Data Analytics

Next class starts: 8/15/2024

View full schedule here.

Registration Eligibility: Adults & High School Seniors

Adults and high school seniors, ages 17 and older. Learn more.

Transfer Credit Opportunity

Credit hours for completing this program can be transferred to UVU and USU. Learn more.

Locations

Lehi Campus - Building B

61%

Graduation rate

100%

Job placement

$62,700

Avg. salary

The Data Analytics program prepares students for roles in technology, finance, healthcare, ecommerce, manufacturing and more. The competency-based curriculum provides training through real-world simulations, personalized mentoring, and practical coursework. Students develop skills in initiating data projects, sourcing information, transforming datasets, analyzing data, and presenting results which form an essential toolkit for the field of data analytics.

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Data Analytics prepares both experienced and inexperienced students with the necessary skills to become skilled data practitioners in business, manufacturing, management, and marketing environments. The self-paced, competency-based curriculum provides extensive hands-on training, text work, computer simulation, and one-on-one teacher to student training. This certificate introduces students to the knowledge, skills, abilities, and tools relevant to data analytics such as: initiating data projects, sourcing data, transforming data, analyzing data, and presenting data.

This program is eligible for Financial and Veterans Aid! Learn more

For more information, contact the Program Lead.

Adults and high school student seniors, ages 17 and older.

Registration for the 2024-2025 school year opens April 23, 2024 at 8:00 am online on the MTECH Student Portal and remains open until classes are full. It is recommended to create your MTECH Student Portal account in advance.

  • High school students should coordinate with their high school counselors prior to registration.
  • Students must satisfy the entrance requirements with the testing center. Math score of 5 and English 4.
  • Prospective students must be proficient in basic computer skills.

SECTIONCAMPUSROOM #START DATEEND DATESTART TIMEEND TIMEDAYS
Fall Core 2024Lehi Campus – Building B2188/15/20244/24/20257:30 AM10:30 AMMonday Hybrid, Tuesday-Friday
Fall Core 2024Lehi Campus – Building B2188/15/20244/24/202511:30 AM2:30 PMMonday Hybrid, Tuesday-Friday
Fall Core 2024Lehi Campus – Building B2188/15/20244/24/20256:00 PM9:00 PMMonday Hybrid, Tuesday-Friday
Fall Elective 2024Lehi Campus – Building B4/25/20256/30/2025Hybrid
Schedule Notes:This program incorporates hours of remote learning to be completed outside of classroom sessions.
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Total hours: 600

Course Descriptions
Course NameCourse HoursCourse CreditsTuition ($120/credit)Course Fees
Introduction to Data Analytics301$120.00$50.00
Spreadsheet Fundamentals602$240.00
SQL Fundamentals903$360.00
Python Fundamentals903$360.00
Data Visualization Fundamentals602$240.00
Data Visualization Fundamentals II301$120.00
Introduction to Machine Learning602$240.00
Data Analytics Capstone Project602$240.00
Electives (Students will work with their instructor to choose 120 hours/4 credits of electives)Course HoursCourse CreditsTuition ($120/credit)Course Fees
R Fundamentals602$240.00
Introduction to Database Design 602$240.00
Introduction to Semi-Conductor Manufacturing301$120.00
Statistical Process Control301$120.00
Data Analytics Capstone Project Elective602$240.00
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Upon registration, you will register and submit payment for the first course in the program. While not required, you may make a deposit, in person or online, for the full program upon registration to be held in your student account.

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Tuition/FeesCost
Tuition:$2,400.00
Application Fee: $0.00
Registration Fee:$40.00
Total Course Fees:$50.00
Required Materials:$0.00
Industry Exam Fee:$0.00
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Course-by-course cost breakdowns can be found on the “Courses” tab above.

Upon registration, you will register and submit payment for the first course in the program. Fees and materials may be required upfront, while some may be paid later throughout the program.

While not required, you may make a deposit, in person or online, for the full program upon registration to be held in your student account.

Required MaterialsQuantityNotes (ISBN numbers, etc)Cost
Internet connectivity for remote learning1 This program is a hybrid program and requires remote learning outside of in-person hours. Access to a computer and the internet is required. $0.00
Total Cost of Required Materials$0.00
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Optional MaterialsQuantityNotes (ISBN numbers, etc)Cost
Additional Monitor/Connection Cables for remote learning1 This program is a hybrid program and requires remote learning outside of in-person hours. Access to a computer and the internet is required. Access to a second monitor is recommended. $0.00
Total Cost of Optional Materials $0.00
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  • MTECH Program Certificate in Data Analytics

At MTECH, you will join an exclusive group of graduates who benefit from a commitment to excellence shared by everyone at the school. From instructors who deliver a relevant, industry-driven curriculum to academic counselors focused on ensuring you have the tools you need to succeed, we all share the same goal: your success as a student.

Program length
9 months
Total cost
$2,490
Lehi Campus - Building B
Program length
9 mos
Total cost
$2,490
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Course Descriptions

Introduction to Data Analytics
This course covers the basics of data analytics, utilizing spreadsheets, statistics, and exploratory data analysis. Students journey through the data analytics project cycle by defining the problem, preparing the information, analyzing the data, visualizing insights and presenting results. As they engage with this project, students organically develop problem-solving acumen and hone critical thinking skills essential for data analysis.
Spreadsheet Fundamentals
This course teaches essential spreadsheet skills for data analysis while integrating basic statistical principles. Students become proficient in spreadsheet functionalities such as data entry, formatting, and formula use, progressing to techniques like identifying outliers, employing descriptive statistics, examining relationships between variables, and harnessing the power of pivot tables for comprehensive data summarization and analysis using spreadsheets. Through practical exercises and real-world case studies, students learn to navigate spreadsheet software effectively, analyze data sets, and derive meaningful insights, culminating in the ability to apply statistical concepts within spreadsheet environments for informed decision-making and analysis.
SQL Fundamentals
This course provides a comprehensive understanding of SQL's foundational principles tailored for data analysis within relational databases. Students develop expertise in constructing SQL queries for data retrieval, focusing on pulling, filtering, aggregating, and joining datasets. Through hands-on projects, they'll apply these skills, learning to extract, filter, and manipulate data effectively, gaining a solid foundation in SQL's role within the realm of data analysis.
Python Fundamentals
This course teaches fundamental Python skills tailored for data analysis, encompassing Python's core syntax, data structures, and procedural programming techniques. Students perform data cleaning, data manipulation, and exploratory analysis using industry-standard libraries, fostering expertise in managing, analyzing, and visualizing data. Through practical projects, learners refine their abilities, gaining confidence to proficiently handle, analyze, and present data using Python. This course cultivates real-world application skills and sharpens proficiency in data project documentation, serving as a strong foundation for future data science endeavors.
Data Visualization Fundamentals
This course teaches students the core principles of data visualization essential for data analysis. With a focus on practical application using industry-standard tools, participants learn to translate complex datasets into compelling visual stories. Covering visualization design fundamentals, data cleansing, exploration of interactive dashboards, and consistent ethical considerations, learners cultivate aptitude essential for creating impactful visualizations. Through hands-on projects, individuals refine their skills, gaining the ability to extract insights effectively and drive informed decision-making across diverse industries.
Introduction to Machine Learning
This course teaches the fundamental principles and practical applications of machine learning for data analysis. Students study essential topics including data preprocessing, exploratory data analysis, and the core concepts of supervised and unsupervised learning. Participants perform regression, classification, and clustering techniques using real-world data. This lays the groundwork to start building machine learning pipelines and approaching data science tasks.
Data Analytics Capstone Project
This course teaches students to harness their data analytics skills by undertaking a comprehensive capstone project. Using various tools and techniques learned throughout the course of this program, students demonstrate their ability to identify business questions, collect, clean, and analyze data. The culmination of this project involves presenting their meaningful insights and findings through a visualization tool, class presentation and written report.

Electives

R Fundamentals
This course equips students with essential R programming skills for effective data analysis. Beginning with foundational R syntax and data structures, learners progress to topics such as data cleaning, data manipulation, and exploratory data analysis through relevant statistical packages. Engaging in hands-on projects, students become capable in data handling, analysis, and visualization techniques using an IDE.
Introduction to Database Design
This course familiarizes students with practical techniques in designing, constructing, and managing database systems. Through exploration of database design, development, and management, students explore strategies that optimize stored data, ensuring its integrity and maximizing its value. By learning these skills, students gain proficiency in creating, implementing, and maintaining databases crucial for efficient information systems.
Introduction to Semi-Conductor Manufacturing
An introduction to semiconductor manufacturing for students interested in semiconductor careers as well as those who wish to gain an overview of basic semiconductor processing. Semiconductor focused students gain basic knowledge of overall process flow and logic gate device functionality. Course material includes definition of semiconductor, n-type and p-type doping, geometries and units of measure, basic semiconductor manufacturing and process module overviews, clean room overview and protocols, automated material handling system (AMHS) overview.
Statistical Process Control
An introduction to statistical process control (SPC) for students interested in semiconductor careers as well as those who wish to gain an overview of basic SPC practices. Semiconductor focused students gain basic knowledge to maintain control of critical manufacturing processes. Course material includes overview and benefit, common cause vs. special cause variation, distributions and histograms, basic statistics, process capability, standard deviation and sigma, and control chart basics.
Data Analytics Capstone Project Elective
This course teaches students to harness their data analytics skills by undertaking a comprehensive capstone project. Using various tools and techniques learned throughout the course of this program, students demonstrate their ability to identify business questions, collect, clean, and analyze data. The culmination of this project involves presenting their meaningful insights and findings through a visualization tool, class presentation and written report.
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