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Date

Tuition

USD $7,950

Duration

Eight months
  • null

    Language

    This program is offered in English and Spanish

Certificate Format

Online learning with live, interactive sessions

Instructors

Gregory Bernstein, MS, Statistics for Data Science; Shree Bharadwaj, MS, and Ashish Pujari, MS, Data Engineering; Brian Craft, BA, MSc, Python for Data Science; and Utku Pamuksuz, MSc, PhD, Artificial Intelligence and Machine Learning; the University of Chicago

Upskilling into the Latest Data Science Trends

The constant flux of applications, programs, and technologies demands a diverse array of data science professionals with evolving knowledge and up-to-date toolkits.

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Unlocking solutions hidden in data to take on business challenges

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.

About the Certificate Program

Our eight-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.

The program is structured around four key areas of knowledge:

Data Engineering

Delve into understanding, managing, reporting, and leveraging data. This first module also covers the history and principles of database systems, how to clean raw data, and how to use SQL to load and query data in databases.

Python for Data Science

Through a highly technical, project-based approach, you will first be introduced to the principles of Python as a programming language and later have the opportunity to create and run your own projects.

Statistics for Data Science

Learn how to code and find meaningful, predictive trends in data. You will be provided with the tools to solve data science problems, taking you further into the world of machine learning. By the end of the course, you will be able to present a start-to-finish assessment using exploratory analysis and dimension reduction as well as linear and classification models.

Artificial Intelligence and Machine Learning

By guiding you through the mathematics and theory of machine learning, you will gain key insights into data investigation, exploration, supervised and unsupervised learning, and the transformation of big data into actionable intelligence.

Methodologies and Techniques Covered

After Completing the Program You Will Be Able To:

  1. Build and extract insights from document databases
  2. Design code that runs in parallel using multiprocessing and multithreading functionality
  3. Understand RStudio and its application
  4. Use scripting languages, including Python, to process, visualize, and analyze large data sets and implement machine learning solutions

Meet the Instructors

Gregory Bernstein, MS
Statistics for Data Science

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Gregory Bernstein works as a data scientist for Kinexon Sports and Media, a small 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 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, MS
Data Engineering and Storytelling with Data

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Shree Bharadwaj works at West Monroe, a management consulting company. As a Center of Excellence 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 operationalizing 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.

Brian Craft, BA, MSc
Python for Data Science

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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 developing models to understand consumer purchase behavior and identify emerging ingredient and flavor trends.

Utku Pamuksuz, MSc, PhD
Artificial Intelligence and Machine Learning

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Dr. Utku Pamuksuz is an AI researcher with expertise in data science, business analytics, applied mathematics, and machine/deep learning. He has been a frequent guest speaker, delivering keynote sessions in academic and professional seminars in Asia, Europe, and the US around the application and development of data analytics in the areas of e-commerce, finance, healthcare, management, quantitative marketing, and strategy. Pamuksuz has served as a senior data scientist at State Farm as well as W.W. Grainger. He co-founded Inference Analytics in 2018.

Ashish Pujari, MS
Data Engineering

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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 Asia, Europe, and North America.

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 by 26.9 percent 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 thirty-three percent 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 percent year-over-year.

$97k

The average annual base salary for a data scientist in the United States

#1

Python’s rank among programming languages that developers want to learn

26.9%

The expected CAGR of the global data science platform size from 2020 to 2027

Potential Job Titles in Data Science

  • 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