MS Data Analytics

At Walsh College and Great Lakes, top experts and working professionals actively design the curriculum for the Master of Science in Data Analytics program. It teaches a wide range of data science and analytical skills, starting from the basics to advanced, practical applications.

Year 1

In the first year, you will learn Data Science, Business Analytics Techniques, and Data Visualization. It also includes a hands-on Capstone Project. Students will develop a comprehensive understanding of Data Analytics skills by the end of the first year.

Data Science Foundations

This module of our Data Analytics MS Program covers data analytics fundamentals. It includes essential topics such as Statistical Methods, Business Finance, Marketing and CRM, SQL Programming, and Python.
 
STATISTICAL METHODS FOR DATA SCIENCE 
In this topic, you will learn the basics and fundamentals of Data Science, including statistics and probability. 
 
BUSINESS FINANCE 
Business Finance is one of the most important topics in data analytics. Here, you will learn how business analytics works in a company. 
 
MARKETING AND CRM 
In this topic, you will learn skills to understand and meet customer needs effectively and to strategize business growth through data-driven insights and relationship building.

SQL PROGRAMMING 
This topic covers SQL programming, including DBMS, Normalization, Joins, and more.. 
 
PYTHON FOR DATA SCIENCE
In this topic, you will learn the key Python libraries like NumPy, Pandas, and visualization tools.
 

 

Data Science Techniques

This is the second module in this Data Analytics Master's Program. It builds on what you learned in the Foundations module. It introduces more advanced data analysis methods. This module shows you how to use different techniques and tools to understand data better and find solutions based on that data.

INFERENTIAL STATISTICS  
In this topic, you will learn advanced statistical techniques such as Variance, Regression, and Dimension Reduction.

PREDICTIVE MODELING  
Predictive Modeling is a process of building a model that can predict data. In this topic, you will learn various Predictive Modeling techniques used in Machine Learning. 

MACHINE LEARNING - I  
In this topic, you will gain a thorough understanding of Supervised and Unsupervised Learning techniques, Decision Trees, Neural Networks, and more.

MACHINE LEARNING - 2  
In this topic, you will learn more about processing data for modeling and use it to identify patterns.

TIME SERIES FORECASTING  
This block will teach you to predict future values based on the previously experimented values. 

SELF-PACED MODULES: OPTIMIZATION TECHNIQUES  
In this topic, you will learn all the optimization techniques used in Deep Learning. 
 

Domain Exposure

This module will teach you how Data Analytics works in various industries. It will explain how it helps improve things like supply chains, predict market trends, and understand customer behavior. You will also develop skills to use Data Analytics to make key decisions in various fields.

SELF-PACED MODULE: DEMYSTIFYING CHATGPT AND APPLICATIONS  
This is a self-paced module where you will learn about ChatGPT. You will understand how it works and its uses in business and education. You will also learn to tweak it to get better results.

MARKETING AND RETAIL ANALYTICS  
This topic focuses on some basic terminologies used in marketing and delves into the application of Retail Analytics. 

WEB AND SOCIAL MEDIA ANALYTICS  
In this topic, you will learn about Google Analytics, social media campaigns, and text mining for websites. You will also acquire an understanding of social media analysis.

FINANCE AND RISK ANALYTICS  
This topic covers basic terms used in Finance and risk Analytics, such as Credit Risk Models, PD Models, and Market Risk. 

SUPPLY CHAIN & LOGISTICS ANALYTICS  
In this module, you will learn about terms like RNN, managing inventory, and forecasting. 
These concepts are crucial for optimizing supply chain operations and predicting future trends, enhancing efficiency and decision-making in business environments.
 

Visualization and Insights

This module teaches you how to communicate complex data findings clearly and effectively. It focuses on turning raw data into visual stories, simplifying interpretation and enhancing decision-making. Moreover, it highlights the importance of presenting complex data analyzes in understandable visual forms, allowing stakeholders to quickly and effectively gain insights.
 
DATA VISUALIZATION USING TABLEAU  
This topic will help you understand data visualization using Tableau. You will learn how to create and interpret interactive charts, graphs, and dashboards, helping you to effectively present and analyze data in a visually engaging way. This skill is essential for making informed decisions based on complex data sets.

Capstone Project

In the first year, you will work on a capstone project with help from an industry expert. The project starts with basic Data Science and Data Analytics concepts and moves to more complex topics.

 

Year 2

You will explore more specialized and advanced topics in the second year of the Data Analytics MS Program. This deepens their understanding and improves their practical skills in Data Analytics. The curriculum carefully covers a wide range of subjects for a thorough learning experience. By the end of the year, you will have the skills and knowledge you need for a successful career in Data Science and Analytics.

Data Storage Technologies

This course explains how database storage technologies have grown to help manage data and make decisions. You will learn how to use various databases for data storage. They will also learn how to apply big data tools for data analytics.

Principles of Data Analytics

This introductory course outlines the key knowledge and skills needed for a Master's in Data Analytics. Each week, you will explore a different aspect of data analytics, building a foundation for their future careers.

Prescriptive Analysis

In this course, you will learn about Prescriptive Analytics, which helps businesses find the best solutions and make informed decisions. The course covers topics like decision-making methods, simulation, and optimization models. These topics help figure out the best thing to do in business settings.

 

Research Methods and Ethics

This course explores different research methods—quantitative, qualitative, and mixed-method. You will learn how to start a research project, develop questions and hypotheses, and ensure your research is valid and ethical. The course follows APA and ASA ethical guidelines.

Data Analytics Capstone

The capstone project allows you to apply what you have learned in a real-world project. You will choose a relevant issue to address, develop a project proposal with an advisor, and complete and present their project at the semester's end upon approval.

Complementary Learning Modules

*Non Credit Module

 

Prompt Engineering

Generative AI

Deep Learning