Business Analytics Foundations

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Business analytics is an emerging inter-disciplinary area and has a fast growing job market. Our top goal is for students to succeed and have a great experience in our programs, and have a successful career in the evolving area of business analytics. The Foundations for Business Analytics is a 2 week hybrid bootcamp program intended for admitted/applied Master of Science Business Analytics students, future applicants, and possible career changers who are planning to start their academic education journey in business analytics, data science, big data, and digital transformation. Students will learn through face-to-face, in-classroom, and online teaching (asynchronous) the foundational knowledge needed to succeed and have a good experience in any graduate and undergraduate business analytics and data science degree program. 

Students who complete at least 10 modules will receive a certificate from the Milgard Center for Business Analytics


Note to International Students:
We are not able to issue I-20s for this program. If you are an F1 student interested in registering for the Business Analytics Foundations, please contact International Student Advisor, Annemarie Martin (amartin2@uw.edu), to see if you are eligible.

This foundations bootcamp includes total 12 modules, and students are welcome to take one or more of these modules based on their current knowledge. Each module session includes three parts:

  1. Pre-module reading/assignment (asynchronous);
  2. 200 minutes face-to-face faculty instruction (synchronous);
  3. Self-paced after-module readings/assignments/tutorials (asynchronous)

Admitted/Applied Milgard Master of Science in Business Analytics (MSBA) students vs. not-admitted students cost per module:

  • Admitted/Applied Students: $125 ea., or $1,200 for 12 modules.
  • Not-Admitted: $175 ea., or $1,800 for 12 modules

Explore module offerings in the catalog below.

Download Workshop Schedule

Terms and Conditions

Cancellation and Refund Policy

If you cannot attend the courses you have registered for, you may either transfer your registration to another attendee or request a refund. 1. TRANSFERS Send an email to mcba@uw.edu and provide the replacement attendee’s first and last names, and valid email address. Upon receipt, the Center will send registration instructions to the new attendee and remove the cancelled attendee’s name from the event list. The courtesy of a minimum of one week’s notice is requested to ensure accuracy of event materials. 2. REFUNDS Requests must be made in writing via email to mcba@uw.edu no later than 2 weeks prior to the event. The processing fee will be withheld from the original registration amount paid.

Terms and Conditions

The Milgard School of Business reserves the right to cancel an event for any circumstances beyond its control. The total amount of liability that the University will be limited to is a refund of the event registration fee. Submission of an event registration form acknowledges acceptance of these provisions.

The University of Washington provides equal opportunity in education without regard to race, color, creed, religion, national origin, gender, sexual orientation, age, marital status, disability or status as a disabled veteran or Vietnam era veteran in accordance with University policy and applicable federal and state statutes and regulations.

The University of Washington is committed to providing access and reasonable accommodation in its services, programs, activities, events, education and employment for individuals with disabilities. To request disability accommodation, contact the Disability Support Services at least two weeks in advance of the event: 253-692-4522 (voice); 253-692-4413 (TTY); 253-692-4602 (fax); dssuwt@uw.edu.

Privacy Statement

The Federal Trade Commission recommends that organizations like ours use their "fair information practices" guidelines when establishing privacy policies, which we have done in creating the following policies:

· None of the personal information we receive from you on this site will be given, sold, or otherwise provided to a third party.

· If you would like to know what personal information we have about you in our records, please email mcba@uw.edu.

· We use every reasonable means to ensure that there is no access to this site by unauthorized users.

Exposure to R1

This course will expose students to the underlying concepts of “R”. Key topics include R – IDE, working with R packages, R fundamentals, vectors, factors, data frames and lists. After completing this module, participants will learn to work with R environment, install packages and work will libraries in R, basic of programming in R, Tidyverse, RMarkdown and Shiny.

Key Topics
  • R IDE
  • Working with packages
  • R fundamentals
  • Vectors, factors, data frames, lists
Specific Objectives After completing this module, participants should:
  • Learn to work with R environment
  • Learn to install packages and work will libraries in R
  • Learn the basic of programming in R
  • Tidyverse, RMarkdown and Shiny
  • Basics of R

Exposure to R2

This course will continue participants exposure to R focusing on conditionals, control flow, loops and functions. After completing this module, participants will be familiar with R data structures operations, Input and output data in R and python and using SQL for data access, functions, control statements and loops, and data visualization (generating graphs).

Key Topics
  • Conditionals
  • Control flow
  • Loops
  • functions
Specific Objectives After completing this module, participants should:
  • R data structures operations
  • Input and output data in R and python and using SQL for data access
  • Functions, Control statements and loops
  • Data visualization - Generating Graphs

Exposure to Python1

This course will expose students to the underlying concepts of Python. Key topics include Python basics, lists, functions and numpy. After completing this module, participants will learn the how to code in Python, work with key python data structures, learn to code functions in Python, and work with a python library.

Key Topics
  • Python Basics
  • Lists
  • Functions
  • numpy
Specific Objectives After completing this module, participants should:
  • Learn the how to code in Python
  • Work with key python data structures
  • Learn to code functions in Python
  • Work with a python library

Exposure to Python2

This course will continue participants exposure to Python. Key topics of this module will include Python libraries, Python logic, control flow and an introduction 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 graphlab create – a machine learning environment.

Key Topics
  • Python libraries
  • Python logic
  • Control flow
  • Introduction Machine learning in python
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 graphlab create – a machine learning environment

    Exposure to Math, Probability and Statistics 1

    This course 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.

    Key Topics
    • Statistical Data Visualization
    • Statistical Data Summarization
    Specific Objectives After completing this module, participants should be able to:
    • 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

    Exposure to Math, Probability and Statistics 2

    This course presents elementary topics in data analysis through regression.

    Key Topics
    • Probability
    • Confidence Intervals
    • Hypothesis Testing
    • Simple OLS Regression
    Specific Objectives After completing this module, participants should be able to:
    • 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

    Exposure to Business Foundations

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

    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
    Specific Objectives After completing this module, participants should be able to:
    • 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

    Exposure to Data, Database Management and Big Data

    This course presents the fundamentals of data, information and knowledge, data and database management, ETL, and big data.

    Key Topics
    • Data, information, knowledge
    • Database
    • Data Warehousing
    • Big Data
    • ETL
    • OLTP
    • OLAP
    Specific Objectives After completing this module, participants should be able to:
    • Understand the fundamentals of transactional (OLTP) and decision support systems (OLAP)
    • Learn relationship between data, information and knowledge
    • Learn fundamentals of data and database management
    • Learn how to collect to database, and how to access it
    • Learn how database, data warehousing, data marts, data lakes, big data and business intelligence are connected, and how they are used to support smart decision making

    Exposure to Information Technology/Systems

    This course will offer current and future developments in information technology, commoditization of business processes, architecture (software-as-a-service), infrastructure (infrastructure-as-a-service), cloud and mobile computing, transactional/operational systems vs. decision support systems.

    Key Topics
    • Service oriented enterprise, architecture
    • Information technology
    • Management information systems
    • Cloud computing
    Specific Objectives After completing this module, participants should be able to:
    • Understand the fundamentals of information systems in context of organizational strategies
    • Explore what information systems skills and knowledge are essential
    • Become familiar with the major trends in management information systems & infrastructures (Cloud, Big Data, ERPs, Mobile, IoT, CRM, SCM, cognitive computing/AI) and how these evolutions will affect workplaces and business strategies
    • Learn the major computer hardware, data storage, input and output technologies used

    Exposure to Data Modeling 

    This course will cover conceptual, logical and physical database modeling, entity relationship diagrams, relational database modeling, and dimensional database modeling.
     
    Key topics will include:
    • Data modeling
    • Entity relationship diagrams
    • Relational modeling
    • Dimensional modeling
    Specific Objectives After completing this module, participants should be able to:
    • 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

    Exposure to SQL 

    This course will cover structured query language (SQL)
     
    Key topics will include:
    • SQL
    • Data Input
    • Data Manipulation
    • Data Retrieval
    Specific Objectives After completing this module, participants should be able to:
    • 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

    Exposure to Excel for Data Analytics 

    This course will expose students to fundamental topics in Excel, such as formula creation, cell referencing, chart creating, and data manipulation. It will then explore applications of data analytics through linear programming and Solver.
     
    Key topics will include:
    • Data Entry and Manipulation in Excel
    • Chart Creation
    • Linear Programming
    Specific Objectives After completing this module, participants should be able to:
    • Enter Data into Excel in multiple ways
    • Create and interpret formulas
    • Create and interpret charts
    • Prepare Linear Programming problems
    • Interpret Linear Programming Problems