Certificates in Analytics
Program Overview
Data Analyics, IoT, and Artificial Intelligence are among the new technologies that are transforming society and industry today. Our certificates provide participants with the competencies that are in high demand across many industries. University of the Pacific is excited to offer graduate-level coursework that gives our participants significant advantages and competencies in the key areas of these new fields.Now, you can add a Pacific Certificate in one of the following topics to your resume:
Certificate Descriptions
Computing for Data Science Certificate
The Computing for Data Science Certificate course is intended for those who wish to enhance their careers by learning the core principles and techniques for designing and developing data analysis solutions. These are highly sought-after skills in today's data-centric world. The course is run outside of normal working hours through live video conferencing sessions so that working professionals can continue their education from anywhere in the world without disrupting work commitments.
Computing for Data Science Description:
Data Analytics is one of the hottest topics in industry today and the skills required to enter this exciting world are in high demand. This 10-week certificate course in Computing for Data Science will introduce participants to all of the foundational skills they require to begin their data science/analytics career. You will learn how to conduct data analysis using some of the most sought-after programming language skills, such as Python and R. By the end of the course, participants will be able to tackle typical data analysis problems that companies often face. The course focuses on how to apply this knowledge to real-world data sets, with an emphasis on statistical and machine learning analysis, and predictive modeling.
Course Learning Objectives: Upon completion of this course students will be able to
- How to manipulate data files within the Unix Shell
- Creating dynamic journals and reports, with embedded R and Python code using Jupyter Notebook
- The foundational concepts of data structures and the syntax of the R and Python programming languages
- All of the essential programming constructs
- How to use some of the most powerful, and most commonly used programming packages
- How to design and develop analytic solutions
Get practical, hands-on experience
You will not only learn the mathematical and programming skills necessary for a career in Data Analytics, but also practical hands-on experience in some of the latest tools used in industry today, including:
- The R and Python programming languages
- RStudio development environment
- Numpy
- Matplotlib
- Pandas
Prerequisites:
- Basic programming skills in some language (loops, conditionals, data types, arrays)
- Familiarity with descriptive statistics (mean, standard deviation, correlation)
- English Proficiency: All lectures, course materials and exams are conducted in English.
Click here for More Information and to Apply >
Machine Learning Certificate
The Machine Learning Certificate course is intended for those who wish to enhance their careers by learning the core principles and techniques for designing and developing Machine Learning solutions. This is a highly sought-after skill in today's data-centric world. The course is run outside of normal working hours through live video conferencing sessions, so that working professionals can continue their education from anywhere in the world without disrupting work commitments.
Machine Learning Description:
Knowledge of machine learning and the solutions that it offers is a highly sought-after skill in our increasingly data-driven world. During this 10-week course, participants will enhance their understanding of the theory and methods that allow machine learning to uncover patterns, recognize relationships and create models that can learn from large data sets. In addition to a solid grounding in the theoretical foundation of machine learning, participants will go on to learn how to build practical machine learning applications, including neural networks, using R programming.
Course Learning Objectives: Upon completion of this course students will be able to
- The statistical foundations and essential programming skills to design and develop Machine Learning solutions
- Supervised and unsupervised learning methods, and how to apply them
- Key techniques of classification, regression and clustering
- How to design and develop predictive models
- Practical case studies of Machine Learning in business and scientific environments
- Main neural network topologies
- How to build a Convolutional Neural Network (CNN)
Get practical, hands-on experience
You will not only learn the mathematical and programming skills necessary for a career in Machine Learning, but also practical hands-on experience in some of the latest tools used in industry today, including:
- R and Python programming languages
- RStudio development environment
- Jupyter Notebook
Prerequisites:
- Linear Algebra (matrix/vector ops, orthogonality, generalized inverse)
- Statistics (frequentist and Bayesian approaches, covariance, distributions)
- Introductory Calculus (derivatives)
- English Proficiency: All lectures, course materials and exams are conducted in English.
