Empowering Business Success through Data Science Expertise
As the business world evolves, professionals in finance roles need to continuously reskill and upskill to stay on top of the latest industry tools, trends, and terminology. The constant flux of applications, programs, and technologies demands a diverse array of data science professionals with evolving knowledge and up-to-date toolkits.
Certificate Overview
Data science, an interdisciplinary field that includes mathematics, statistics, computer science, and other science skills, seeks to extract actionable insights from the large and ever-growing volumes of data that organizations currently generate. Data science practitioners prepare data for analysis and processing, perform advanced data analysis, and present results to reveal patterns and enable stakeholders to draw informed conclusions.
Are You Looking to Leverage Emerging Data Science Opportunities to Enhance Business Outcomes?
Professionals with backgrounds in computer science, data analysis, mathematics, programming, or statistics who want to sharpen their skills for today’s data-driven industries will highly benefit from this certificate program.
About the Learning Journey
The Professional Certificate in Data Science for Business comprises the following eight-week courses:
This certificate includes the following courses:
Course 1: Data Engineering
This course provides a technical overview of data collection, storage, management, and usage to bolster business intelligence.
Course 2: Python for Data Science
Project-based and application-oriented, the course will teach you how to design, write, and run high-performing Python code.
Course 3: Statistics for Data Science
This course equips you with the core tools to manage datasets, extract meaningful insights, and enhance decision-making processes.
Course 4: Artificial Intelligence and Machine Learning
In this course, you will expand your Python skills and acquire the mathematical and theoretical frameworks to leverage machine learning.
Course 5: Strategic Data Storytelling
The course teaches you the art of conveying insights through compelling narratives to drive impactful, data-led decision-making.
Methodologies and Techniques
After completing the program, you will be able to:
- Build and extract insights from document databases.
- Design code that runs in parallel using multiprocessing and multithreading functionality.
- Understand R and RStudio and their applications.
- Use scripting languages, including Python, to process, visualize, and analyze large datasets and implement machine-learning solutions.
- Apply techniques and tools to turn data-driven insights into compelling narratives.
Earn a certificate of completion from the University of Chicago and become part of the UChicago network.
Meet Your Instructors
Abid Ali, PhD
Data Engineering Instructor
Abid Ali has spent a large part of his career working in data and analytics at major consulting firms, designing and delivering large-scale transformations worldwide across industries. He leads internal initiatives and capabilities and works with C-suite executives to devise strategies for migration and transition to modern data platforms.
A believer in lifelong learning, Ali has earned several advanced degrees, including two master’s degrees, an EMBA, and a PhD in Organizational Leadership, as well as certifications from Teradata, Celonis, SAFe Agile, Azure, and AWS.
Gregory Bernstein, MSc
Statistics for Data Science Instructor
Gregory Bernstein works as a data scientist and product manager for Kinexon Sports and Media, a branch of a Germany-based company that works with teams in the NBA, NFL, and other professional leagues to monitor athletes’ movement and exertion, provide consultation on load management, and optimize in-game performance. Bernstein enjoys working in the start-up environment and engages in analytical modeling, content creation, and strategic decision-making for product development. He earned a BA from Lafayette College in 2012 and an MSc in Analytics from the University of Chicago in 2019.
Patrick McQuillan, MBA
Python for Data Science Instructor
Patrick McQuillan is passionate about data as a tool for change and decision-making. He has held leadership roles, most recently as the Global Head of Data Governance and Operational Effectiveness at Wayfair. Previously, he led international consulting teams to drive AI strategy and technology enablement for Fortune 100, government, and higher education clients.
A sought-after subject matter expert in data governance, business intelligence, and AI strategy, McQuillan earned an MBA from the University of Oxford and a Bachelor of Economics and International Affairs from Northeastern University.
Utku Pamuksuz, PhD
Artificial Intelligence and Machine Learning Instructor
Utku Pamuksuz is an AI and analytics professor with expertise in data science, applied mathematics, and machine and deep learning. As a frequent guest speaker, he delivers academic and professional seminars. His published research involves AI algorithms in management, finance, strategy, healthcare, e-commerce, and quantitative marketing. He cofounded Inference Analytics, a Chicago-based healthcare analytics company, and serves as chief scientist.
Dr. Pamuksuz has a PhD in IS/Analytics from the University of Illinois Urbana-Champaign and an MSc in Computer Science from Northwestern University.
Rebeca Pop, MA
Strategic Data Storytelling Co-Instructor
Rebeca Pop is the founder of Vizlogue and an expert in data storytelling and visualization. She has delivered presentations to over 3,500 participants worldwide. Her training approach is grounded in understanding adult learning strategies and combines hands-on exercises with feedback sessions and real-life examples.
Pop has a decade of experience in marketing science and analytics at media agencies across industries. She has published thought leadership articles on Everviz.com, the Data Visualization Society’s Nightingale, and the UN blog. She earned her MA from the University of Oklahoma.
Bridget Sheahan, MBA, CFA
Strategic Data Storytelling Co-Instructor
Bridget Sheahan works as the VP of Analytics and Insights at Vericast, a global media and marketing services company. She leads teams that work with companies of all sizes and verticals to create, innovate, and consult on marketing strategy. Prior to her current role, Sheahan held a number of analytics-related and marketing positions at retail, automotive, and financial companies.
She earned a BA from Colgate University and an MBA from the University of Pittsburgh’s Katz Graduate School of Business.
Michael Colella, MS, MA, MS
Senior Director of Global Data Strategy and Analytics, AXS
Michael Colella is the senior director of Global Data Strategy and Analytics at AXS, where he leads business intelligence, analytics engineering, and web analytics. He is passionate about helping organizations use advanced analytics and AI to thrive.
Career Outlook
In today’s rapidly evolving business landscape, data science has become a cornerstone of decision-making across industries. Given data’s exponential growth, businesses increasingly rely on data scientists to extract insights and drive strategic initiatives. As companies continue to invest in technology and analytics, the demand for skilled data scientists is expected to surge, offering lucrative career opportunities in finance, healthcare, retail, and beyond. With the potential to revolutionize operations, enhance customer experiences, and boost profitability, data science remains at the forefront of innovation, shaping the future of business.
108
kThe average annual base salary for a data scientist in the United States.
Source: U.S. Bureau of Labor Statistics
35
%The projected growth in employment of data scientists to 2032.
Source: U.S. Bureau of Labor Statistics
26.9%
The expected CAGR of the global data science platform size to 2027.
Source: Cision
- Analytics Consultant
- AI Engineer
- AI Specialist
- Big Data Engineer
- Business Intelligence Developer
- Business Intelligence Engineer
- Computer Vision Engineer
- Data Architect
- Data Insight Analyst
- Data-Mining Analyst
- Data Scientist
- Database Administrator
- Entry-Level Software Developer
- GIS Analyst
- Junior Python Developer
- Machine Learning Engineer
- Machine Learning Researcher
- Machine Learning Specialist
- Python Full-Stack Developer
- Quality Assurance Engineer
- Senior Python Developer
- Statistician
Admission Process
1
Application
Complete the application form and pay the non-refundable fee.
2
Interview
Receive a call for an interview with our Admissions Team.
3
Results
Our Admissions Committee will inform you of their final decision within two business days.