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.
Datasets
Kaggle Datasets
UCI ML Repository
R for ML - github/@stedy |
Resources
Best Practices for ML Engineering (Google)
Short Online Courses
[Power BI]
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
Principles and Techniques of Data Science https://www.textbook.ds100.org/intro
Mining massive datasets http://www.mmds.org/