Data visualization tools and techniques using python

This course introduces the fundamental concepts and techniques of data visualization using Python.

Course Fee - ₹ 2000

Overview

This course introduces the fundamental concepts and techniques of data visualization using Python. It covers the complete visualization pipeline, including data acquisition, preprocessing, and transformation using Pandas, and the creation of effective visualizations using Matplotlib and Seaborn. Students will learn to analyze statistical and time-series data, interpret visual patterns, and communicate insights through meaningful graphical representations. The course emphasizes hands-on learning with real-world datasets, including financial data from YFinance, and culminates in a capstone project that integrates data collection, cleaning, visualization, and insight generation for data-driven decision-making.

START DATE

Jul 01 2026

END DATE

Nov 30 2026

Delivery Mode

Offline

Duration

15 Hours

Type

Theory

Course Fee

₹ 2000

Credentials and Certification

1 Credit
1 Digital Badge
1 Certificate

Course Category Micro-Credential (Advanced)

TARGET AUDIENCE UG and PG students, IT professionals and developers

COURSE DELIVERY

Action-based learning materials with guided assessments and faculty support.

Digital Credential

indicating verified micro-credential completion.

RECOMMENDED FOR

AUDIENCE 01

Students exploring cloud computing

AUDIENCE 02

Professionals planning cloud projects

AUDIENCE 03

Faculty building digital deployment literacy

COURSE DELIVERY

Action-based learning materials with guided assessments and faculty support.

Course Outcomes

Our curriculum matches modern standard practices to provide exceptional training milestones.

Data Visualization Fundamentals

Understand the principles of data visualization and learn how effective visual design transforms complex datasets into meaningful business insights.

CORE KNOWLEDGE

Data Preparation with Python

Prepare, clean, and transform datasets using Python and Pandas to ensure accuracy, consistency, and readiness for effective data analysis.

PRACTICAL LEARNING

Visualization Techniques

Create informative visualizations using Matplotlib and Seaborn to represent patterns, trends, and relationships within real-world datasets.

TECHNICAL SKILLS

Statistical Data Analysis

Analyze statistical and time-series data to identify trends, measure performance, and support evidence-based decision-making across diverse domains.

ANALYTICAL THINKING

Insightful Data Communication

Interpret visual findings and communicate meaningful insights through clear, engaging, and audience-focused data storytelling and presentation techniques.

DATA STORYTELLING

End-to-End Visualization Projects

Develop complete data visualization projects by integrating data preparation, analysis, visualization, and interpretation to solve real-world problems.

INDUSTRY READINESS

ELIGIBILITY CRITERIA

Basic knowledge of computer operations and programming concepts. Familiarity with Python programming fundamentals (variables, data types, loops, functions, and libraries). Understanding of basic statistics and data handling concepts is desirable but not mandatory. Suitable for undergraduate students in Computer Science, Information Technology, Data Science, Artificial Intelligence, Electronics, and other engineering disciplines, as well as beginners interested in data visualization and analytics.

Faculty In Charge

Expert guidance from acclaimed industry professional leaders.

Dr Karthikeyan H

Assistant Professor

Karthikeyan H is associated with CHRIST (Deemed to be University), Bengaluru. His focus is on data analysis, visualization, and Python-based technologies.
He emphasizes on experiential and application-oriented learning. His work spans multiple areas of computational analytics, statistical modeling, and visual data storytelling.—skills that are highly relevant for modern data-driven industries.
In this course, Dr. Karthikeyan leverages their research insight and practical expertise to guide learners through the essentials of data visualization using Python. Their teaching philosophy centers on clarity, real-world relevance, and empowering learners to transform data into meaningful visual insights.

Program Overview

Academic Curriculum

A meticulous, guided learning path engineered to transform your cloud engineering expertise.

Module 1

Introduction to Data Visualization

Introduction to Data Visualization, Importance of Data Visualization, Types of Data and Charts, Visualization in the Data Science Lifecycle, Chart Selection Based on Data Types, Exploring Datasets for Visualization.

Module 2

Python Environment and Data Handling

Introduction to Python Libraries for Visualization (NumPy, Pandas, Matplotlib, Seaborn), Pandas DataFrames for Visualization, Importing Data from Various Formats (CSV, Excel, JSON, APIs), Data Exploration and Inspection, Working with Financial Data using YFinance.

Module 3

Data Preparation for Visualization

Data Cleaning Concepts, Handling Missing Data, Handling Duplicate Data, Data Transformation and Feature Extraction, GroupBy Operations and Aggregation Techniques, Data Preparation and Preprocessing for Visualization.

Module 4

Core Visualization Using Matplotlib

Introduction to Matplotlib, Line Charts, Bar Charts, Histograms, Scatter Plots, Chart Customization (Titles, Labels, Legends, Colors, Grids, Annotations), Best Practices for Effective Visualization and Chart Readability, Creating and Customizing Visualizations using Matplotlib.

Module 5

Statistical Visualization Using Seaborn

Introduction to Seaborn, Distribution Plots, Categorical Plots, Box Plots and Violin Plots, Heatmaps, Pair Plots, Time-Series Visualization Basics, Moving Averages and Trend Analysis (20-Day and 50-Day Moving Averages), Interpretation of Statistical Charts and Visual Patterns, Creating Statistical and Time-Series Visualizations using Seaborn.

Module 6

Capstone Project and Final Assessment

Capstone Project Planning and Problem Identification, Dataset Selection and Preparation, Data Cleaning and Exploratory Analysis, Visualization Design and Dashboard Development, Data Interpretation and Insight Generation, Project Documentation and Report Preparation, Final Project Presentation, Demonstration, Submission and Evaluation.

Data visualization tools and techniques using python

Overview

Credentials and Certification

COURSE DELIVERY

Digital Credential

Students exploring cloud computing

Professionals planning cloud projects

Faculty building digital deployment literacy

Course Menu

COURSE DELIVERY

Course Outcomes

Data Visualization Fundamentals

Data Preparation with Python

Visualization Techniques

Statistical Data Analysis

Insightful Data Communication

End-to-End Visualization Projects

ELIGIBILITY CRITERIA

Faculty In Charge

Dr Karthikeyan H

Assistant Professor

Academic Curriculum

Introduction to Data Visualization

Python Environment and Data Handling

Data Preparation for Visualization

Core Visualization Using Matplotlib

Statistical Visualization Using Seaborn

Capstone Project and Final Assessment

Build skills that matter in the real world