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
Ashish Pujari is a leader in data and analytics, IT strategy, and technology consulting. As a Director of Analytics at GLG, Pujari leads the design and implementation of business intelligence, predictive analytics, and visualization. Before joining GLG, Pujari served as an AVP of Analytics Architecture for CNA Insurance, where he was responsible for insurance analytics platforms and data strategy. Pujari specializes in big-data analytics, cloud computing, algorithm development, application and database design, decision management, and visualization technologies. He has been involved in technology consulting in finance, banking, insurance, and communications domains for clients in Europe, North America, and Asia.
Brian Craft is a seasoned data scientist with years of industry experience. In his role as a data scientist at Conagra Brands, he focuses on scaling their machine learning capabilities and develops models to understand consumer purchase behavior and identify emerging ingredient and flavor trends.
Gregory Bernstein works as a data scientist for Kinexon Sports and Media, a small branch of a German based company working with teams in the NBA, NFL and other professional leagues to monitor athletes’ movement and exertion, provide consultation regarding load management, and optimize in-game performance. Bernstein enjoys working in the startup environment and engages in analytical modeling, reporting and visualization, and product development. He earned a BA from Lafayette College in creative writing and economics in 2012 and graduated from the University of Chicago Master of Science in Analytics program in 2019. In addition to sports, Bernstein has a passion for teaching and education and has previously worked with the Princeton Review and as a TA for both the MS Analytics program and the Booth School of Business.
Shree Bharadwaj works at West Monroe, a management consulting company. As a M&A Data and AI Practice Lead, he advises private equity and venture capital firms and C-level executives on value creation, post-close synergies, and data and analytics (BI/AI) strategies focused on business outcomes. In his previous executive leadership role at Syndigo, he led the data strategy, data science, next-gen platform and M&A integrations. His expertise revolves around driving innovation, standardization, development and operationalization of machine learning models, data engineering at scale using on-premise and cloud platforms, effective data visualizations, model-driven design, and algorithmic thinking. Bharadwaj was elected to the Global Standards Architecture Board at GS1, where he worked with global industry leaders to develop standards, road maps, and governance and compliance requirements relating to food services, healthcare, retail, supply chain, and CPG/FMCG verticals. His experience spans across multiple industries that include AdTech, EdTech, Fintech, healthcare, MarTech, public safety, retail, and telecom in organizations that range from startups to Fortune 100 companies.
Dr. Pamuksuz is an AI researcher with expertise in data science, business analytics, applied mathematics, and machine/deep learning. He has been an invited speaker, delivering keynote sessions in academic and professional seminars in Europe, Asia, and the US, for application and development of data analytics in the areas of management, finance, strategy, healthcare, e-commerce, and quantitative marketing. Pamuksuz has served as a senior data scientist at State Farm as well as W.W. Grainger. He co-founded Inference Analytics in 2018.
Bridget Sheahan works as the VP of Analytics and Insights at Vericast (formerly Valassis Marketing Solutions), a global media and marketing services company. She leads the analytics services, data innovation, and data science teams. Prior to her current role at the company, Sheahan held various analytics-related positions, including serving as executive director of Client Analytics. Before joining Vericast, she was the director of Pricing and Marketing Analytics at the Learning Care Group.
Sheahan earned her bachelor’s degree 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:
2
Interview
3
Results
Our Admission Committee will inform you of the final decision*.
*(2 business days max)
