MS Data Analytics

Leading experts and practicing industry professionals at Walsh College and Great Lakes design the curriculum of this MS in Data Analytics Program. It is specifically curated to cover a range of Data Science and Analytical skills, starting from the fundamentals and progressing to more complex, hands-on applications.

Year 1

During the first year of this Master’s in Data Analytics Program, learners will delve deep into the core principles and methodologies, such as Data Science and Business Analytics Techniques and Data Visualization, along with a hands-on, integrative Capstone Project. By the end of the first year, learners will have acquired a broad understanding and substantial skills in various aspects of Data Analytics.

Data Science Foundations

The first module of our Data Analytics MS Program is Data Science Foundations, where learners acquire the essential knowledge and skills to excel in Data Analytics. It covers a range of critical subjects such as Statistical Methods, Business Finance, Marketing and CRM, SQL Programming, and Python.
 

STATISTICAL METHODS FOR DATA SCIENCE 

Introduction to Data Science and fundamentals like Statistics, Probability, etc, which are key topics to understand Data Analytics. 

 

BUSINESS FINANCE 

This focuses on all the fundamentals of how business finance works in a company and is one of the most important topics in Data Analytics. 

 

MARKETING AND CRM 

This topic covers all the fundamentals of how a business handles its marketing strategies and  Customer Relationship Management (CRM). 

 

SQL PROGRAMMING 

Learn about SQL programming, including DBMS, Normalization, Joins, etc. 

 

PYTHON FOR DATA SCIENCE

Learn critical Python programming concepts for Data Science, such as NumPy, Pandas,  Matplotlib, Seaborn, etc.

 

Data Science Techniques

The second module in this Data Analytics Master's Program is Data Science Techniques, which builds upon the foundational knowledge acquired in the Foundations module, delving deeper into specialized techniques and methodologies used in advanced data analysis. It provides an expansive look at the analytical tools and techniques pivotal for extracting meaningful insights from data and creating viable data-driven solutions.

INFERENTIAL STATISTICS  

In this Data Science and Analytics course, we will discuss advanced statistical techniques such as  Variance, Regression, and Dimension Reduction. 

PREDICTIVE MODELING  

Predictive Modeling is a process to build a model that can predict data. This topic will focus on  various Predictive Modeling techniques used in Machine Learning. 

MACHINE LEARNING - I  

Learners will gain a thorough understanding of Supervised and Unsupervised Learning  techniques, Decision Trees, Neural Networks, etc. 

MACHINE LEARNING - 2  

The next module focuses on all the Machine Learning techniques used in Data Science and  Business Analytics. 

TIME SERIES FORECASTING  

This block will teach learners how to predict future values based on the previously experimented  values. 

SELF-PACED MODULES: OPTIMIZATION TECHNIQUES  

This module will cover all the optimization techniques used in Deep Learning. 

Domain Exposure

This module is meticulously designed to expose learners to various sectors and industries where Data Analytics plays a pivotal role, ensuring a broad understanding and multifaceted perspective on real-world applications of Data Analytics. Whether optimizing supply chains, predicting market trends, or understanding customer behaviors, learners will be prepared to leverage Data Analytics to drive impactful decisions across domains.

SELF-PACED MODULE: DEMYSTIFYING CHATGPT AND APPLICATIONS  

Gain an understanding of what ChatGPT is and how it works and delve into the implications of  ChatGPT for work, business, and education. Additionally, learn about prompt engineering and  how it can be used to fine-tune outputs for specific use cases. 

MARKETING AND RETAIL ANALYTICS  

This topic focuses on some basic terminologies used in marketing and delves into an  application of Retail Analytics. 

WEB AND SOCIAL MEDIA ANALYTICS  

This topic focuses on some basic terminologies used in Web and Social Media Analytics like  Google Analytics, Social Media Campaigns, Text Mining, etc. 

FINANCE AND RISK ANALYTICS  

This topic covers basic terms used in Finance & Risk Analytics like Credit Risk Models,  PD Models, Market Risk, etc. 

SUPPLY CHAIN & LOGISTICS ANALYTICS  

Learners will be exposed to basic terms used in Supply Chain and Logistics Analytics like RNN,  Inventory Management, Forecasting, etc.

Visualization and Insights

This module is crucial in shaping our learners’ ability to communicate complex data findings clearly and effectively. It focuses on transforming raw data into visual narratives, allowing for more accessible interpretation and more informed decision-making processes. It emphasizes the importance of translating complex data analyses into understandable visual forms, enabling stakeholders to gain insights quickly and effectively.

 

DATA VISUALIZATION USING TABLEAU  

This module helps you understand Data Visualization using Tableau.

Capstone Project

Learners get their hands dirty with a real-time project under an industry expert’s guidance. The project covers everything from an introduction to Data Science and Data Analytics to complex concepts.

 

Year 2

In the second year of the MS in Data Science and Analytics Program, learners will be exposed to more specialized and advanced subjects of the field, allowing them to deepen their understanding and enhance their hands-on skills in Data Analytics. The curriculum for the second year is meticulously structured to cover a broad spectrum of themes, ensuring a well-rounded and comprehensive learning experience. By the end of the year, learners will be well-equipped with the advanced skills and knowledge needed to excel in their future careers in Data Science and Analytics.

Data Storage Technologies

Database storage technologies have transformed into complex systems that support knowledge management and decision support systems. This course takes a look at the foundations of database storage technologies. Students will learn about Database Storage Architecture, types of Database Storage Systems (legacy, current, and emerging), Physical Data Storage, Transaction  Management, Database Storage APIs, Data Warehousing, Governance, and Big Data Systems. The student will tie all this together to see how database storage technologies apply to Data Analytics.

Principles of Data Analytics

This course provides an overview of the knowledge that students gain successfully completing the MS Data Analytics curriculum. Each weekly session’s material presented focuses on one aspect of the principles of Data Analytics. The course acts as a roadmap with respect to an overview of the Data Analytics skill set students need to earn an MS.

Prescriptive Analysis

This course exposes the student to Prescriptive Analytics. It is used as a method of identifying competitive advantages in the enterprise by identifying the optimal solution. The students will learn how Prescriptive Analytics is used in business analytics and decision-making. Students will explore optimization and optimal decision-making models for the identification of optimal solutions.  Topics such as Simulation Modeling and multi-criteria decision-making are introduced. The students will be exposed to transformative findings that can be applied to make effective decisions. 

 

Research Methods and Ethics

The focus of this course is on exploring the three research method approaches: quantitative,  qualitative, and mix-method. Phases of research are discussed in detail with respect to identifying and translating research problems based on business needs into specific research questions,  developing hypotheses, determining independent and dependent research variables, and examining the validity and reliability of the research designs. Ethical considerations in research are deliberated considering APA and ASA ethics guidelines.

Data Analytics Capstone

The Capstone/Practicum Project provides the opportunity for integrating program learning within a project framework. Each student identifies or defines a professionally relevant need to be addressed that represents an opportunity to assimilate, integrate, or extend learning derived through the program. The student will work with the Capstone Project Advisor to develop a  proposal. After review and approval by the Capstone Project Advisor, the student is authorized to complete the project. The student presents the completed project at the end of the semester.