Statistical Data Analytics
What is it?
The Statistical Data Analytics Certificate of Achievement addresses industry’s continuing demands for professionals to test and build tools to make data-driven decisions to solve real-world problems as well as to plan for the future by valuable insights and knowledge from data using statistical techniques to prepare them to enter the professional fields in data science.
Benefits
This program not only prepares students to develop predictive models, evaluate the effectiveness of different interventions and strategies, and mitigate risks and anomalies in data, but also equips students with practical skills in data visualization and experience using programming tools such as Python and R. Students will acquire the skills to communicate complex statistical information to a variety of stakeholders effectively, and ultimately, to equip students for success in the data-driven world.
Potential Careers
Data Analyst, Data Scientist, Healthcare Data Analyst, Statistical Analyst, Research Scientist, Market Research Analyst, Data Consultant, Statistical Software Developer, Data Visualization Developer, Computer Systems Analyst, Computer Network Support Specialist, Computer Network Architect, Statistical Assistant, and professional careers in data science fields.
Statistical Data Analytics Certificate of Achievement
The Statistical Data Analytics Certificate of Achievement provides students with foundational knowledge of statistical theory, methodologies, and tools, enabling them to apply statistical techniques to diverse data analysis tasks. Students will demonstrate proficiency in using data science tools, like Python and R, for data processing, including data collection, cleaning, and transformation, to ensure data quality and reliability for analytical purposes.
Statistical Data Analytics Courses
4 Units (Lec 3 Hrs / Lab 2 Hrs)
Transfer Credit: UC
This course covers the foundational skills to solve mathematical problems with the use of programming languages, like Python and R. Topics include visualization, defining and manipulating functions, solving equations, calculating sequence and series, and programming techniques to solve a variety of mathematical models and applications including probability and statistics. This course is designed to prepare students for further coursework in data science, statistics, liberal arts, education, and related fields.
4 Units (Lec 3 Hrs / Lab 2 Hrs)
Transfer Credit: CSU (CSUGE Area B4), UC (IGETC Area 2A)
This course examines fundamental concepts that are the building blocks for data science work, include gathering and summarizing data (descriptive statistics) and relationships between variables, probability techniques, and distributions such as conditional probability and Bayes Theorem, and hypothesis testing to facilitate decision-making (inferential statistics). This course will analyze the pros and cons in decision theory through the exploration of sampling and control limits. Students will study correlation and regression analyses such as linear models for data science and multivariate regression and the application of technology for statistical analysis including the interpretation of the relevance of the statistical findings to data science. The course will examine applications using data from disciplines including engineering, business, natural and social sciences, and psychology. There will be a hands-on approach to statistical analysis using the tools (statistical software) of choice, such as Python and R.
4 Units (Lec 3 Hrs / Lab 2 Hrs)
Transfer Credit: CSU, UC
This course explores key areas of data science including question formulation, data collection and cleaning, exploratory analysis and visualization, statistical inference, predictive modeling, regression, classification and clustering, and applications of decision analytics to prepare students to understand real world problems and make optimal decisions.
4 Units (Lec 3 Hrs / Lab 2 Hrs)
Transfer Credit: CSU, UC; Advisory: MATH 260 OR MATH 260S.
Eligibility met to enroll in transfer-level math/statistics Prerequisite(s): MATH 229 This course introduces the basic concepts and principles of linear algebra and its applications in data science. Topics include vectors and matrices, systems of linear equations, determinants, eigenvalues and eigenvectors, linear transformations, and applications in machine learning. The course also includes hands-on exercises and projects using programming languages.
3 Units (Lec 2 Hrs / Lab 2 Hrs)
Transfer Credit: CSU, C-ID (ITIS 150)
This course is designed to provide students with a solid foundation in computer networking technology. It covers network cables, connectors & devices, network typologies & architecture, wired and wireless networking protocols & standards, OSI model, TCP/IP, wide area networks, network security & troubleshooting, and client/ server operating systems survey. The students become prepared for the CompTIA Network+ certification exam.
Mathematics Department
Franklin Hall 101 O
Kee Lam, Department Chair
Email Kee Lam
Phone: (323) 953-4000 Ext. 2810
Franklin Hall 103
Christine Fineberg, Department Secretary
Email Christine Fineberg
Phone: (323) 953-4000 Ext. 2810