Course description:-

IE 6400. Foundations for Data Analytics Engineering is a 4-hour course that provides an introduction to the field of data analytics engineering. The course covers various concepts and applications related to data analytics and emphasizes the importance of data in decision-making processes.

The course covers topics such as probability and statistics for data analytics, including probability spaces, random variables, and distributions. It also covers descriptive statistics and exploratory data analysis.

Linear algebra fundamentals, including eigenvalues and eigenvectors, are also covered in the course. Students will learn about the application of eigenvalues and eigenvectors in data analysis.

The course also covers basics of clustering algorithms and their applications in grouping similar data points. Additionally, students will learn about text mining techniques for processing and analyzing text data, including natural language processing techniques.

Time series analysis, which involves analyzing temporal patterns in data, is also covered in the course. Students will learn about forecasting using time series models.

The course also includes a section on computational methods for data cleaning and wrangling. Students will learn various techniques for cleaning and preparing data, as well as implementation of computational methods.

Assignments and assessments in the course include regular problem sets and quizzes, as well as data cleaning and wrangling projects. There will also be a midterm exam, and students will complete a final project that involves applying the learned concepts to a real-world dataset.

IE 6400. Foundations for Data Analytics Engineering (4 Hours)

Course Outline:

1. Introduction to Data Analytics Engineering

• • Overview of data analytics concepts and applications
  • Importance of data in decision-making processes

1. Probability and Statistics for Data Analytics

• • Probability spaces, random variables, and distributions
  • Descriptive statistics and exploratory data analysis

1. Eigenvalues and Eigenvectors

• • Linear algebra fundamentals
  • Application of eigenvalues and eigenvectors in data analysis

1. Cluster Analysis

• • Basics of clustering algorithms
  • Applications in grouping similar data points

1. Text Mining

• • Processing and analyzing text data
  • Natural language processing techniques

1. Time Series Analysis

• • Analyzing temporal patterns in data
  • Forecasting using time series models

1. Computational Methods for Data Cleaning and Wrangling

• • Techniques for cleaning and preparing data
  • Implementation of computational methods

Assignments and Assessments:

• Regular problem sets and quizzes
• Data cleaning and wrangling projects
• Midterm exam
• Final project: Application of learned concepts to a real-world dataset