Design, build, verify, and test predictive data models.
Modern databases can contain massive volumes of data. Within this data lies important information that can only be effectively analyzed using data mining. Data mining tools and techniques can be used to predict future trends and behaviors, allowing individuals and organizations to make proactive, knowledge-driven decisions. This expanded Data Mining for Advanced Analytics certificate provides individuals with the skills necessary to design, build, verify, and test predictive data models.
Newly updated with added data sets, a robust practicum course, a survey of popular data mining tools, and additional algorithms, this online certificate program equips students with the skills to make data-driven decisions in any industry. Students begin by learning foundational data analysis and machine learning techniques for model and knowledge creation. Then students take a deep-dive into the crucial step of cleaning, filtering, and preparing the data for mining and predictive or descriptive modeling.
Building upon the skills learned in the previous courses, students will then learn advanced models, machine learning algorithms, methods, and applications. In the practicum course, students will use real-life data sets from various industries to complete data mining projects, planning and executing all the steps of data preparation, analysis, learning and modeling, and identifying the predictive/descriptive model that produces the best evaluation scores. Electives allow students to learn further high-demand techniques, tools, and languages.
For more information about this program, please contact the program manager at email@example.com or 858-534-9358.
There will be a $60 fee upon acceptance into the program
Statistics can be used to draw conclusions about data and provides a foundation for more sophisticated data analysis techniques. Viewing questions about data from a statistical perspective allows data scientists to create more predictable algorithms to convert data effectively into knowledge. As such, it is essential for data analysts to have a strong understanding of both descriptive and inferential statistics.
In this course, students will gain a comprehensive introduction to the statistical theories and techniques necessary for successful data mining and analysis. Particular attention will be paid to topics critical to data analytics, such as descriptive and inferential statistics, probability, linear and multiple regression, hypothesis testing, Bayes Theorem, and principal component analysis. This course prepares students for subsequent Data Mining courses.
Software: Students will use MyStatLab and StatCrunch to complete assignments. Both are included with the purchase of the required course textbook and instructions for access will be provided by the instructor on the course start date.
Course typically offered: Online in Fall, Winter, Spring and Summer (every quarter)
Prerequisites: Strong understanding of college algebra required
Next steps: Upon completion of this course, considering taking Fundamentals of Data Mining to continue learning.
More Information: For more information about this course, please contact firstname.lastname@example.org.
Four courses required. Courses must be taken in the order listed.
An ever-increasing volume of research and industry data is being collected on a daily basis. Skilled data scientists are needed to process and filter the data, to detect new patterns or anomalies within the data, and gain deeper insight from the data.
This course provides students with a foundation in basic data mining, data analysis, and predictive modelling concepts and algorithms. Using practical exercises, students will learn data analysis and machine learning techniques for model and knowledge creation through a process of inference, model fitting, or learning from examples.
Software: WEKA is used for class assignments. There is no additional cost for this product.
Course typically offered: Online in Fall and Spring
Prerequisites: Statistics for Data Analytics or equivalent working knowledge is required. Linear Algebra for Machine Learning is also recommended, but not required.
Next Steps: Upon completion of this course, consider taking Data Preparation for Analytics to continue learning.
An essential, yet often under-emphasized step in the data mining process is data preparation. Habitually, people are more inclined to focus on knowledge discovery, but without sufficient preparation of the data, return on efforts will be limited. Without adequate skill and knowledge, preparing data for modeling can lead to less than adequate modeling results.
This class offers in-depth coverage of data preparation techniques and a step-by-step approach through a variety of tools while providing practical illustrations using real data sets. The hands-on exercises will anchor the learned concepts and offer valuable first-hand experience in cleaning, filtering, and preparing the data for mining and predictive or descriptive modeling. The goal is to transform the datasets so that their information content is best exposed to the mining tool.
Course typically offered: Online in Winter and Summer
Prerequisites: Fundamentals of Data Mining or equivalent experience required.
Next Steps: Upon completion of this course, consider taking Data Mining: Advanced Concepts and Algorithms.
As the amount of research and industry data being collected daily continues to grow, intelligent software tools are increasingly needed to process and filter the data, detect new patterns and similarities within it, and extract meaningful information from it. Data mining and predictive modeling offer a means of effective classification and analysis of large, complex, multi-dimensional data, leading to discovery of functional models, trends and patterns.
Building upon the skills learned in previous courses, this course covers advanced data mining, data analysis, and pattern recognition concepts and algorithms, as well as models and machine learning algorithms.
Prerequisites: Data Preparation for Analytics and Fundamentals of Data Mining or equivalent experience required.
Next Steps: Upon completion of this course, consider taking the Data Mining Practicum to continue learning.
Theoretical knowledge of data preparation, data mining, and machine learning techniques can be very useful. However, in order to be a successful data scientist, you must be able to put the theory into practice and draw useful information and insight from large datasets.
This challenging course is designed to give students hands-on practical experience data mining and predictive modeling. Students will go through several data mining projects, planning and executing all the steps of data preparation, analysis, learning and modeling, and identifying the predictive/descriptive model that produces the best evaluation scores. This course will ensure preparedness for complex real-life data mining tasks.
Prerequisites: Fundamentals of Data Mining, Data Preparation for Analytics, and Data Mining: Advanced Concepts and Algorithms required.
Next Steps: Upon completion of this course, consider taking additional courses in data science, data storage and management, or programming and scripting languages to continue building your skills.
Statistical computing is employed within a diverse range of industries. In recent years, an open source project, R, has emerged as the preeminent statistical computing platform. With its unsurpassed library of freely available packages, R is capable of addressing almost every statistical inference problem.
In this course, you will learn to write R programs that access data from multiple sources, generate output, manipulate different types of R objects that are based on programming objectives, perform character manipulation, generate statistical reports, create statistical graphics, and, most importantly, write flexible R functions by using different types of control structures
Software: R, a free software environment for statistical computing and graphics, is used for this course.
Course typically offered: Online in Fall, Winter, Spring, and Summer (every quarter)
Prerequisites: Knowledge of basic programming or Introduction to Programming is recommended.
Informatics is the study of structure, algorithms, behavior, and interactions of information systems. Its applications are powerful and broad, and include such fields as life sciences, data mining, business analytics, and social computing.
This hands-on course introduces the Python programming language, and is targeted toward students without prior programming experience who are interested in how informatics can be employed to provide solutions to complex, data intensive problems in a variety of scientific and business domains. After learning the core syntax and elements of the Python language, students will gain experience in the fundamentals of network programming, web services, databases and Structured Query Language (SQL), and data visualization.
Software: Students will use Python 2.7 and 3.X in this course. There is no additional cost to access this software.
Course typically offered: Online in Winter and Summer; In-class in Fall and Spring
Next Steps: Upon completion of this course, consider taking other courses in data science to continue learning.
With the vast amounts of unstructured data available on the web and stored in databases, and the promise it will provide insights unavailable in structured data, text mining has become an indispensable addition to traditional predictive analytics.
In this course, students will learn practical techniques for text extraction and text mining in a data mining context, including document clustering and classification, information retrieval, and the enhancement of structured data. Emphasis will be placed on the practical use of text mining in business. In addition, basic concepts of textual information such as tokenization, part-of-speech tagging, and disambiguation will be covered.
Software: Students will use R in this course. There is no additional cost for this product.
Prerequisites: Introduction to R Programming or equivalent knowledge required.
Knowledge of statistics and probability theory is required. A foundation in a programming language and advanced mathematics such as linear algebra is recommended.
You may enroll in the certificate program at any time. However, it is recommended that you enroll as soon as possible. The program curriculum may be updated at any time; if certificate requirements change, you must adhere to the curriculum at the time of your enrollment into the certificate.
From the 'Apply Now' button, login to your student account, complete the online application, and pay the application fee if applicable.
It is preferable that you create an account before proceeding if you have not already done so.
Candidates are encouraged to apply in the certificate program as early as possible to take advantage of program benefits.
See Certificate FAQs for more information.
Science & Technology. Call 858-534-9358 or Email: email@example.com
Although programs are open to all adult learners, UC San Diego Extension programs are designed to best serve college-prepared working professionals. Where program capacity is limited, applicants with this profile will receive preference for admission.
KAR Auction Services, Inc.
San Diego Regional Data Library
UC San Diego
Data Insight Discovery, Inc.
Knowledge of statistics is required prior to beginning required program courses. This prerequisite can be fulfilled by taking Statistics for Data Analytics, which is designed specifically to prepare students for this program. We also recommend that you have knowledge of probability theory and linear algebra, but this is not required.
Students who are working with statistics, probability theory, and/or linear algebra in the course of their current employment or who have completed a similar course previously may waive the Statistics for Data Analytics prerequisite, and begin the program directly with Fundamentals of Data Mining. If you are unsure if you have the necessary statistics skills, there is a short self-assessment quiz attached at the end of the program FAQ document (under "Related Documents" on the righ side of the top of this page) to help you gauge whether or not you should forego the prerequisite.
Most students complete the program in approximately a year and a half by taking one course per quarter for six consecutive quarters. You have up to five years to complete all requirements for the certificate.
The program costs approximately $4,690 including the certificate fee, course fees, and required textbooks. This is an estimate of the full cost of the program. This estimate may vary based on a variety of factors. All estimated costs are subject to change. Program fees are paid on a per-course basis, when you enroll in a course, and current fees and textbooks are listed on the individual course pages.
Yes! This program is designed for you to take it online in the convenience of your own home or office. Some courses may also have in-class options, but all program requirements can be completed online. For online courses, all assignments, tests, and quizzes can be completed online and submitted through Blackboard, our online learning platform.
If you have taken a course from an accredited university covering the learning objectives of a program course, you may be able to transfer your previous coursework to Extension. If you have not taken a course elsewhere, but already have the skills covered in a course, you may be able to substitute an alternate Extension course in its place. Please contact the program representative at firstname.lastname@example.org or (858) 534-9358 for more information.
Yes, the program is open to non-California residents, including non-US residents. The tuition is the same for all students. If you have questions about how enrolling in courses may or may not affect your visa status, please contact our International Department at email@example.com or (858) 534-6784.
You will find a downloadable program flyer and program FAQs under "Related Documents" on the right side of the top of this page. If you need further information, please contact the program representative at 858-534-9358 or firstname.lastname@example.org.
There will be a $60 fee upon
acceptance into the program
about Data Mining for Advanced Analytics
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