New College of Florida offers four graduate certificate programs that can be completed online, in person, or in a hybrid fashion, designed to provide you with valuable educational experience in a more accessible, focused format than a traditional master’s degree program. Certificates include Data Analysis and Visualization with R, Machine Learning with Python, Statistical Modeling, and Distributed Computing.

Certificate courses are offered concurrently with the MS in Applied Data Science program and each course is delivered over a 7-week period. Lectures are scheduled 4 – 7 pm on either Mondays and Wednesdays, or Tuesdays and Thursdays.

Certificate programs are charged by the credit hour for a total of 12 credit hours. Learn more about our affordable Graduate Program Tuition & Fees.

You can use the credits completed towards earning your MS in Applied Data Science later. Just apply to be admitted to the M.S. program and transfer your course credits.

Application Information

Click here to apply!

Each certificate program’s prerequisite’s are listed below. The Application requirements include:

  • Application (no application fee required)
  • CV/Resume
  • One-paragraph Statement of Purpose
  • Official post-secondary transcripts indicating that a Bachelor’s degree has been conferred

For more information, please contact Assistant Director of Graduate Admissions Nikita Bagley at 941-487-4103 or nbagley@ncf.edu

Data Analysis and Visualization with R Certificate

Learn to tell the best story from data in the most effective way. This 3-course certificate program provides participants with a solid background in data analysis and data visualization using the widely used data science programming language R. Starting with a course that introduces the fundamental principles in data extraction, loading, pre-processing, and analysis, it continues with a course on applied statistics and concludes with a course on Data Visualization. This certificate is great for professionals who routinely prepare reports and dashboards, give presentations to clients or superiors.

  • Course 1: Data Munging and Exploratory Data Analysis (3 credit hours)
    • Exploratory data analysis in the context of knowledge discovery, including the use of data visualization software. Practical skills for working with large datasets including common methods for gathering, organizing, and reshaping structured and unstructured data, with an introduction to related methods in R, and some examples in Python
  • Course 2: Applied Statistics I (3 credit hours)
    • A statistics course focusing on descriptive and inferential statistics, with topics on linear regression, confidence intervals and hypotheses testing, including probability theory and modern approaches such as resampling, with all methods illustrated in R and a focus on methods relevant for data science using industrial datasets.
  • Course 3: Data Visualization (3 credit hours)
    • A project-centered introduction to the visual display of quantitative information for both knowledge discovery and the communication of results. Students develop, over the course of the semester, a visual application in their interest with data collected from an industrial application or project.

Ty Ryba, Ph.D. Associate Professor of Bioinformatics | Prof. Ryba is a computational biologist with interests in epigenetic regulation and applying computational methods to study the biology of human disease. His research examines relationships between genome structure and function, with a focus on large-scale domain regulation and misregulation in respiratory disorders and pediatric leukemia.

Andrey Skripnikov, Ph.D. Assistant Professor of Applied Statistics | Prof. Skripnikov teaches Applied Statistics, Statistical Computing and Machine Learning in graduate and undergraduate programs and conducts research on high-dimensional data and time series analysis. His areas of interest include gene expression data, studying brain activity, econometric time series and sports data.

Bernhard Klingenberg, Ph.D. Professor of Statistics | Prof. Klingenberg teaches Advanced Applied Statistics and Data Visualization at New College. His research interests include biostatistics, statistical modeling, and categorical data analysis.

Participants who complete this graduate certificate should be able to:

  • demonstrate understanding of fundamental concepts in
    • data cleansing and exploratory data analysis
    • introductory statistical analysis
    • data visualization and its role and importance in data analysis
  • construct effective data visualizations and communicate findings
  • demonstrate proficiency in R programming language
  • demonstrate awareness and recognition of ethical issues in data analysis and visualization
  • operate effectively in a teamwork environment and communicate effectively with peers
  • communicate orally and in writing with audiences the results of data analysis or visualization

  • Bachelor’s degree (or be on track to earn Bachelor’s degree before classes start)
  • 1-paragraph statement of interest/purpose

Machine Learning with Python

Understand what's "under the hood" for most advanced AI and machine learning techniques, this certificate is great for professionals and individuals who would like to build models for consumer behavior and segmentation, analyze textual data, build computer vision models, or build predictive models in sales, marketing, and e-commerce. This 3-course certificate program aims to equip its participants with knowledge and skills to develop and apply machine learning models to solve complex real-world problems. Starting with a course that introduces the fundamental principles in algorithms and optimization, it continues with models in machine learning and concludes with a course on advanced topics in computing including neural networks and deep learning. All implementations are done in Python.

  • Course 1: Algorithms for Data Science (3 credit hours)
    • Fundamentals of algorithms and measures of performance. Taught in Python, the course includes an exploration of efficient algorithms for sorting and retrieving data, graph algorithms and combinatorial optimization, dynamic programming, randomized algorithms, and approximation algorithms.
  • Course 2: Applied Machine Learning (3 credit hours)
    • Project-based course with a coverage of supervised and unsupervised learning and an emphasis on working with real industrial data. Bayesian analysis and other specific learning paradigms including regression, clustering, random forests, support vector machines, kernel methods, and neural networks.
  • Course 3: Advanced Applied Computing (3 credit hours)
    • Advanced topics in computing, including such topics as image processing and object detection, text mining, natural language processing, recurrent neural networks, reinforcement learning. Taught with a project-based focus using real industrial data in an applied business context.

Matt Lepinski, Ph.D. Associate Professor of Computer Science | Prof. Lepinski’s interests include cyber-security, computer networks, and software development. He worked for nine years at BBN Technologies focusing on transitioning security and privacy technologies from the academic literature to real-world systems. His current research focuses on security of widely-deployed Internet protocols, and the evolution of the public Internet.

Burcin Bozkaya, Ph.D. Professor of Data Science | Prof. Bozkaya serves as the Director of the MS program in Applied Data Science, and teaches courses on Machine Learning, Algorithms and Practical Data Science. His research interests include big data analytics, consumer behavior, predictive analytics in finance, insurance and telecom, and geographical information systems.

Participants who complete this graduate certificate should be able to:

  • demonstrate understanding of fundamental concepts in
    • algorithms and optimization
    • machine learning (ML) models and their applications
    • advanced computational models, including deep learning (DL), and their applications
  • build data science pipelines that include ML/DL models
  • demonstrate proficiency in Python programming language
  • demonstrate awareness and recognition of ethical issues in machine learning and artificial intelligence
  • operate effectively in a teamwork environment and communicate effectively with peers
  • communicate orally and in writing with audiences the results of data analysis or visualization

  • Bachelor’s degree (or be on track to earn Bachelor’s degree before classes start)
  • 1-paragraph statement of interest/purpose
  • Knowledge of or experience in introductory Python
  • Knowledge of Linear Algebra

Statistical Modeling

Develop a deep understanding of statistical concepts and models used in various industrial applications and programming skills in R while you work with real datasets and use statistical models to explain real-world phenomena. This certificate is great for professionals and individuals who work with statistical models in industries such as insurance, marketing, finance, healthcare, and epidemiology. This 3-course certificate program provides participants with a solid background in statistical modeling over a three-course sequence in statistics. Starting with fundamental concepts in descriptive and inferential statistics, it continues by exploring a variety of statistical models such as multivariate linear and logistic regression, time series modeling, survival analysis, and Bayesian statistics, among others.

  • Course 1: Applied Statistics I (3 credit hours)
    • A statistics course focusing on descriptive and inferential statistics, with topics on linear regression, confidence intervals and hypotheses testing, including probability theory and modern approaches such as resampling, with all methods illustrated in R and a focus on methods relevant for data science using industrial datasets.
  • Course 2: Applied Statistics II (3 credit hours)
    • A second course on statistical modeling, including multiple linear and logistic regression, and more generally, generalized linear models. Emphasis is placed on model formulation, building, assumptions, interpretations, predictions and assessments, with implementation carried out in R and a focus on methods and models relevant for data science using industrial datasets.
  • Course 3: Advanced Applied Statistics (3 credit hours)
    • The third course in the sequence continues with statistical modeling, with a mix of topics such as generalized additive models, models for longitudinal responses, time series models, survival analysis, statistical learning or Bayesian statistics, with a focus on models relevant for data science. Taught with a project-based focus using real industrial data in an applied business context.

Andrey Skripnikov, Ph.D. Assistant Professor of Applied Statistics | Prof. Skripnikov teaches Applied Statistics, Statistical Computing and Machine Learning in graduate and undergraduate programs and conducts research on high-dimensional data and time series analysis. His areas of interest include gene expression data, studying brain activity, econometric time series and sports data.

Bernhard Klingenberg, Ph.D. Professor of Statistics | Prof. Klingenberg teaches Advanced Applied Statistics and Data Visualization at New College. His research interests include biostatistics, statistical modeling, and categorical data analysis.

Participants who complete this graduate certificate should be able to:

  • demonstrate understanding of fundamental concepts in
    • descriptive and inferential statistics
    • statistical modeling and computational techniques in statistical analysis
    • various types of statistical models, including linear, logistic and generalized linear models, time series models, survival models and more
  • demonstrate proficiency in R programming language
  • demonstrate awareness and recognition of ethical issues in statistical modeling
  • operate effectively in a teamwork environment and communicate effectively with peers
  • communicate orally and in writing with audiences the results of data analysis or visualization

  • Bachelor’s degree (or be on track to earn Bachelor’s degree before classes start)
  • 1-paragraph statement of interest/purpose
  • knowledge of or experience in introductory R (unless the first course of Graduate Certificate in Data Analysis and Visualization with R is taken prior)

Distributed Computing

Understand modern database systems and distributed/cloud computing concepts. This certificate is great for professionals and individuals who work with large and complex datasets and databases who would like to move their systems towards cloud computing. This 3-course certificate program provides solid background as well as hands-on experience in distributed computing. Starting with a course that introduces the fundamental principles in algorithms and optimization, it continues with traditional and modern database systems including SQL and NoSQL databases. It concludes with a course on massively parallel datasets and database systems, and algorithms for parallel architectures. All implementations are done in Python.

  • Course 1: Algorithms for Data Science (3 credit hours)
    • Fundamentals of algorithms and measures of performance. Taught in Python, the course includes an exploration of efficient algorithms for sorting and retrieving data, graph algorithms and combinatorial optimization, dynamic programming, randomized algorithms, and approximation algorithms.
  • Course 2: Databases for Data Science (3 credit hours)
    • Fundamentals of traditional database design and management. Various types and comparison of databases including SQL databases (eg. Postgre, SQLite), NoSQL databases, column-oriented databases (eg. HBase) and document-oriented databases (eg. MongoDb). Consistency, availability, scalability, efficiency and performance in data retrieval and storage.
  • Course 3: Distributed Computing (3 credit hours)
    • Fundamentals concerning the design and maintenance of massively parallel data sets. Non-relational databases and their management. Algorithms for parallel architectures and associated software tools including the MapReduce/Hadoop framework and Spark.

Matt Lepinski, Ph.D. Associate Professor of Computer Science | Prof. Lepinski’s interests include cyber-security, computer networks, and software development. He worked for nine years at BBN Technologies focusing on transitioning security and privacy technologies from the academic literature to real-world systems. His current research focuses on security of widely-deployed Internet protocols, and the evolution of the public Internet.

Burcin Bozkaya, Ph.D. Professor of Data Science | Prof. Bozkaya serves as the Director of the MS program in Applied Data Science, and teaches courses on Machine Learning, Algorithms and Practical Data Science. His research interests include big data analytics, consumer behavior, predictive analytics in finance, insurance and telecom, and geographical information systems.

Participants who complete this graduate certificate should be able to:

  • demonstrate understanding of fundamental concepts in
    • algorithms and optimization
    • database systems; storage, retrieval and distribution of massive data sets
    • parallel and distributed computing 
  • demonstrate proficiency in Python programming language
  • operate effectively in a teamwork environment and communicate effectively with peers
  • communicate orally and in writing with audiences the results of data analysis or visualization

  • Bachelor’s degree (or be on track to earn Bachelor’s degree before classes start)
  • 1-paragraph statement of interest/purpose
  • Knowledge of or experience in introductory Python

Apply Now

Contact Assistant Director of Graduate Admissions Nikita Bagley to learn more and apply today! Fall 2022 Graduate program admission still open. Contact Us

Questions? Contact Us

Nikita Bagley

Assistant Director of Graduate Admissions
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