Exploratory Data Analysis detailed syllabus for Computer Science & Design (CSD) for 2021 regulation curriculum has been taken from the Anna Universities official website and presented for the CSD 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 Computer Science & Design 5th Sem scheme and its subjects, do visit CSD 5th Sem 2021 regulation scheme. For Professional Elective-I scheme and its subjects refer to CSD Professional Elective-I syllabus scheme. The detailed syllabus of exploratory data analysis is as follows.
Course Objectives:
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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.
Unit II
EDA USING PYTHON
Data Manipulation using Pandas – Pandas Objects – Data Indexing and Selection – Operating on Data – Handling Missing Data – Hierarchical Indexing – Combining datasets – Concat, Append, Merge and Join – Aggregation and grouping – Pivot Tables – Vectorized String Operations.
Unit III
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Unit IV
BIVARIATE ANALYSIS
Relationships between Two Variables – Percentage Tables – Analysing Contingency Tables -Handling Several Batches – Scatterplots and Resistant Lines.
Unit V
MULTIVARIATE AND TIME SERIES ANALYSIS
Introducing a Third Variable – Causal Explanations – Three-Variable Contingency Tables and Beyond – Fundamentals of TSA – Characteristics of time series data – Data Cleaning – Timebased indexing – Visualizing – Grouping – Resampling.
Practical Exercises:
- Install the data Analysis and Visualization tool: R/ Python /Tableau Public/ Power BI.
- Perform exploratory data analysis (EDA) with datasets like email data set. Export all your emails as a dataset, import them inside a pandas data frame, visualize them and get different insights from the data.
- Working with Numpy arrays, Pandas data frames , Basic plots using Matplotlib.
- Explore various variable and row filters in R for cleaning data. Apply various plot features in R on sample data sets and visualize.
- Perform Time Series Analysis and apply the various visualization techniques.
- Perform Data Analysis and representation on a Map using various Map data sets with Mouse Rollover effect, user interaction, etc..
- Build cartographic visualization for multiple datasets involving various countries of the world; states and districts in India etc.
- Perform EDA on Wine Quality Data Set.
- Use a case study on a data set and apply the various EDA and visualization techniques and present an analysis report.
Course Outcomes:
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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”, First Edition, O Reilly, 2017. (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:
- 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.
For detailed syllabus of all the other subjects of Computer Science & Design 5th Sem, visit CSD 5th Sem subject syllabuses for 2021 regulation.
For all Computer Science & Design results, visit Anna University CSD all semester results direct link.