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University of California, Irvine

Data Science

University of California, Irvine
Data Science

This is an educational program open to anyone interested in learning more about data science. You do not need to be a resident of the United States or an existing UCI student to enrol in it, and you do you not need to be of typical college age. It is intended to equip aspiring and new professionals, as well as those switching industries, with the skills they need to succeed in the workplace.

The Data Science Certificate from the University of California, Irvine Division of Continuing Education acts as an excellent tool for professionals at every level of data experience, training them in an array of data skills and techniques. It is a study-from-home program that lasts 9-15 months, split into 2 required courses and 9 units in elective courses that make up the rest of the students course load.

REQUIRED COURSES:

Introduction to Data Science (3.00 Units)

  • Learn the basic design, management and manipulation tools used by data scientists. These building-block skills include SQL, NoSQL, data analysis and statistical modeling, which are all crucial to gaining value from data sets.

Data Exploration, Analytics and Visualization (3.00 Units)

  • This course will give students the knowledge to get the best out of data sets once they have been collected and structured. Students will learn how to pull the best insights out of large data sets, as well as learning how to use the most common industry processes to build useful predictive models. Related topics such as Graph Analytics and data privacy will also be covered.

ELECTIVE COURSES:

Effective Data Preparation (2.00 Units)

  • Students on this course will learn how to effectively extract stored data elements and use this in a professional environment. This course will also impart the understanding required to effectively process and extract data.

Introduction to Python Programming (2.00 Units)

  • This course will give a beginner introduction to the Python programming language and will teach students how to use Jupyter, a popular tool for working with Python.

Python for Data Analysis (1.50 Units)

  • Students with some experience with Python can use this course to learn how to utilize these skills to import and analyze data more effectively. This course will also give an introduction to eclipse, an important development environment for Python and many other coding languages.

Tools and Techniques for Machine Learning (2.00 Units)

  • This course will teach students how machine learning is used in data and its benefits. Students will be exposed to both the theory behind machine learning and deep learning, how the two differ and practical applications of both.

R Programming (2.00 Units)

  • R is a scripting language used in many areas of data processing and prediction. This course focuses on teaching R programming, and how it can be used to solve data problems. Relevant mathematical concepts will also be refreshed and taught as part of this course.

Business Intelligence & The Data Warehouse Development Process (2.50 Units)

  • Having an effective data warehouse solution can help save a company time and resources, as well as allowing decisions to be made with better information. This course will give students the skills to do just that, as well as giving an overview on careers working in business intelligence as well as the educational requirements for the field.

We have seen a great increase in esports and gaming jobs that work directly with data and data modeling, either as a prime job requirement or as an added benefit of the role. Companies from across the gaming and esports industry are increasingly interested and reliant on skilled and qualified data professionals to succeed as a business, so showing you have these skills can open up new avenues in your career.

Course Benefits:

  • Learn from experts in the industry how to utilize a combination of skills.
  • Gain proficiency with several key tools and processes used in data science, as well as having other uses outside of the field.
  • Learn how to understand and utilize data to gain the best insight available, through both modelling and analytic techniques.
  • Understand and develop data warehousing plans.