MS Data Analytics

The curriculum is designed by leading experts and practicing industry professionals at Walsh College and Great Lakes. It is specifically curated to cover a range of Data Science skills, starting from the fundamentals and progressing to more complex, hands-on applications.

Year 1

Data Science Foundations

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

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

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

DATA VISUALIZATION USING TABLEAU  

This module helps you understand Data Visualization using Tableau.

Capstone Project

Learners can get their hands dirty with a real-time project under an industry expert’s guidance.  This covers everything from an introduction to Data Science and Data Analytics to complex  concepts. Successful completion of the project will earn you Post Graduate Certificate. 

 

Year 2

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 this all 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 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 in identifying  competitive advantages in the enterprise by identifying the optimal solution. The students will  learn how Pescriptive Analytics is used in business analytics and decision making. Students will  explore optimization and optimal decision-making models for 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 actioned into 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.