Data Analytics for Business

Data Analytics in Business

The goal of this course, from an interdisciplinary perspective, is to introduce students to the foundation of data science life cycle, knowledge extraction, visualization, and interpretation, as well as the ethical implications of data analytics in business.


The course is designed in a way that provides students with a solid theoretical and practical exposure to prepare them for entry-level and mid-level data analyst skills for research and job market roles.


Course Outline

  • Big Data and Analysis in the Real World

  • Data Governance , Management, Privacy & Ethics

  • The Data Analyst's Portfolio

  • Statistical Methods - Inferences, Regression Analysis, Basic Concepts

  • Data Collection

  • Data Wrangling

  • Data Visualization

  • Exploratory Data Analysis

  • Anomaly Detection

  • Social Network Analysis

  • Text Analysis

  • Machine Learning - Supervised & Unsupervised


Laboratory Practice

  • Data Analysis & Visualization with

    • R

    • Python

    • Power BI

  • Data Workflow

    • Git


Guest Lectures, Seminars, Workshops, MasterClasses

To be announced.

Reading List & Online Resources

Python [Books]

  • Bernard, Joey. "Python data analysis with pandas." Python Recipes Handbook. Apress, Berkeley, CA, 2016. 37-48.

  • Nelli, Fabio. Python data analytics: Data analysis and science using PANDAs, Matplotlib and the Python Programming Language. Apress, 2015.

  • Milliken, Connor P. Python Projects for Beginners. Apress, 2020.

  • Pochiraju, Bhimasankaram, and Sridhar Seshadri, eds. Essentials of Business Analytics: An Introduction to the Methodology and Its Applications. Vol. 264. Springer, 2019.


R [Books]

  • Campbell, Matthew. "Essential R Packages: Tidyverse." Learn RStudio IDE. Apress, Berkeley, CA, 2019. 63-72.

  • Campbell, Matthew. "Learn RStudio IDE." New York: Apress (2020).

  • Wade, Ryan. "Creating R Custom Visuals in Power BI Using ggplot2." Advanced Analytics in Power BI with R and Python. Apress, Berkeley, CA, 2020. 39-148.


Data Mining Books

  • Gorunescu, Florin. Data Mining: Concepts, models and techniques. Vol. 12. Springer Science & Business Media, 2011.

  • Sullivan R. (2012) Statistical Methods. In: Introduction to Data Mining for the Life Sciences. Humana Press. https://doi.org/10.1007/978-1-59745-290-8_6