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Marvin A. Pomerantz Business Library

Business Analytics & Information Systems Toolkit

BAIS Courses

List of skills and activities in courses taken by all BAIS students.

 

BAIS:3020 - Computational Thinking:

Coding or programming is considered a must-have job skill for business and other professionals. However, it is much more than that. Coding is a way of thinking about problems and solving them with the use of computers. Python is one of the most popular languages for writing general-purpose programs. In this class, we learn to solve problems by writing Python programs.

 

Students will learn:

  • Anaconda programming environment
  • Python programming language, including:
    • Variables and functions
    • Loops and logic
    • String manipulation
    • Structured types
    • Dictionaries
    • Exceptions
    • Object oriented programming
    • Plotting
    • Libraries

 

Students will be able to:

  • Write robust Python applications that utilize multiple functions
  • Solve problems using techniques such as:
    • exhaustive enumeration
    • approximation
    • bisection search
  • Scrape web pages for data
  • Perform text analytics on data
  • Produce visualizations
  • Process images

 

 

BAIS:3200 - Database Management:

Due to exponentially growing computation power and storage capacity, as well as the as increasingly digital nature of business operations, modern organizations collect and store a wealth of data about their interactions with various stakeholders, including customers, employees, shareholders, government, and suppliers. It is critical to manage data effectively so that it supports business strategy and provides competitive advantage. In this class, we learn the core concepts needed to design and utilize database management systems (DBMS) and Structured Query Language (SQL).

 

Students will learn:

  • Data modeling
  • Relational database design and normalization
  • Physical implementation of databases
  • SQL language & syntax
  • Database applications

 

Students will be able to:

  • Draw Entity Relationship and Enhanced Entity Relationship diagrams to represent data and business rules
  • Create logical models of databases, including normalization to third normal form
  • Construct relational database tables with SQL commands
  • Manipulate and load data into a database using database management software
  • Perform SQL queries, including:
    • Joins
    • Sub-queries
    • Compound queries
    • Conditional logic
  • Build a Web-based database application

 

 

BAIS:3050 - BAIS Professional Preparation:

This course is an introduction to the many career avenues available to a BAIS major and how to successfully search for for and landing one of those careers. Along the way students will be guided as they build their personal brand and prepare for their future internships and careers.

 

Students will learn:

  • To write a technical resume
  • To navigate technical interviews
  • To navigate case interviews
  • different careers in BAIS
  • Utilizing Career Service's tools and professionals
  • Life in BAIS from an industry panel
  • Negotiating the offer!

 

Students will be able to:

  • Better understand career opportunities within the business analytics and information systems field
  • Learn about the course curriculum - including the capstone course and additional educational options
  • Identify how to best manage your career search - from creating a technical resume to managing applications to networking and more
  • Learn about the industry from leading experts
  • Understand various types of interviews (behavioral, technical, case) and have the opportunity to practice your interview skills
  • Gain an understanding of the importance of hard and soft skills required in the workplace
  • Learn how to translate your knowledge and skills to match job posting requirements

 

 

BAIS:3250 - Data Wrangling:

"Data wrangling" encompasses collecting, cleaning, transforming, integrating, and describing data. It is estimated that data scientists spend up to 80% of their time on data wrangling tasks. In addition to being the most time-consuming step of most analytics workflows, wrangling is critical, as it impacts the outcome of all subsequent experiments/analyses. This course introduces essential R programming skills for data wrangling.

 

Students will learn:

  • R and R Studio
  • File Handing
  • Data Cleaning and Transformation
  • Web Scraping
  • APIs
  • Data Integration
  • Descriptive Analytics and Visualization

 

Students will be able to:

  • Build foundational programming skills, including variables, data types, data structures, functions, and subsetting
  • Read and write plain text, delimited, and nested file formats (XML, JSON), including file encoding
  • Learn methods to identify and correct common types of errors, handle missing values, and create derived features
  • Access data via APIs, Web scraping, and external database connections
  • Perform vertical and horizontal data integration
  • Use statistical methods (summary tables, hypothesis testing, regression) and visualizations to describe data
  • Apply techniques for time-series analysis and text analytics, including regular expressions and term frequency representation

 

 

BAIS:3500 - Data Mining:

In this course you will learn the basic concepts and techniques of data mining and knowledge discovery as applied to business problems. The focus will be on using recent data mining software to solve practical problems. Students will learn the process of turning raw data into intelligent decisions, and the algorithms that are commonly used to build predictive models and find relevant patterns in data.

 

Students will learn:

  • R programming
  • Knowledge discovery process
    • data mining problem formulation
    • data cleaning & transformation
    • data visualization
    • model evaluation & comparison
    • dimensionality reduction
  • Predictive modeling techniques
    • regression
    • decision trees
    • support vector machines
    • artificial neural networks
    • Ensemble Models
      • Random forest
      • Adaboost
      • Gradient boost
  • Clustering
    • hierarchical clustering
    • k-means
  • Model evaluation metrics

 

Students will be able to:

  • Understand the business prediction and model selection process
  • Discuss their theoretical appreciation for a variety of data mining methods
  • Apply practical knowledge of the uses and limitations of data mining
  • Build data mining models to solve practical problems using a software package
  • Evaluate, compare and select models using a variety of metrics

 

 

BAIS:4150 - Business Analytics and Information Systems Capstone:

The purpose of this course is to allow students to gain exposure to practical, real-world applications of the concepts they have encountered throughout the BAIS curriculum. Students will gain relevant analytics project experience by working in teams with a sponsor institution. Projects explore data-based topics of business interest to each sponsor. Significant time outside of class will be required to complete the deliverables of the project AND deliver value to project sponsors.

 

Students will learn:

  • Project management skills for data analytics projects
  • To clarify problems and document accurate problem statements
  • How to effectively provide weekly progress reports and status updates
  • Working within a professional team, including how to give constructive feedback to team members

 

Students will be able to:

  • Apply concepts from all Tippie College of Business core courses to help organizations understand their problems and potential impact of solutions
  • Organize, manage, and execute analytics projects to help organizations solve problems
  • Apply concepts from all BAIS courses to help solve those problems
  • Lead status meetings with their project sponsor
  • Implement a software solution to analyze data from the project sponsor
  • Summarize and present process, findings, and recommendation to project sponsor

List of skills and activities in BAIS elective courses.

 

BAIS:3800 - Optimization and Simulation Modeling: (offerings planned fall & spring semesters)

In Optimization and Simulation Modeling, students learn the art and science involved with translating a business problem stated in layperson’s terms into a mathematical model that can provide actionable insight for the decision-maker. Students learn to assess the business problem, acquire the appropriate data, and formulate a mathematical model that captures the essential elements of the decision-making problem. Using spreadsheet software, students will learn how to employ data to guide business decisions in areas such as operations, accounting, finance, marketing, economics, and human resources.

Students will:

  • Understand what factors make a decision difficult and identify the appropriate modeling technique (optimization or simulation)
  • Formulate optimization models by identifying the business objective and constraints and stating these in terms of decision variables
  • Understand the difference between linear and nonlinear optimization models and the implication of this difference
  • Model logical conditions using binary decision variables
  • Use data to model uncertainty via probability distributions
  • Construct Monte Carlo simulation models to quantify risk

 

 

BAIS:4280 - Cyber Security: (offerings planned spring semesters)

This elective course...

 

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Students will be able to:

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BAIS:3025 - Business Process Automation: (offerings planned fall semesters)

This elective course...

 

Students will learn:

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Students will be able to:

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BAIS:3140 - Information Visualization: (offerings planned fall semesters)

Information Visualization is the art and science of turning raw data into something that can be understood and utilized by both technical and non-technical audiences to make evidence-based decisions. We will explore the process of turning raw data into useful information that tells a story and then present that story in a way that technical and non-technical audiences can use it to make decisions. It may help to think of this course as “graphic design for data”.

 

Students will learn:

  • list item 1
  • The concept of Data Graphics
  • Data exploration and graphical representation
  • Visual design practices and techniques
  • Analytical design principles
  • Visualizing distributions
  • Revealing temporal change
  • Visualizing relationships
  • Visual storytelling

 

Students will be able to:

  • Present analytical results
    • Visually through reports and presentations
    • Interactively through digital formats
  • Utilize commercial tools such as Tableau and Excel to support visual storytelling
  • Create useful, easy to use, dashboards
  • Create compelling storyboards

 

 

BAIS:3100 - Accounting Information Systems: (offerings planned fall & spring semesters)

This elective course...

 

Students will learn:

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Students will be able to:

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BAIS:4220 - Advanced Database Management and Big Data: (offerings planned spring semesters)

Contemporary firms of all sizes store a wealth of data about their interactions with various stakeholders, such as customers, employees, shareholders, government, suppliers, and so on. Modern database management systems (DBMS) have long been used to store and manage massive data and are a crucial component of modern business intelligent systems. This course will build on skills learned in BAIS:3200 – Database Management to help students learn advanced querying and database management skills as well as introduce students to the top of "big data".

 

Students will learn:

  • Deeper knowledge of relational database principles and concepts
  • Advanced SQL and PL/SQL programming skills
  • Data quality, triggers and transaction management
  • Basic theories of big data, cloud computing, distributed databases, Hadoop, and HiveQL

 

Students will be able to:

  • Write advanced SQL queries (e.g., nested query) on Oracle database
  • Utilize Procedural Language extensions to the Structured Query Language (PL/SQL) code for data management and analysis on Oracle Database
  • Use stored procedures and triggers to interact with relational database management systems (RDBMS)
  • Understand the basic idea of the Hadoop system and MapReduce. Write basic HiveQL queries to process large data using Hive and Spark.

 

 

BAIS:3300 - Digital Project Management: (offerings planned spring semesters)

This elective course...

 

Students will learn:

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Students will be able to:

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BAIS:3400 - Cloud Computing: (offerings planned fall semesters)

This elective course...

 

Students will learn:

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Students will be able to:

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List of BAIS elective courses offered in other departments.

 

CS:1210 - Computer Science I:Fundamentals:

This elective course...

 

 

ECON:3355 - Economic and Business Forecasting:

This elective course...

 

 

MKTG:3102 - Marketing Analytics:

This elective course...