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Subject Syllabus

AL603 (B) - Data and Visual Analytics

AIML · Semester 6

Course Modules

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Elements, Variables, and Data categorization Levels of Measurement Data management and indexing Introduction to Statistical Concepts: Sampling Distributions, Resampling Statistical Inference and Descriptive Statistics Measures of central tendency Measures of location of dispersions

Statistical hypothesis generation and testing Chi-Square test t-Test Analysis of variance Correlation analysis Maximum likelihood test Regression Modelling Multivariate Analysis Bayesian Modelling, Inference and Bayesian Network Regression analysis

Intro to Data Wrangling Gathering Data Assessing Data Cleaning Data Design of Visualizations Univariate Exploration of Data Bivariate Exploration of Data Multivariate Exploration of Data Explanatory Visualizations

Overview of the Data Analyst Ecosystem Types of Data Understanding Different Types of File Formats Sources of Data Overview of Data Repositories NoSQL, Data Marts, Data Lakes ETL, and Data Pipelines Foundations of Big Data Big Data processing tools such as Hadoop, Hadoop Distributed File System (HDFS), Hive, and Spark

Python visualization libraries (matplotlib, pandas, seaborn, ggplot, plotly)Introduction to PowerBI tools Examples of inspiring (industry) projects Exercise: create your own visualization of a complex dataset