CHRIST CUx

Skills

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

Learning 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.

Course Instructor

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.

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Sania Fathima

MBA

“The course provided useful knowledge and insights related to the subject. The content was informative and relevant, and it helped me gain a better understanding of the concepts covered.”

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Kaushik Vallioor Sankaran

MBA

“I thoroughly enjoyed the Micro Credential course. It was direct and very informative.”

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Adrija Roy

MBA

“The modules were structured in a simple and accessible way, making the concepts easy to understand even without prior knowledge of the topic. The workbooks provided valuable hands-on practice, and the course was an enriching learning experience that helped me build practical skills and confidence.”

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Xavier Amith J

MCA

“The hands-on demonstrations and coding sessions were my favourite part. Overall, it was a valuable learning experience that helped me learn beyond the classroom, explore a new area, and become more curious about what AI can do.”

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M A Lekshmi Narayanan

BCom

“It was interesting to learn about a new topic which was predominant in the world. The videos were short and kept me engaged throughout the course.”

Data visualization tools and techniques using python

Overview

Credentials and Certification

Students exploring industry-relevent skills

Professionals planning to upskill and reskill

Faculty building digital deployment literacy

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