Master of Science (MS) in Business Analytics From Fairfield University

• Introduction to SQL
• Database Normalization and Entity Relationship Model(self-paced)
• SQL Operators
• Working with SQL: Join, Tables, and Variables
• Deep Dive into SQL Functions
• Working with Subqueries
• SQL Views, Functions, and Stored Procedures
• Deep Dive into User-defined Functions
• SQL Optimization and Performance
• Advanced Topics
• Managing Database Concurrency
• Practice Session

Case Study

Writing comparison data between past year to present year with respect to top products, ignoring the redundant/junk data, identifying the meaningful data, and identifying the demand in the future(using complex subqueries, functions, pattern matching concepts).

Excel Fundamentals

• Reading the Data, Referencing in formulas, Name Range, Logical
• Functions, Conditional Formatting, Advanced Validation, Dynamic Tables in Excel, Sorting and Filtering
• Working with Charts in Excel, Pivot Table, Dashboards, Data, and File Security
• VBA Macros, Ranges, and Worksheets in VBA
• IF conditions, loops, Debugging, etc.

Excel For Data Analytics

• Handling Text Data, Splitting, combining, data imputation on text data, Working with Dates in Excel, Data Conversion, Handling Missing Values, Data Cleaning, Working with Tables in Excel, etc.

Data Visualization with Excel

• Charts, Pie charts, Scatter, and bubble charts
• Bar charts, Column charts, Line charts, Maps
• Multiples: A set of charts with the same axes, Matrices, Cards, Tiles

Ensuring Data and File Security

• Data and file security in Excel, protecting rows, columns, and cells, and the different safeguarding techniques.

Getting started with Macros

• Learning about macros in Excel, executing macros in Excel, the macro shortcuts, applications, the concept of relative reference in macros, In-depth understanding of Visual Basic for Applications, the VBA Editor, module insertion and deletion, performing action with Sub and ending Sub if condition not met.

Statistics with Excel

• One Tailed Test and Two Tailed T-test, Linear Regression, Performing Statistical Analysis Using excel, Implementing Linear Regression with Excel

Introduction to Python and IDEs – The basics of the python programming language, how you can use various IDEs for Python development like Jupyter, Pycharm, etc.

Python Basics – Variables, Data Types, Loops, Conditional Statements, functions, decorators, lambda functions, etc.

Object Oriented Programming – Introduction to OOPs concepts like classes, objects, inheritance, abstraction, polymorphism, encapsulation, etc.

Data Manipulation with Numpy, Pandas, and Visualization – Using large datasets, you will learn about various techniques and processes that will convert raw unstructured data into actionable insights for further computations i.e. machine learning models, etc.

Case Study – The culmination of all the above concepts with real-world problem statements for better understanding.

Statistics and Descriptive Analytics using MS Excel

• Measure of central tendency, measure of spread, five points summary, etc.
• Probability Distributions, Probability in Business Analytics
• Probability Distributions, Binomial distribution, Poisson distribution, bayes theorem, central limit theorem.

Python for Descriptive, Diagnostic, and Inferential Statistics

• Correlation, covariance, confidence intervals, hypothesis testing, F-test, Z-test, t-test, ANOVA, chi-square test, etc.

Prescriptive Analytics

Introduction to Machine learning

• Supervised, Unsupervised learning.
• Introduction to scikit-learn, Keras, etc.

Regression

• Introduction to classification problems, Identification of a regression problem, dependent and independent variables.
• How to train the model in a regression problem.
• How to evaluate the model for a regression problem.
• How to optimize the efficiency of the regression model.

Classification

• Introduction to classification problems, Identification of a classification problem, dependent and independent variables.
• How to train the model in a classification problem.
• How to evaluate the model for a classification problem.
• How to optimize the efficiency of the classification model.

Clustering

• Introduction to clustering problems, Identification of a clustering problem, dependent and independent variables.
• How to train the model in a clustering problem.
• How to evaluate the model for a clustering problem.
• How to optimize the efficiency of the clustering model.

Supervised Learning

• Linear Regression – Creating linear regression models for linear data using statistical tests, data preprocessing, standardization, normalization, etc.
• Logistic Regression – Creating logistic regression models for classification problems – such as if a person is diabetic or not, if there will be rain or not, etc.

Unsupervised Learning

• K-means – The k-means algorithm that can be used for clustering problems in an unsupervised learning approach.
• Dimensionality reduction – Handling multi dimensional data and standardizing the features for easier computation.

Making use of time series data, gathering insights and useful forecasting solutions using time series forecasting.

• Regression and Multivariate Analysis
• Classification problems in machine learning
• Data Multidimensionality and Linear Algebra
• Feature engineering and Feature selection
• Hyperparameter Tuning and Other Optimization Techniques

Business Domains – Learn about various business domains and understand how one differs from the other.

• Finance
• Marketing
• Retail
• Supply Chain

Understanding the business problems and formulating hypotheses – Learn about formulating hypotheses for various business problems on samples and populations.

Exploratory Data Analysis to Gather Insights – Learn about the exploratory data analysis and how it enables a foolproof producer of actionable insights.

Data Storytelling – Narrate stories in a memorable way – Learn to narrate business problems and solutions in a simple, relatable format that makes it easier to understand and recall.

Case Study

This case study will cover the following concepts:

• Create actionable insights from raw unstructured data to solve real-world business problems.

• Understand what Agentic AI is and it use
• Get to know what LLMs are, and how to use LLMs for reasoning and analysis
• Learn how to build a data assistant using Agno/ LangChain
• Evaluate and integrate Pandas, Excel (openpyxl), Matplotlib in your Agentic AI workflow
• Hands-on: Building an agent that can output CSV reports and auto-generated visualisations, by following the workflow below:
  • Load a CSV or Excel file
  • Clean and transform the data

KNIME

• Introduction to KNIME – learn about the KNIME tool that can be quite efficient for data analytics, creating workflows, etc.
• Working with data in KNIME – Learn about creating workflows, loading datasets in KNIME, etc.
• Loops in KNIME – Learn about the loops in KNIME that enables efficient data transformation in KNIME
• Feature Selection, Hyperparameter optimization in KNIME – Learn about hyperparameter optimization, feature selection in KNIME that will enable efficient machine learning models.
• Case Study: Using KNIME to create end-to-end machine learning models with various algorithms like linear regression, logistic regression, decision tree, random forest, etc.

Feature Selection – Feature selection techniques in Python that include recursive feature elimination, Recursive feature elimination using cross-validation, variance threshold, etc.

Feature Engineering – Feature engineering techniques that help in reducing the best features to use for data modeling.

Model Tuning – Optimization techniques like hyperparameter tuning to increase the efficiency of the machine learning models.

Power BI Basics

• Introduction to Power BI, Use cases and BI Tools, Data Warehousing, Power BI components, Power BI Desktop, workflows and reports, Data Extraction with Power BI.
• SaaS Connectors, Working with Azure SQL database, Python and R with Power BI
• Power Query Editor, Advanced Editor, Query Dependency Editor, Data Transformations, Shaping and Combining Data ,M Query and Hierarchies in Power BI.

DAX

• Data Modeling and DAX, Time Intelligence Functions, DAX Advanced Features

Data Visualization with Analytics

• Slicers, filters, Drill Down Reports
• Power BI Query, Q & A and Data Insights
• Power BI Settings, Administration and Direct Connectivity
• Embedded Power BI API and Power BI Mobile
• Power BI Advanced and Power BI Premium

Case Study:

This case study will cover the following concepts:

• Creating a dashboard to depict actionable insights in sales data.

Problem Statement and Project Objectives – You will learn how to formulate various problem statements and understand the business objective of any problem statement that comes as a requirement.

Approach for the Solution – Creating various statistical insights-based solutions to approach the problem will guide your learnings to finish a project from scratch.

Optimum Solutions – Formulating actionable insights backed by statistical evidence will help you find the most effective solution for your problem statements.

Evaluation Metrics – You will be able to apply various evaluation metrics to your project/solution. It will validate your approach and point towards shortcomings backed by insights, if any.

Gathering Actionable Insights – You will learn about how a problem’s solution isn’t just creating a machine learning model; the insights that were gained from your analysis should be presentable in the form of actionable insights to capitalize on the solutions formulated for the problem statement.

• Introduction to R
• R Packages
• Sorting DataFrame
• Matrices and Vectors
• Reading Data from External Files
• Generating Plots
• Analysis of Variance (ANOVA)
• K-Means Clustering
• Association Rule Mining
• Regression in R
• Analyzing Relationship with Regression
• Advanced Regression
• Logistic Regression
• Advanced Logistic Regression
• Receiver Operating Characteristic (ROC)
• Kolmogorov–Smirnov Chart
• Database Connectivity with R
• Integrating R with Hadoop
• R Case Studies

• Introduction to Generative AI in Data Analytics
• Best Practices for Creating Effective Prompts
• Common Prompt Engineering Techniques
• Generative AI Tools and Techniques for Data Analytics
• Data Preparation with Generative AI
• Data Analysis and Insights Generation
• Data Visualization Using Generative AI
• Case Studies: Successful Applications of Generative AI in Data Analytics
• Ethical Considerations and Challenges
• Future Trends in Generative AI for Data Analytics
• Hands-on Practice and Application