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School of Health Sciences

B.S. in Data Analytics

Data Analytics

Data Analytics

The B.S. in Data Analytics major prepares students for careers in sports analytics, big data, business and data analysis, and more.

The Data Analysis degree program prepares students to understand data and create actionable insights. Organizations across industries have the increasing need for skilled analysts who can collect, analyze, and produce actionable insights for their teams to generate more revenue, increase customer base, and develop products.


Program Features

The Data Analytics degree program features many extracurricular opportunities to give students leadership/team experience and portfolio material. 

  • The Data Analytics major allows students to earn certifications and badges in: Google Analytics and SAS Visual Analytics.
  • Students learn techniques and tools to set-up, retrieve, aggregate, and process large data sets - from a traditional data store to Big Data insights.
  • Learn methods of data analysis and visualization using software to create charts, dashboards and reports.
  • Students complete a minimum of one industry-relevant internship.

What You'll Learn

From your first day, you’ll take courses in your major and advance towards graduation with a yearly plan. Not sure what classes to take? We’ll help you create the perfect plan. 

For a complete list of courses and learning outcomes, view the Academic Catalog >>


Accelerated Master's Program

Save time and money — earn your graduate degree in just 1 year with the Accelerated Master's program. Learn more and how to apply >>

Undergraduate alumni return to Lasell for second (or third!) degrees 
Read their stories >>

Career Success in the Data Analytics Industry

Data Analytics majors are prepared for careers in a broad array of industries including sports analytics, big data engineering, and business analysis.

Our students have gone on to have careers as:

  • Citizen Data Scientist
  • Data Analyst
  • Business Analyst
  • Big Data Engineer
  • Data Scientist

 

 

 

 

 

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BUSS105 - Excel for Business

This course introduces students to basic Microsoft Excel skills. Excel is an electronic spreadsheet program used for storing, organizing and manipulating data. It is critical to the business world today as the volume of data generated has exploded. This introductory course will provide students with information and skills needed to create basic workbooks and worksheets, create simple formulas, copy and move data, format data and cells, work in large spreadsheets and with data series, create pivot tables, and more. As part of this course, all students will have the opportunity to become certified in Microsoft Excel through the professional certification called Microsoft Office Specialist: Excel 2016 – Core Data Analysis, Manipulation, and Presentation. The certification also comes with an electronic badge. Students are also introduced to Income Statements, Balance Sheets, Statement of Cash Flows, Ratios, and the Basic Accounting Cycle.

DSCI105 - Data Warehouse and Business Intelligence

This course begins with the introduction of a data warehouse. Students will learn the concepts, tools and application of data warehouse for business reporting and online analytical processing. Students will also learn how to create visualizations and dashboards, and descriptive analytics. The material builds from the concepts learned in basic statistics courses. Core tools used in this course include Microsoft Excel, and SAS Visual Analytics. Excel will be used to teach the basics of visualizations – like bar charts, line charts etc. in order to ramp-up the students’ expertise into 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.

DSCI201 - 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: DSCI105.

DSCI202 - Business Analytics

This course provides the conceptual and technical foundations of various aspects of Data Analytics. The purpose is to prepare students with foundation skills in Big Data, a skill widely needed and valued across the business world. The course will expose students to the data analytics practices executed in the business world and explores key areas of the analytical process, how data is created, stored, accessed, and how organizations work with data and creates the environment in which analytics can flourish. This course will provide students with a strong foundation in all the areas that support analytics and will help them to better position themselves for success within any organization. This course provides the conceptual and technical foundations of various aspects of Big Data Analytics, including cloud computing, NoSQL Databases, predictive and prescriptive analytics. Prerequisite: MATH208 or MATH209.

DSCI203 - OS + Algorithms

An introduction to the theory and structure of modern operating systems, including hardware abstraction, process management, memory management, system performance, and security. Specific attention to multi-threaded processing, semaphores, locking and inter-process communication. Prerequisites: DSCI102 and DSCI103 (formerly INTC102/INTC103).

DSCI204 - How to Think Like a Data Scientist

This course 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 data sets 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 data sets chosen by the students will be an important part of the course. Prerequisites: DSCI102 and DSIC103 (formerly INTC102/103).

DSCI301 - Big 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 Big Data, Cloud Computing and NoSQL Databases are; Various components and architecture of Big Data Analytics; Different types of Analytics including Text, Descriptive, Predictive and Prescriptive; and how Big Data Analytics is used in different contexts. Students should also be able to use Analytics and Dashboards to present actionable Insights. This course will use SAS Visual Analytics as one of the tools for illustrating the volume of Big Data, and how it can be used to harness actionable insights. Students will use datasets to create visualizations and actionable insights. Prerequisites: DSCI102, DSCI105 and DSCI201.

DSCI306 - Advanced Python Programming

This course provides students with the opportunity to write useful Python applications in the ETL, web, and data analysis domains and knowledge of industry-standard tools and techniques for working within a development team. The course goes further into Python’s powerful advanced features, such as user-defined classes, object-oriented design, decorators, and generators. Students will learn to employ the most widely used algorithms and libraries to solve common problems in the field and gain a working familiarity with statistical analysis and visualization using Pandas, NumPy, and Matplotlib. Query and parse HTML, XML, and JSON are used. Students will learn to apply industry-standard tools and techniques for working within a development team, such as Git for versioning and code review. The course concludes with a discussion of common interview questions and pathways for gaining experience and eventually securing a position in the field. Prerequisites: DSCI102, DSCI202 and DSCI204.

DSCI402 - Analytics with R

This course introduces students to R, a widely used statistical programming language. Students will learn to manipulate data objects, produce graphics, analyze data using common statistical methods, and generate reproducible statistical reports. They will also gain experience in applying these acquired skills in various public policy areas.Prerequisites: DSCI102, DSCI202 & DSCI204

MATH205 - Calculus I

This course is an introduction to limits, continuity, and methods of differentiation. Application to problems in business management and physical science is emphasized. Prerequisite: MATH 203 with a grade of C or better or demonstrated competency through placement testing. Restrictions: not open to students who have completed MATH 206, or any 300 level mathematics courses.