students Enrolled : 2000+

Key Features of the course

Create visualizations with Python.

Understand statistical charts.

Modify and enhance plots.

Communicate data visually.

Use Python libraries.

What you`ll Learn

Introduction to Data Visualization

Importance of Data Visualization: Understand why data visualization is crucial for data analysis and communication.
Types of Visualizations: Learn about different types of visualizations and their applications.
Python for Data Visualization: Get introduced to the Python libraries commonly used for data visualization.

Understanding Statistical Charts

Statistical Chart Types: Learn about various statistical charts, including bar charts, line charts, histograms, and scatter plots.
When to Use Which Chart: Understand the scenarios and data types best suited for each statistical chart.
Reading and Interpreting Charts: Develop skills to read and interpret data from different types of statistical charts.

Creating Charts Using Python

Python Libraries for Visualization: Get hands-on experience with Python libraries like Matplotlib, Seaborn, and Plotly.
Basic Chart Creation: Learn how to create basic charts using Python, including setting up your environment and writing code.
Customizing Charts: Understand how to customize charts by modifying colors, labels, and other visual elements.

Modifying Plots

Enhancing Visual Appeal: Learn techniques to enhance the visual appeal of your plots.
Interactive Visualizations: Get introduced to creating interactive visualizations using advanced Python libraries.
Advanced Plotting Techniques: Explore advanced plotting techniques to create complex and insightful visualizations.

Estimating Model Parameters

Parameter Estimation Techniques: Discover various techniques for estimating the parameters of linear regression models.
Ordinary Least Squares (OLS): Learn how to use OLS for parameter estimation.
Maximum Likelihood Estimation (MLE): Get introduced to MLE for estimating model parameters.

Using Gradient Descent to do Parameter Estimation

Gradient Descent Algorithm: Understand the gradient descent algorithm and its importance in parameter estimation.
Implementation: Learn how to implement gradient descent for both linear and logistic regression models.
Convergence Criteria: Explore the criteria for convergence and how to ensure accurate parameter estimation.

Measuring Performance

Performance Metrics: Learn about various metrics for measuring the performance of regression models, including accuracy, precision, recall, and F1 score.
ROC and AUC: Understand Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) for evaluating model performance.
Cross-Validation: Discover the importance of cross-validation in assessing model performance and preventing overfitting.

Selecting Predictors and Controlling Overfitting

Predictor Selection: Learn strategies for selecting the most relevant predictors for your regression models.
Regularization Techniques: Explore regularization techniques like Lasso and Ridge to control overfitting.
Model Complexity: Understand the trade-off between model complexity and performance, and how to achieve a balance.

Skills you’ll gain

Logistic Regression Fundamentals

Business Scenario Classification

Building Logistic Regression Models

Linear Regression Basics

Parameter Estimation Techniques

Gradient Descent for Parameter Estimation

Model Performance Measurement

Predictor Selection Strategies

Controlling Overfitting

Data-Driven Decision Making

Get certified

Upon successful completion of this course, you will receive a certificate from Hero Vired. This certificate, backed by Hero Group’s decades of research in Indian education, places you in an elite league of professionals. It validates your proficiency in both linear and logistic regression techniques, enhancing your career prospects and demonstrating your commitment to professional growth in data science and machine learning.

Frequently Asked Questions

Is Python good for data visualisation?

Yes, Python is great with Data Visualization. It is packed with libraries like Matplotlib, Seaborn, and Plotly that aid you in creating a vast number of visualisations with ease. Be it simple line charts or difficult 3D plots, Python takes care of all your problems.

What is the salary for Python data visualisation?

The salary range of an expert in Python data visualisation depends on a lot of factors: experience, location, and industry.

On average, professionals can expect a salary of 8.8 Lakhs to 18 lakhs on an annual basis. The senior roles or other positions that are much in demand might have even higher pay scales.

How do you master data visualisation with Python?

To master data visualisation using Python, one has to:

Learn basic programming in Python.

Explore libraries like Matplotlib, Seaborn, and Plotly.

Try your hand with real-world datasets.

Learn different types of visualisations and where to apply them.

Work on projects that test how much you're able to visualise, e.g., create really complex visualisations.

Keep learning new libraries and techniques.

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