Data Science and Business Analytics
Today, from regional offices to the boardroom, data drives decisions. Data science professionals are directly involved in the highest levels of the corporate and organizational decision-making process. Business Analysis and Analytics are in the top ten most in-demand jobs in the United States. Every industry, be it finance, retail, healthcare or information technology, has opened its doors for data science and analytics professionals.
Format: Online self-paced
Duration: 6-8 months
Tuition: $100/course*
*Discounts available for alumni, veterans, federal employees and active military
Who should take this course?
Working professionals with a bachelor’s degree or five years of professional experience.
Valuable Outcomes
Review the most popular analytics tools and technologies.
Apply analytics and data science to solve business problems on your own.
Explain Data Science uses and ramifications in various Industries like BFSI, Retail, E-commerce and Healthcare.
Identify key business insights from data and convey them to stakeholders in a clear and concise manner.
Create models that forecast future trends and utilize them to guide business decisions.
Apply the cutting edge ML algorithms to develop solutions for real-life business problems.
Design the AI strategy for your vertical and evaluate the various factors involved in its implementation.
Statistical Analysis and Visualization
The first part of the Data Science and Business Analytics program explains the foundational skills for practitioners: visualization, storytelling techniques, statistical analysis, and strategies for testing and experimentation. The courses in this series are as follows:
- Introduction to Data Science Landscape
- Course 1: Exploratory Data Analysis I
- Course 2: Exploratory Data Analysis II
- Course 3: Visualization Using Tableau
- Course 4: Data Storytelling
- Course 5: Inferential Statistics
- Course 6: Hypothesis Testing
- Course 7: Designing Business Experiments, July 31, 2025
This free introductory course serves as a preview and overview of the Data Science and Business Analytics program, providing you with a foundational understanding of the key concepts and skills you will develop throughout your learning journey.
In this course (the first of two on the topic), you will learn about various aspects of data visualization and exploration to derive meaningful insights from data.
Learn to visualize and interpret relationships between multiple variables using charts for different data types. This 15-hour, self-paced course builds your skills in multivariate analysis and effective data storytelling.
Learn to create dynamic, interactive dashboards using Tableau in this self-paced course. Gain hands-on experience transforming data into compelling visual stories—no coding required. Ideal for enhancing your data storytelling skills in business and analytics environments.
This module introduces you to Tableau, a leading tool for building interactive and visually engaging dashboards and stories. You’ll gain hands-on experience in using Tableau to turn data into clear, compelling visual narratives.
Learn to draw meaningful conclusions from sample data using probability, random variables, and the central limit theorem—essential inferential statistics tools for data-driven decision-making when full datasets aren’t available.
Master confidence intervals and hypothesis testing to make data-driven decisions. Learn essential statistical techniques—t-tests, proportion tests, chi-square tests—and apply them in Python through guided, self-paced modules.
John Bono
Kislaya Prasad
Machine Learning
The second part of the program moves on to more advanced topics in machine learning, such as linear regression, classification, decision trees, model selection, and clustering. The courses in this series are as follows:
- Course 1: Linear Regression in an Inferential Setting
- Course 2: Linear Regression in a Predictive Setting
- Course 3: Introduction to Classification: Logistic Regression
- Course 4: Classification: Performance Measures
- Course 5: Decision Trees
- Course 6: k-NN and Model Selection
- Course 7: Unsupervised Learning: Clustering
Launch dates TBA for courses 1 through 7.
P. K. Kannan
Anil K. Gupta