One of the most exciting aspects of business analytics is finding patterns in the data using machine learning algorithms. In this course you will gain a conceptual foundation for why machine learning algorithms are so important and how the resulting models from those algorithms are used to find actionable insight related to business problems. Some algorithms are used for predicting numeric outcomes, while others are used for predicting the classification of an outcome. Other algorithms are used for creating meaningful groups from a rich set of data. Upon completion of this course, you will be able to describe when each algorithm should be used. You will also be given the opportunity to use R and RStudio to run these algorithms and communicate the results using R notebooks.
Offered By
Business Analytics SpecializationUniversity of Illinois at Urbana-ChampaignAbout this Course
Experience using R to assemble data, summarize data, and visually explore data.
What you will learn
1. Conceptual framework of ML algorithms
2. Conceptual foundation for interpreting ML results
3. Practice applying ML algorithms to business data
Skills you will gain
- clustering
- regression
- R Programming
- classification
- prediction
Experience using R to assemble data, summarize data, and visually explore data.
Offered by
University of Illinois at Urbana-Champaign
The University of Illinois at Urbana-Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni. Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs.
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Syllabus - What you will learn from this course
Course Orientation and Module 1: Regression Algorithm for Testing and Predicting Business Data
Exploratory data analysis (EDA) is a critical step in the business analytic workflow; however, EDA is a time-consuming approach for uncovering complex relationships. Moreover, the visualizations that are often used for EDA do not lend themselves well for quantifying confidence in results or for making predictions.
Module 2: Framework for Machine Learning and Logistic Regression
Gain an understanding of machine learning in business and logistic regression
Module 3: Classification Algorithms
Classification algorithms in general, K-nearest neighbors, and decision trees.
Module 4: Clustering Algorithms
Clustering algorithms, k-means, and DBSCAN
About the Business Analytics Specialization
Our world has become increasingly digital, and business leaders need to make sense of the enormous amount of available data today. In order to make key strategic business decisions and leverage data as a competitive advantage, it is critical to understand how to draw key insights from this data. The Business Analytics specialization is targeted towards aspiring managers, senior managers, and business executives who wish to have a well-rounded knowledge of business analytics that integrates the areas of data science, analytics and business decision making.
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