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Lasell University
Lasell University

2020 - 2021 Academic Catalog

Data Analytics

The Data Analytics minor consists of six courses.

Course Code Course Title Credits
Core Courses
INTC102 Introduction to Computer Science 3
INTC105 Data Warehouse & Business Intelligence 3
INTC202 Data Analytics  3
MATH208 Statistics 3
MATH209 Business Statistics 3
Choose 2 from the following:
INTC201X Analytics using SAS Visual Analytics 2
INTC204 How to Think Like a Data Scientist 3
MATH305X Advanced Statistics 3


Credit Requirements for minor: 18 credits

INTC102 - Introduction to Computer Science

This introduction to computer science, developed by Google and their university partners, emphasizes problem solving and data analysis skills along with computer programming skills. Using Python, students learn design, implementation, testing, and analysis of algorithms and programs. And within the context of programming, they will learn to formulate problems, think creatively about solutions, and express those solutions clearly and accurately. Problems will be chosen from real-world examples such as graphics, image processing, cryptography, data analysis, astronomy, video games, and environmental simulation. Students get instruction from a world-class computer science professor, delivered remotely through video and interactive media and attend class for collaborative team projects to solve real-life problems, similar to those a team at Google might face. As part of the course, students also hear from Google engineers about their careers in the tech industry and learn how they can prepare for similar careers. Prior programming experience is not a requirement for this course.

INTC105 - Data Warehouse & Business Intelligence

This course begins with an introduction of a data warehouse. Students will learn the concepts, tools, and application of Data Warehouse for business reporting and Online Analytical Processing. The course will also teach students how to create visualizations and dashboards and Descriptive Analytics. Core tools used in this course are Microsoft Excel and SAS Visual Analytics. Excel will be used to teach the basics of Visualizations – like Bar charts and Line charts. in order to increase student expertise in SAS Visual Analytics. SAS Visual Analytics will be used as a tool to introduce students to Data Warehousing and building basic visualizations. Students will also be exposed to Facts and Dimensions.

INTC202 - Data Analytics 

This course provides the conceptual and technical foundations of various aspects of Big Data Analytics. The purpose is to help students acquire foundation skills in Big Data – which can be used to further their specialization in a niche within Big Data. Upon completion of the course students should be able to:•Understand what is Big Data, Cloud Computing and NoSQL Databases.•Various components and architecture of Big Data Analytics.•Different types of Analytics: Text, Descriptive, Predictive and Prescriptive.•How Big Data Analytics is used in different contexts.•Using Analytics and Dashboards to present Actionable Insights.Prerequisite: MATH208

MATH208 - Statistics

This is an introductory course in descriptive and inferential statistics. Topics include: data analysis, and graphical methods of describing data, measures of central tendency and variability, probability, the normal distribution, sampling distributions, confidence intervals, hypothesis testing, correlation, and regression analysis. Prerequisites: MATH 106 with a grade of C or better or demonstrated competency through placement testing and ENG 102.

MATH209 - Business Statistics

This is an introductory course in descriptive and inferential statistics focused on applications in business. Topics include: data analysis, and graphical methods of describing data, measures of central tendency and variability, time-series analysis, trend and seasonality analysis, simple and multiple correlation and regression analysis, sales and cost forecasting, probability, expected monetary value, and the Normal distribution. Prerequisites: MATH 106 with a grade of C or better or demonstrated competency through placement testing and ENG 102. With permission of the instructor only.

INTC201X - Analytics using SAS Visual Analytics

This course focuses on building and enhancing skills from the Data Warehousing and Business Intelligence course. Students will expand their concepts of Business Intelligence, Visualizations, Dashboards, and Descriptive Analytics. The core tool used in this course is SAS Visual Analytics. Students will create visualizations, dashboards, and export reports to be able to present to the class. Prerequisite: INTC104X

INTC204 - How to Think Like a Data Scientist

How to Think Like a Data Scientist introduces students to the importance of gathering, cleaning, normalizing, visualizing and analyzing data to drive informed decision-making, no matter the field of study. Students will learn to use a combination of tools and techniques, including spreadsheets, SQL and Python to work on real-world datasets using a combination of procedural and basic machine learning algorithms. They will also learn to ask good, exploratory questions and develop metrics to come up with a well thought-out analysis. Presenting and discussing an analysis of datasets chosen by the students will be an important part of the course. Like INTC 102, this course will be "flipped," with content learned outside of class and classroom time focused on hands-on, collaborative projects. This course is delivered in partnership with Google. As part of the course, students also hear from Google engineers about their careers in the tech industry and learn how they can prepare for similar careers. Prerequisite: INTC 102.

MATH305X - Advanced Statistics

Quantitative statistical tools for modern data analysis are used across a range of disciplines and industries to guide organizational, societal and scientific advances. Using data sets from across a variety of fields, the focus will be on applications and analysis. Topics include two sample confidence intervals, Chi Square tests, multiple regression analysis, ANOVA, non- parametric tests, sampling, and simulation. Prerequisite: Math 208 or Math 209