Subject code AD3301 deals with semester III of B.Tech Artificial Intelligence and Data Science regarding affiliated institutions of Anna University Regulation 2021 Syllabus. In this article, you can gather certain information relevant to the Data Exploration And Visualization. We added the information by expertise.
We included the proper textbooks and references to assist in some way in your preparation. It will enhance your preparation and strategies to compete with the appropriate spirit with others in the examination. If you see, you can find the detailed syllabus of this subject unit-wise without leaving any topics from the unit. In this article AD3301 – Data Exploration And Visualization Syllabus, You can simply read the following syllabus. Hope you prepare well for the examinations. I hope this information is useful. Don’t forget to share with your friends.
If you want to know more about the syllabus of B.Tech Artificial Intelligence And Data Science connected to an affiliated institution’s four-year undergraduate degree program. We provide you with a detailed Year-wise, semester-wise, and Subject-wise syllabus in the following link B.Tech. Artificial Intelligence And Data Science Syllabus Anna University, Regulation 2021.
Aim of Objectives:
- To outline an overview of exploratory data analysis.
- To implement data visualization using Matplotlib.
- To perform univariate data exploration and analysis.
- To apply bivariate data exploration and analysis.
- To use Data exploration and visualization techniques for multivariate and time series data.
AD3301 – Data Exploration And Visualization Syllabus
Unit I: Exploratory Data Analysis
EDA fundamentals – Understanding data science – Significance of EDA – Making sense of data – Comparing EDA with classical and Bayesian analysis – Software tools for EDA – Visual Aids for EDA – Data transformation techniques – merging database, reshaping and pivoting, Transformation techniques – Grouping Datasets – data aggregation – Pivot tables and cross-tabulations.
Unit II: Visualizing Using Matplotlib
Importing Matplotlib – Simple line plots – Simple scatter plots – visualizing errors – density and contour plots – Histograms – legends – colors – subplots – text and annotation – customization – three dimensional plotting – Geographic Data with Basemap – Visualization with Seaborn.
Unit III: Univariate Analysis
Introduction to Single Variable: Distributions and Variables – Numerical Summaries of Level and Spread – Scaling and Standardizing – Inequality – Smoothing Time Series.
Unit IV: Bivariate Analysis
Relationships between Two Variables – Percentage Tables – Analyzing Contingency Tables Handling Several Batches – Scatterplots and Resistant Lines – Transformations.
Unit V: Multivariate And Time Series Analysis
Introducing a Third Variable – Causal Explanations – Three-Variable Contingency Tables and Beyond – Longitudinal Data – Fundamentals of TSA – Characteristics of time series data – Data Cleaning – Time-based indexing – Visualizing – Grouping – Resampling.
Text Books:
- Suresh Kumar Mukhiya, Usman Ahmed, “Hands-On Exploratory Data Analysis with Python”, Packt Publishing, 2020. (Unit 1)
- Jake Vander Plas, “Python Data Science Handbook: Essential Tools for Working with Data”, Oreilly, 1st Edition, 201 6. (Unit 2)
- Catherine Marsh, Jane Elliott, “Exploring Data: An Introduction to Data Analysis for Social Scientists”, Wiley Publications, 2nd Edition, 2008. (Unit 3,4,5)
References:
- Eric Pimpler, Data Visualization and Exploration with R, GeoSpatial Training service, 2017.
- Claus O. Wilke, “Fundamentals of Data Visualization”, O’Reilly publications, 2019.
- Matthew O. Ward, Georges Grinstein, Daniel Keim, “Interactive Data Visualization: Foundations, Techniques, and Applications”, 2nd Edition, CRC Press, 2015.
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