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AL-603 (B) · Data and Visual Analytics/Official Syllabus

Data and Visual Analytics (AL-603 (B)) - Official Syllabus

1
Unit 1

Data Definitions and Analysis Techniques

  • 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
2
Unit 2

Advance Data analysis techniques

  • 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
3
Unit 3

Data Wrangling

  • Intro to Data Wrangling
  • Gathering Data
  • Assessing Data
  • Cleaning Data
  • Data Visualization in Data Analysis: Design of Visualizations
  • Univariate Exploration of Data
  • Bivariate Exploration of Data
  • Multivariate Exploration of Data
  • Explanatory Visualizations
4
Unit 4

Data Ecosystem

  • 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
5
Unit 5

Data Visualization tools

  • 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

Reference Books

  • Data Science from Scratch — Joel Grus — Shroff Publisher /Ob4Reilly Media
  • Numsense! Data Science for the Layman — Annalyn Ng, Kenneth Soo — Shroff Publisher
  • Doing Data Science, Straight Talk from The Frontline — Cathy Ob4Neil, Rachel Schutt — Ob4Reilly Media
  • Mining of Massive Datasets — Jure Leskovek, Anand Rajaraman, Jeffrey Ullman — Cambridge University Press
  • Python Data Science Handbook — Jake VanderPlas — Shroff Publisher /Ob4Reilly Media
  • Data Analysis with Open Source Tools — Philipp Janert — Shroff Publisher /Ob4Reilly Media
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