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DSO101 Basic Statistics Hours: Theory 30 / Laboratory 30 / Total 60 / Quarter Credits 4.5 / Outside Hours 15 / Prerequisites: None The Basic Statistics course will help students gain a fundamental understanding of statistical concepts that will be used throughout the Data Science program. Topics covered include probability, data types, common distributions, common descriptive statistics, and statistical inference.

DSO102 Statistical Programming in R Hours: Theory 30 / Laboratory 30 / Total 60 / Quarter Credits 4.5 / Outside Hours 15 / Prerequisites: None The Statistical Programming course teaches students how to load R and R Studio onto their PC. Students will then learn basic scripting commands, and will be introduced to a vast library of functions to perform various statistical analyses.

DSO103 Metrics and Data Processing Hours: Theory 30 / Laboratory 30 / Total 60 / Quarter Credits 4.5 / Outside Hours 15 / Prerequisites: None The Metrics and Data Processing course will prepare students to be able to create new metrics that directly answer or monitor business questions. This module will also teach the theory and practice of statistical process control. Upon completion of this module, students will be equipped to help businesses monitor their processes and know when a process is out-of-control, and needs to be fixed.

DSO104 Data Wrangling and Visualization Hours: Theory 30 / Laboratory 30 / Total 60 / Quarter Credits 4.5 / Outside Hours 15 / Prerequisites: DSO101, DSO108, & DSO109 The Data Visualization course is designed to help students understand that the heavy lifting in any analysis happens before the analytical procedure starts. Data wrangling is the process of changing the structure and format of raw data until the data are compatible with sometimes rigid requirements for analysis. Data wrangling also includes a quick sanity check of data quality. Data Visualization will give students an understanding and appreciation of the power in representing data graphically.

DSO105 Intermediate Statistics Hours: Theory 30 / Laboratory 30 / Total 60 / Quarter Credits 4.5 / Outside Hours 15 / Prerequisites: DSO101, DSO102, DSO108, & DSO109 The Intermediate Statistics course is designed to teach students about hypothesis testing under multiple scenarios. Students will be able to determine which hypothesis test to utilize and be able to perform that test. Students will also learn to identify and verify the data requirements for each hypothesis test.

DSO106 Machine Learning and Modeling Hours: Theory 30 / Laboratory 30 / Total 60 / Quarter Credits 4.5 / Outside Hours 15 / Prerequisites: DSO102, DSO108, & DSO109 The Machine Learning and Modeling course will introduce students to several commonly used machine learning methods. Students will learn how to determine the best methods for a given set of data, and how to use common software tools to utilize these methods.

DSO107 Introduction to Big Data Hours: Theory 30 / Laboratory 30 / Total 60 / Quarter Credits 4.5 / Outside Hours 15 / Prerequisites: DSO102, DSO104, & DSO109 The Introduction to Big Data course introduces students to Big Data on a conceptual level, and gives students exposure and practice with several skills and tools currently in use. These skills will be taught at a manageable level, and then scale up methods will be used to help students grasp the meaning and popularity of analyzing substantial amounts of data. Students will learn the foundational concepts of Big Data and will know how to move from Big Data basics to more business specific needs and requirements.

DSO108 Databases Hours: Theory 30 / Laboratory 30 / Total 60 / Quarter Credits 4.5 / Outside Hours 15 / Prerequisites: None This course is an introduction to working with, and designing databases. Students will develop a foundational knowledge of database concepts, theory, and an overview of the various implementations and architectures.

DSO109 Programming Foundations Hours: Theory 30 / Laboratory 30 / Total 60 / Quarter Credits 4.5 / Outside Hours 15 / Prerequisites: None This course will give students programming foundations in languages utilized in the industry. This course also provides a secure foundation upon which students can build on as they progress through the program.

DSO110 Group Project Hours: Theory 50 / Laboratory 110 / Total 160 / Quarter Credits 10.5 / Outside Hours 15/Prerequisites: Final Mod The Group Project course combines each part of the program into a group project for the student. Each student will work together as a team member for the group project, which includes daily scrum meetings to cover tasks and progress while working separately to complete them. The final group project is due at the end of the course.

Course Ordering
First 5 taken In Order
DS101-Basic-Statistics 1
DS102-Statistical-Programming-in-R 2
DS108-Databases 3
DS109-Python 4
DS104-Data-Wrangling-and-Visualization 5
next 4 variable based on semester (Wheel)
DS105-Intermediate-Statistics *
DS106-Machine-Learning *
DS103-Metrics-and-Data-Processing *
DS107-Big-Data *
Last Course
DS110-Final-Project 10

Data Science Admissions Questionaire

Behavioral Questions

  1. Do you have 20 hours available and a quiet place to focus on the data science curriculum each week? If So, how many hours will you be able to apply to this program?

  2. Are you willing to work independently and problem/puzzle solve?

  3. Are you willing to maintain communication with your mentor and commit to a weekly one-on-one meeting for 30 minutes per week?

  4. Are you willing to take responsibility for your success in this program: turn in lessons on time, meet deadlines, attend office hours as needed, and make adjustments to your schedule to prioritize the classes?

  5. In the past, how have you solved a problem you didn’t fully understand? What approach did you use?

Technical Questions

  1. What is the central limit theorem and why is it important?

  2. What are the two top programming languages utilized in Data Science? Name their strengths and weaknesses

  3. Solve the following problems:

    a. $ 3 ^ 3 = $

    b. $ 10 * 3 / 2 + 5 = $

    c. What is the square root of 9?

    d. $ (2 + 2) / 2 = $

    e. $ 9 + 10 ^ 2= $

    f. What percent of 16 is 4?

  4. What would the output be of this simple code?

a = 9
b = 21
print( a + b)

  1. There are two types of variables used in statistics, Categorical and Continuous, name two examples of each variable type.

  2. Please describe these variables and give an example of each of them

int
str
float
bool

Requirements

Do you have a viable computer to utilize in the Data Science Program? Check One.

  • Minimum: PC (Windows 10/11) or Mac (Big Sur or Monterey) laptop. 8GB ram, 512GB HD, Intel Core i5, AMD Ryzen 5, or Apple Intel or M1 Chipsets.

  • Recommended: PC (Windows 10/11) or Mac laptop(Big Sur or Monterey). 16GB ram, 1TB SSD, Intel Core i7, AMD Ryzen 7, or Apple M1/M1 Pro Chipsets.

  • Professionals: PC (Windows 10/11) or Mac(Big Sur or Monterey). 32-64 GB ram, 2-8TB SSD, Intel Core i9, AMD Ryzen 9/Threadripper, or Apple M1 Max Chipsets.




Signature of Applicant