Data Exploration and Visualization detailed syllabus for Artificial Intelligence & Data Science (AI&DS) for 2021 regulation curriculum has been taken from the Anna University official website and presented for the AI&DS students. For course code, course name, number of credits for a course and other scheme related information, do visit full semester subjects post given below.
For Artificial Intelligence & Data Science 3rd Sem scheme and its subjects, do visit AI&DS 3rd Sem 2021 regulation scheme. The detailed syllabus of data exploration and visualization is as follows.
Course Objectives:
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Unit I
EXPLORATORY DATA ANALYSIS 9 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
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Unit III
UNIVARIATE ANALYSIS 9 Introduction to Single variable: Distributions and Variables – Numerical Summaries of Level and Spread – Scaling and Standardizing – Inequality – Smoothing Time Series.
Unit IV
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Unit V
MULTIVARIATE AND TIME SERIES ANALYSIS 9 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. 45 PERIODS
Practical Exercises:
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Course Outcomes:
At the end of this course, the students will be able to:
- Understand the fundamentals of exploratory data analysis.
- Implement the data visualization using Matplotlib.
- Perform univariate data exploration and analysis.
- Apply bivariate data exploration and analysis.
- Use Data exploration and visualization techniques for multivariate and time series data.
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, 2016. (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)
Reference Books:
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For detailed syllabus of all other subjects of Artificial Intelligence & Data Science, 2021 regulation curriculum do visit AI&DS 3rd Sem subject syllabuses for 2021 regulation.
For all Artificial Intelligence & Data Science results, visit Anna University AI&DS all semester results direct link.