Business/Data Analytics Degree Preparation

Get prepared for a degree program in Business Analytics.

Cost

$159* per course (regular price)

$119* per course (discounted price for eligible students)

(*plus 2.5% registration fee)

Duration

8-10 weeks

Mode

Online, Zoom meetings

Level

Beginner

Get ready for your education journey!

This pathway is intended for individuals who are planning to start their academic education journey in business analytics or data analytics. Students will learn the foundational knowledge needed to succeed and to have a good experience in any graduate and undergraduate business/data analytics degree program. This pathway meets all of the pre-requisites necessary to be prepared for the UW Tacoma MSBA program. Upon completion of a minimum of 12 courses, students will be issued a shareable, verifiable, and printable digital badge in Foundations for Data & Analytics.

Courses

Beginner R1

R is a programming language that is used to store, analyze and visualize large amounts of data. R is used to create: 

  • statistical models that will reveal patterns 
  • mathematical models that lead to future projections 
  • visualizations to present a large amount of data in an easy-to-read format

Beginner R1 will expose students to the underlying concepts of “R”. Key topics include the RStudio IDE, R fundamentals, installing and using packages, working with vectors and data frames, and running basic models. After completing this module, participants will learn to work with R environment, conduct basic of programming in R, and use the Tidyverse for data manipulation.

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Specific Objectives After completing this module, participants should:

  • Learn to work with R using RStudio
  • Learn to install packages and work will libraries in R
  • Learn the basic of programming in R
  • Learn tidyverse approaches to data manipulation
Key Topics

  • RStudio IDE
  • Working with packages
  • Conditional statements
  • Vectors and data frames
  • Loading Data

Beginner R2

This course will continue participants exposure to R focusing on visualizing, summarizing, tidying data. After completing this module, participants will be familiar with generating graphs with ggplot2 and aggregating, joining, and reshaping data using dplyr and tidyr.

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Specific Objectives After completing this module, participants should:

  • Learn to summarize (aggregate) and reshape data
  • Learn to join data frames (tables)
  • Learn to create visualizations (graphs)

Key Topics

  • Visualization with ggplot2
  • Summarizing and joining with dplyr
  • Reshaping with tidyr
Beginner Python 1

This course will expose students to the underlying concepts of Python – used for a Machine learning and data analytics applications. Key topics include Python IDE (Jupyter Notebook), Python programming  basics, lists, functions and python libraries. After completing this module, participants will learn basic programming in Python, work with key Python data structures, learn to code functions in Python, and work with a Python library used for  working with arrays- NumPy.

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Specific Objectives After completing this module, participants should:

  • Learn how to code in Python in a Jupyter Notebook (IDE)
  • Work with key Python data structures
  • Learn to code functions in Python
  • Work with a Python library – NumPy

Key Topics

  • Jupyter Notebooks IDE
  • Python programming Basics -control structures
  • Lists
  • Functions
  • NumPy
Beginner Python 2

This course will continue participants’ exposure to Python. Key topics of this module will include additional Python libraries, Python logical statements, control flow, and an introduction to Machine learning in Python. After completing this module, participants will have learned which are fundamental Python libraries, how to code control flow and set up logical statements and use a machine learning environment.

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Specific Objectives After completing this module, participants should:

  • Learn which are fundamental Python libraries
  • Learn to code control flow and set up logical statements
  • Learn to use a machine learning environment

 

Key Topics

  • Common Python libraries
  • Python program logic and control flow
  • Introduction to Machine learning in Python
Beginner Math, Probability and Statistics 1

The basis of most data analytics is mathematical models. Beginner Math, Probability, and Statistics 1-2 modules will introduce you to basic mathematics that underlies the math concepts, probability functions, and statistical analysis that allow data analytics to work. It will teach you how people use, and misuse, statistics, how to create useful graphs with data, and introduce a simple model of data prediction. 

Beginner Math, Probability, and Statistics 1 will expose students to the underlying concepts of probability, statistics, and graphing necessary for simple data reports and data visualizations. It also serves as background introduction for future MSBA classes.

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Specific Objectives After completing this module, participants should:

  • Prepare and interpret visual data representations
  • Define and interpret different data summarization techniques
  • Prepare and interpret a data summarization report in R
  • Prepare and interpret data visualization in R

 

Key Topics

  • Statistical Data Visualization
  • Statistical Data Summarization
Beginner Math, Probability and Statistics 2

This course presents elementary topics in data analysis through regression.

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Specific Objectives After completing this module, participants should:

  • Explain the difference between different types of probability distributions
  • Calculate and interpret the confidence interval of different data types
  • Create and interpret different types of hypothesis tests
  • Prepare data for OLS regression analysis in R
  • Describe the different assumptions of OLS regression
  • Perform a simple OLS regression in R
  • Interpret and present the results of simple OLS regression

 

Key Topics

  • Probability
  • Confidence Intervals
  • Hypothesis Testing
  • Simple OLS Regression

Business Fundamentals for Analytics

For those without a business background, this module will provide you with a basic understanding of business concepts. It introduces you to how data analytics is used by marketing, management, and finance professionals. 

This course provides students a grounding in the basic terminology and theories of business, including accounting, finance, marketing, and operations management.

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Specific Objectives After completing this module, participants should:

  • Be able to understand the basic economic theories of supply and demand and their applications to business
  • Describe different types of marketing theories and their translation to strategy
  • Understand the six fundamental principles of finance
  • Understand the purpose for accounting laws
  • Be able to interpret and use basic accounting tools such as income statements and balance sheets
  • Describe the role of management in business

 

Key Topics

  • Goals of Business
  • Economics – Supply and Demand
  • Marketing – Branding and market strategies
  • Financial Accounting – Income statement, balance sheet, statement of cash flows
  • Finance – Time value of money, risk-return tradeoff, cost of capital, IRR
  • Management – Theories and applications

Beginner Data, Big Data Management & Cloud Computing

This course will give you an introductory level overview of data infrastructure including relational and non-relational databases. This module also focuses on how big data is different than traditional data workloads and how cloud solutions address the challenges of Big Data. Additionally, the module includes a hands-on activity that will walk students through the process of deploying a cloud database and accessing and importing data.

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Specific Objectives After completing this module, participants should:

  • Understand the fundamentals of transactional (OLTP) and decision support systems (OLAP)
  • Learn the fundamentals of data and database management
  • Learn how to create a database, and how to access it
  • Learn how database, data warehousing, data lakes, big data, and business intelligence are connected, and how they are used to support smart decision making
  • Learn the fundamentals of Cloud Technology and cloud services used for Data Management.

 

Key Topics

  • Database
  • Data Warehousing
  • Big Data
  • ETL
  • OLTP vs OLAP
  • On-premise vs cloud solutions
  • Data Management in the cloud
Beginner Data Modeling

Data modeling is a tool to conceptually describe the data and relationships between data. We will learn how to create simple diagrams to describe and link complex data, showing the types of data used and how it can be organized.  

This course will cover conceptual, logical and physical database modeling, entity relationship diagrams, relational database modeling, and dimensional database modeling.

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Specific Objectives After completing this module, participants should:

  • Understand what is data modeling
  • Create a conceptual, logical and physical database model
  • Understand fundamentals of relational vs. dimensional modeling, and how they are being used
  • Hands-on exercise to design a data model with various data modeling tools

 

Key Topics

  • Data modeling
  • Entity relationship diagrams
  • Relational modeling
  • Dimensional modeling

Beginner SQL 1

SQL, or structured query language, is a programming language used to query, organize, and interact with the data stored in databases. SQL is widely used in all major relational database management systems (RDBMS) software tools and is utilized across industries. In this course, you will learn how to use SQL for data input, manipulation, and retrieval.

This course will cover structured query language (SQL).

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Specific Objectives After completing this module, participants should:

  • Learn fundamentals of structured query language
  • Learn how to obtain information from a database with SQL
  • Update database content with SQL and transaction handling
  • Retrieve data with filter conditions and from multiple tables using various types of join

 

Key Topics

  • SQL
  • Data Input
  • Data Manipulation
  • Data Retrieval
Beginner Excel for Data Analytics 1

This beginner class will teach you how to create formulas, reference cells, create charts and manipulate data. Additionally, you will be introduced to a powerful mathematical modeling method that allows you to identify optimal choices with the click of a button. 

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Specific Objectives After completing this module, participants should:

  • Enter Data into Excel in multiple ways
  • Create and interpret formulas
  • Create and interpret charts
  • Prepare Linear Programming problems
  • Interpret Linear Programming Problems

 

Key Topics

  • Data Entry and Manipulation in Excel
  • Chart Creation
  • Linear Programming

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