About Data Science
The certificate expands the data Analytics Certificate of Achievement by providing students with advanced skills in Data Science tools such as Machine Learning, and Deep Learning. The Data Science certificate presents a more robust preparation for work within data Science teams at various organizations. Students learn to use Cloud-based tools to manage, wrangle, and analyze data, and to use Python libraries to draw statistical references, which solve real-world problems.
Program Learning Outcomes:
- Students will be able to utilize cloud database systems on AWS, as well as Oracle, and Access in order to manage databases, and respond dynamically to information and computing technology workloads.
- Student will be able to wrangle data using Excel and Python libraries in order to communicate summaries of data using multiple representations including graphs, tables, numerical summaries, and words.
- Students will be able to identify statistical methods, and models that represents, and describe real-world data, and draw inferences that solve real-world problems using Python libraries.
- Students will be able to use advanced Python libraries, and AWS in order to develop supervised, and unsupervised learning models, and to recommend data-driven actionable insights for business, and marketing problems.
Programs
Certificate of Achievement Data Analytics Roadmap
The certificate program provides students with a basic foundation in the fields of data analytics, and prepares them for a potential career in Data Science. The program is designed to prepare students for entry-level jobs such as Business Analytics Specialist, Data Analyst, Data Visualization Developer, Operations Research Assistant, and Market Research Assistant.? Students learn to use Cloud-based tools to manage, wrangle, and analyze data, and to use Python libraries to draw statistical inferences, which solve real-world problems.
Program Learning Outcomes:
- Students will be able to utilize cloud database systems on AWS, as well as Oracle, and Access in order to manage databases, and respond dynamically to information and computing technology workloads.
- Students will be able to wrangle data using Excel, and Python libraries in order to communicate summaries of data using multiple representations including graphs, tables, numerical summaries, and words.
Prerequisite: None
Lecture: 2 hrs, Lab: 2 hrs
Analytics and data-mining using Excel spreadsheets and Access databases. The course includes: using databases, spreadsheets and other systems to gather information, research, analyze, and interpret complex data, loan amortization schedules, automatic update of spreadsheets with data downloaded from other sources, database management and reporting, and automating processes with VBA. Recommended for Business Majors.
Prerequisite: None
Lecture: 2 hrs, Lab: 2 hrs
Students will learn topics of the Python language such as data types, variables, control structures, Python Objects, standard and advanced mathematical libraries, tool-chain use and Python Frameworks, user-defined classes and abstract collections, single and multidimensional arrays, Python lists, tuples, collections, and dictionaries.
Prerequisite: None
Corequisite: CO SCI 401 or CS 101.
Lecture: 2 hrs, Lab: 2 hrs
This course introduces the fundamentals of cloud computing including the different cloud computing models: Infrastructure as a Service; Platform as a Service; and Software as a Service on the Amazon Web Services (AWS) platform. Review of the basic concepts of server, networking, storage and virtualization is covered. Industry trends of computing, storage and application migration to cloud computing is covered. Advantages and disadvantages of cloud computing are examined. Cloud careers and industry demand for cloud computing skills are listed.
Prerequisite: None.
Advisories: CO SCI 430 or CIS 124
Lecture: 2 hrs, Lab: 2 hrs
The student learns the concepts of both relational and object relational databases and the SQL language. Data server technology, creating and maintaining database objects, as well as storing, retrieving and manipulating data are also covered.
Prerequisite: CIS 192
Advisory: CO SCI 434 or CIS 219.
Lecture: 2 hrs, Lab: 2 hrs
This course introduces AWS' data storage services. It covers introduction of AWS database technologies and AWS block and object-based storage services. Students learn the principles of database design and management, AWS SQL and NoSQL database technologies. Students use principles of block and object-based storage options. They will study various use case scenario for AWS data storage services. The hands-on labs will allow them to apply the knowledge acquired.
Prerequisites: None
Lecture: 2 hrs, Lab: 2 hrs
An introduction to the foundation of Data Science from three perspectives: inferential thinking, computational thinking, and real-world relevance. The course introduces the Python programming language and the Foundations of Statistics with hands-on analysis of real-world datasets. The course is foundational to any discipline, industry, or career which makes data-based decisions.
Prerequisite: CIS 192
Lecture: 2 hrs, Lab: 2 hrs
This course introduces AWS (Amazon Web Services) computing related services. Students will learn about the core computing services offered by AWS. The computing services will follow the computing models: Infrastructure as a Service, Platform as a Service, Function as a Service or Micro-services and server less computing (Lambda functions). Students will set up and manage computing services, configure auto scaling and load balancing. Students will learn to code auto deployment scripts to automate the management of AWS infrastructure.
Prerequisites: CS 119
Lecture: 2 hrs, Lab: 2 hrs
Students will build on basic Python programming concepts by learning additional features such as Iterators, List Comprehension, Generators, Packages & Modules. Data Science related Python libraries like Numpy, Pandas, Matplotlib, Seaborn, and Scikit-learn will be covered. The Jupyter Notebook will be used for interactive visualization and sharing of results.
Certificate of Achievement Data Science Roadmap
Includes all the courses above and
Prerequisites: CS 121
Lecture: 2 hrs, Lab: 2 hrs
Students will gain knowledge of data analytics, SQL queries, data views, data visualizations, and applied predictive analysis. AWS will be employed in developing data-driven actionable insights to business divisions. Students will be eligible for entry-level roles in data analysis/engineering.
Prerequisites: CS 121
Lecture: 2 hrs, Lab: 2 hrs
Students will receive training on the four-machine learning (ML) models: Supervised, Unsupervised, Reinforcement & GAN models. They will analyze a given scenario and determine which model should be applied. Students will use existing services as well as explore custom solutions to generate explainable predictions given a dataset.
Contact Us
Office Hours and Location
Lab Hours: 8:00 - 4:00 / Monday-Friday
Location: INST 2009