Prerequisite online coursework includes a total of forty hours of work and over two hundred exercises. The Prework will prepare students to work with both R and Python as well as revisit basic concepts in linear algebra, calculus, and statistics.
- Mathematics/Statistics: Refresh your memory in linear algebra and statistics.
- Calculus: Exercise basic calculus techniques for data.
- Conda Installation: Kick off your Python journey with a beginner-friendly setting!
- Python: Designed for people who are new to programming.
- R: Learn R to process and analyze data.
DABC502 Data Science Toolkit
The Unix environment is widely used in the data science field. Being familiar with the
common tools is important in order to carry out further data analysis. This course enables students to communicate with the computers via the command line environment. It also
introduces the SQL database, a traditional database that has been widely used in the
enterprise setting, as well as GitHub, a file sharing platform generally used by programmers
for version control.
DABC506 Data Analytics with Python
This course introduces students to data analysis with the Python programming language.
Students learn to work with different data structures in Python and the most popular data
analytics and visualization packages such as numpy, scipy, pandas, matplotlib, and seaborn.
Ultimately, students will use effective Python code and packages to solve problems; extract,
transform, load, and analyze data to gain insights; and communicate the analyses, aided by
appropriate visualizations. Students are required to complete a project incorporating these
practices, culminating in a presentation of derived insights.
DABC511 Data Analytics with R
This course is designed to provide a comprehensive introduction to the R programming
language for data analysis. Students will learn to load, save, and otherwise wrangle data with
effective use of functions in R and relevant libraries, including those within the tidyverse
collection. Students will practice deriving insights from data using common statistical
techniques, including hypothesis testing and basic statistical modeling; effective visualization;
and other frequently used techniques within data analysis. Further, students will learn to
successfully communicate their insights, including creating reports with tools like knitr.
Students are required to complete a project demonstrating the ability to analyze data in R.
DABC516 Business Cases in Data Science
This course was designed to help students place data analytics and data science work in the
real-world context of business operations across industries. Students will be presented
various business cases in which datasets were explored to gain insights to guide and/or
enhance business operations. They will also be required to take given business cases and
conceptualize viable project approaches with defined objectives, selected tools and methods,
and expected deliverables
DABC519 Data Analytics Capstone Project
The capstone project is designed for students to employ the data analytics concepts, tools, and
methods they have learned in the bootcamp to solve a business operational problem with real
data sets from a real business entity. Students are presented data sets and potential problems
to solve. Students are then required to form project teams, develop a project proposal for
instructor review and approval, and execute the project. When the project is completed, each
project team is required to present the project findings and share the business insights
obtained from the research.