Upskilling into the Latest Data Science Trends
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 a Professional Looking to Take Advantage of Emerging Opportunities in the Field of Data Science?
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 Certificate Program:
Our ten-month Data Science certificate is designed to provide solid theoretical frameworks in key areas of expertise within the field. Taught by seasoned industry experts, the program will guide participants through core principles, tools, and industry trends, culminating in a deep dive into artificial intelligence and machine learning.
This certificate includes the following courses:
Data Engineering
This course provides you with a technical overview of how to interpret, manage, and report data. You will learn about the history and principles of database systems and be able to source, prepare, and leverage historical data.
Python for Data Science
At its core, this course is project-based and integrates practical application as well as the opportunity to create and run your own Python projects.
Statistics for Data Science
Our Statistics for Data Science online program will provide you with the essential tools and analytical methods to manage data sets and extract meaningful insights from them.
Artificial Intelligence and Machine Learning
Acquire breadth of knowledge in predictive analytics, the Python programming language, and machine learning to confidently consider and resolve issues through the use of big data.
Strategic Data Storytelling
This online program will provide you with the techniques and tools you need to turn insights into compelling narratives. Over eight weeks, you will learn the art of conveying data in a meaningful way to support stakeholder decision-making and drive action.
Methodologies and Techniques Covered:
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 RStudio and its application
- Use scripting languages, including Python, to process, visualize, and analyze large data sets and implement machine learning solutions
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.
Career Outlook
Propelled by rising data volume and the growing adoption of sophisticated data management tools, the data science market platform is expected to grow 26.9% by 2027. Data engineering, statistics, AI and machine learning (ML), and Python coding are each fast-growing fields with an increased demand for qualified professionals. The World Economic Forum has ranked AI and ML roles as today’s most in-demand jobs. Statisticians will be 33% more employable by 2031. Python, one of the most popular programming languages, has seen an increase in the number of job openings for those who know it—from 70,242 positions in September 2019 to 79,942 in February 2021. Positions in data engineering are expected to grow 50% year-over-year.
- 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
Admissions Process
1
Candidacy Fee:
Pay the candidacy fee (non-refundable) and fill out the application form.
2
Interview
Receive a call for an interview with our admissions team.
3
Results
Our Admission Committee will inform you of the final decision*.
*(2 business days max)
