DIGITAL NOTES OF DATA VISUALIZATION TECHNIQUES - (R22A6705)

Sep 17, 2026

Course notes on Data Visualization Techniques for B.Tech IV Year-I Semester (2025-26), focusing on statistical methods, visualization techniques, and core Python fundamentals. Covers data abstraction and validation concepts, visualization categories (scalar, point, vector, cluster, matrix), and hands-on workflows with Pandas including loading, cleaning, transformation, and descriptive statistics. Includes Matplotlib-based visualization (basic, specialized, advanced tools, waffle charts, word clouds) and Excel data analysis with cleaning, conditional formatting, filtering, lookup, pivot tables, validation, and analysis tools such as regression and smoothing.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages109

Reading Time4 h 35 min

AccessFree to download

Course Objectives

Unit I - Introduction to Data Visualization

Key factors and importance in Business Intelligence

Data abstraction and validation

Unit II - Visualization Techniques

Scalar, point, vector, multidimensional, cluster, matrix visualizations

Unit III - Getting Started with Pandas

Data structures, descriptive statistics, loading and storage

Reading/writing, web scraping, APIs, databases

Data cleaning, transformation, and string manipulation

Unit IV - Data Visualization Using Matplotlib

Matplotlib fundamentals and advanced visualization tools

Waffle charts and word clouds

Unit V - Working With Excel

Data analysis, cleaning, conditional formatting, filtering

Lookup functions, pivot tables, validation

Analysis tool pack: ANOVA, correlation/covariance, smoothing, Fourier, sampling, t-test/f-test, regression

Text Books and Reference Books

Course Outcomes

Unit I - Introduction to Data Visualization (Expanded Notes)

Definition and goals

Relation to data science and data presentation architecture

Importance across careers

DIGITAL NOTES OF DATA VISUALIZATION TECHNIQUES (R22A6705) B.TECH IV Year-I SEM (2025-26) PREPARED BY Dr. P.V. Naresh Associate Professor DEPARTMENT OF INFORMATION TECHNOLOGY MALLA REDDY COLLEGE OF ENGINEERING AND TECHNOLOGY (Autonomous Institution – UGC, Govt. of India) Sponsored by CMR Educational Society MALLA REDDY COLLEGE OF ENGINEERING AND TECHNOLOGY IV Year B. TECH–I-SEM L/T/P/C 3/0/0/3 DATA VISUALIZATION TECHNIQUES (R22A6705) COURSE OBJECTIVES: 1. To learn different statistical methods for Data visualization. 2. To learn Visulization Technoques . 3. To understand the basics of Python. 4. To understand the usage of the Matplotlib, Seaborn Packages 5. To Learn about Excel and various operations using Excel UNIT I Introduction to Data Visualization, Key factors of Data Visualization, Importance of Data Visualization in Business Intelligence, Data Visualization tools and types of data, Data Abstraction: data types, dataset types, Attribute types, Task Abstraction, Four Levels for Validation. UNIT II Visualization Techniques: Scalar and point visualizations, – vector visualizations – multidimensional visualizations – Cluster Visualizations – matrix visualization in Bayesian data analysis UNIT III Getting Started with Pandas: Arrays and vectorized computation, Introduction to pandas Data Structures, Essential Functionality, Summarizing and Computing Descriptive Statistics. Data Loading, Storage and File Formats. Reading and Writing Data in Text Format, Web Scraping, Binary Data Formats, Interacting with Web APIs, Interacting with Databases Data Cleaning and Preparation. Handling Missing Data, Data Transformation, String Manipulation UNIT IV (Data Visualization Using Matplotlib) Data Visualization Tools in Python- Introduction to Matplotlib, Basic plots Using matplotlib, Specialized Visualization Tools using Matplotlib, Advanced Visualization Tools using MatplotlibWaffle Charts, Word Clouds. UNIT-V (Working With Excel) Introduction: Data Analysis, Excel Data analysis. Working with range names. Tables. Cleaning Data. Conditional formatting, Sorting, Advanced Filtering, Lookup functions, Pivot tables, Data Visualization, Data Validation. Understanding Analysis tool pack: Anova, correlation, covariance, moving average, descriptive statistics, exponential smoothing, fourier Analysis, Random number generation, sampling, ttest, f-test, and regression. TEXT BOOKS: 1. Core Python Programming-Second Edition, R. Nageswara Rao, DreamtechPress. 2. A to Z of MS EXCEL: A Book for Learners & Trainers (MS Excel Comprehensive Guide by Rinkoo Jainn 3. Data Analysis with Excel by Manish Nigam. Bpb Publications 4. KNIME Essentials, by Gábor Bakos, 2013 5. Data Science Tools by Christopher Greco, 2020 REFERENCE BOOKS: 1. Introduction to Data Science a Python approach to concepts, Techniques and Applications, Igual, L;Seghi’, S. Springer, ISBN:978-3-319-50016-4. 2. ALL-IN-ONE-EXCEL 2022 Bible for Dummies by Bryant Shelton 3. Excel® 2019 BIBLE BY Michael Alexander, Dick Kusleika 4. Python for Data Analysis by William McKinney, Second Edition, O’Reilly MediaInc. COURSE OUTCOMES: At Completion of this course, students would be able to- 1. Apply statistical methods for Data visualization on Various Datasets 2. Understand different Visulization Tecniques. 3. Gain knowledge on various visualization techniques using Python 4. Understand usage of various packages in Python. 5. Understand the concept of Excel, Visualization using Excel Unit-I Introduction to Data Visualization, Key factors of Data Visualization, Importance of Data Visualization in Business Intelligence, Data Visualization tools and types of data, Data Abstraction: data types, dataset types, Attribute types, Task Abstraction, Four Levels for Validation. Introduction In our increasingly data-driven world, it’s more important than ever to have accessible ways to view and understand data.

FAQs

What are the main course objectives for data visualization techniques?

The course aims to learn statistical methods and visualization techniques, understand Python basics, and apply Matplotlib/Seaborn packages. It also covers Excel concepts and operations for visualization and analysis.

What topics are included in Unit I on data visualization?

Unit I covers introduction to data visualization, key factors, importance in business intelligence, data visualization tools and types of data, data abstraction (data types, dataset types, attribute types), task abstraction, and four levels for validation.

What does the course cover for visualization in Python and Excel?

Matplotlib-based visualization topics include basic and specialized/advanced plots, waffle charts, and word clouds. Excel topics include data analysis, range names, tables, cleaning data, conditional formatting, sorting, advanced filtering, lookup functions, pivot tables, data visualization, and data validation tools like ANOVA and regression.

Sep 17, 2026

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