Master of Business Administration (MBA) in Business Analytics & AI

Course Instructor SkillArbitrage

To enroll in this course, please contact the Admin

Course Overview

The Rushford 100% online MBA is perfect for grads and professionals aiming for success in business. With a self-paced, tech-driven learning environment, it combines the latest knowledge sources for a top-notch experience. This hands-on program not only covers business basics but also hones skills applicable in real workplaces. Our MBA lets graduates fast-track their careers with practical knowledge and skills. Taught by industry experts, it provides a competitive edge with a respected European MBA degree. Each student gets an industry mentor for real-world guidance, matched to their field or aspirations. No formal bachelor’s degree is required, thanks to Recognition of Prior Experience (RPE).

Schedule of Classes

Course Curriculum

7 Subjects

Module 1 : Business Fundamentals

21 Learning Materials

Induction PPT

PDF

Class Notes

Marketing Management Basics

Audio

Operation Research Basics

PPT

Statistics for Business

PPT

PY202 class notes

Linear Algebra using Numpy

ZIP

Basics of Series and DataFrames

ZIP

Filtering Using Pandas

ZIP

Filtering Using Pandas

ZIP

L5: Extracting and Aggregating Pandas

ZIP

L6: Matplotlib and seaborn

ZIP

L7 Plotly and Cuffinks

ZIP

Project Session

ZIP

PY203 Class Notes

Day 3: Advance Data Cleaning Concepts

ZIP

Day 5: Feature Extraction and Transformation

ZIP

Day 3: Advance Data Cleaning Concepts

ZIP

Day 5: Feature Extraction and Transformation

ZIP

Day 2: Exploring and Visualization the missing values

ZIP

PY 204 Class Notes

L3: Polynomial Linear Regression

ZIP

Logistic Regression

ZIP

Advanced Clustering Techniques

ZIP

PY204 Notes

L1: Introduction to Decision Trees

ZIP

PY 202: Python For Data Science _ Data Viz (Pandas)

13 Learning Materials

Class Notes[MBA-RBS-FEB24-2 ]

Linear Algebra using Numpy

ZIP

Basics of Series and DataFrames

ZIP

Filtering Using Pandas

ZIP

Filtering Using Pandas

ZIP

L5: Extracting and Aggregating Pandas

ZIP

L6: Matplotlib and seaborn

ZIP

L7 Plotly and Cuffinks

ZIP

Project Session

ZIP

class Notes

Day 3: Advance Data Cleaning Concepts

ZIP

Day 5: Feature Extraction and Transformation

ZIP

Day 3: Advance Data Cleaning Concepts

ZIP

Day 5: Feature Extraction and Transformation

ZIP

Day 2: Exploring and Visualization the missing values

ZIP

PY 203 : Data Cleaning and Preparation

PY 206: Time Series Analysis & Forecasting

60 Learning Materials

Class Notes[DSCUS- OCT/DEC23]

Day 5: Machine learning models for time series forecasting

ZIP

Class Notes[12pm Vallabh Batch]

Time series analysis and forecasting

ZIP

Time Series L2

ZIP

Time_Series_L3_Exponential_smoothing

ZIP

Day 6: Time series forecasting using prophet

ZIP

Project Session

ZIP

Class Notes[DSCAF-FEB24]

Day 1: Introduction to Time Series Analysis

ZIP

Day 3: Time series forecasting using arima

ZIP

Day 4:Exponential Smoothing models for time series forecasting

ZIP

Day 6: Time series forecasting using prophet

ZIP

Day 6: Time series forecasting using prophet

ZIP

Class Notes[DSCUS-NOV23]

Time Series Project

ZIP

Time_series_analysis_

ZIP

Class Notes[DSCUS-06-MAR24]

L3 - ARIMA

ZIP

Preprocessing_Time_Series_Data

ZIP

Preprocessing_Time_Series_Data

ZIP

Preprocessing_Time_Series_Dat

ZIP

L4 Exponential Smoothing Models

ZIP

Day 5: Machine learning models for time series forecasting

ZIP

Day 6: Time series forecasting using prophet

ZIP

Class Notes[DSCUS-JUL23]

Day 1: Introduction to Time Series Analysis

ZIP

Day 1: Introduction to Time Series Analysis

ZIP

Smoothing techniques and fbprophet

ZIP

Class Notes[DSCUK-April24]

Day 1: Introduction to Time Series Analysis

ZIP

Day 2: Preprocessing and visualization of timeseris data

ZIP

Day 3: Time series forecasting using arima

ZIP

Day 4:Exponential Smoothing models for time series forecasting

ZIP

Day 5: Machine learning models for time series forecasting

ZIP

Class Notes[DSCUS-JAN24]

Day 2: Preprocessing and visualization of timeseris data

ZIP

TIME SERIES (INTERNATIONAL AIRLINES) (MAR 02, 2025)

ZIP

Day 5: Machine learning models for time series forecasting

ZIP

Day 6: Time series forecasting using prophet

ZIP

Time series forecasting using arima

ZIP

Time series forecasting using prophet

ZIP

Class Notes[DSCMO-MAR24+DSCMO-May24]

Time_Series_Introduction

ZIP

Time_Series_Day_2

ZIP

Time_Series_Modelling

ZIP

Time_Series_Modelling_Day_2

ZIP

Time Series Forecasting using ARIMA

ZIP

Exponential smoothing models Time series

ZIP

Exponential Smoothing Models

ZIP

Project Session

ZIP

PY 205 : Tree Based and Boosting Models

92 Learning Materials

Class Notes(DSCUS-JUL23)

Agglomerative Clustering

ZIP

Quick Notes

ZIP

DecisionTree

ZIP

Decision Tree Matrix

ZIP

Decision Tree

ZIP

Cross Validation

ZIP

L3: Introduction to the Concept of Bagging

ZIP

L4: Introduction to Concept of Random Forest

ZIP

L5: Introduction to Boosting

ZIP

L6: Introduction to extreme Gradient Boosting

ZIP

L6: Introduction to extreme Gradient Boosting

ZIP

L6: Introduction to extreme Gradient Boosting

ZIP

L6: Introduction to extreme Gradient Boosting

ZIP

L8: Introduction to Recommendation Engines

ZIP

Imbalance Techniques

ZIP

L8: Introduction to Recommendation Engines

ZIP

Recommendation Engine

ZIP

Class Notes(DSCUS-OCT23)

Introduciton to Random Forest

ZIP

Introduction to Boosting

ZIP

L7: Introduction to Imbalanced Machine Learning models

ZIP

L7 Introduction to Imbalanced Machine Learning models

ZIP

L8: Introduction to Recommendation Engines

ZIP

L8: Introduction to Recommendation Engines

ZIP

L8: Introduction to Recommendation Engines

ZIP

L8: Introduction to Recommendation Engines

ZIP

Class Notes

L1: Introduction to Decision Trees L2: Implementation of Decision Trees

ZIP

L3: Introduction to the Concept of Bagging

ZIP

L4: Introduction to Concept of Random Forest & Introduction to Boosting

ZIP

L5: Introduction to Boosting(Contd.) & Introduction to extreme Gradient Boosting

ZIP

Class Notes[DSCUS-NOV23]

Decision_Trees

ZIP

L7: Introduction to Imbalanced Machine Learning models

ZIP

L7: Introduction to Imbalanced Machine Learning models

ZIP

L7: Introduction to Imbalanced Machine Learning models

ZIP

L8: Introduction to Recommendation Engines

ZIP

L8: Introduction to Recommendation Engines

ZIP

Project Session

ZIP

Class Notes[DSCEV-DEC23]

AdaBoost

PDF

L5: Introduction to Boosting

ZIP

L6: Introduction to extreme Gradient Boosting

ZIP

L8: Introduction to Recommendation Engines

ZIP

Project Session

ZIP

Class Notes[DSCAF-FEB24]

L1: Introduction to Decision Trees

ZIP

L3: Introduction to the Concept of Bagging

ZIP

L5: Introduction to Boosting

ZIP

Class Notes[DSCEV-JAN24]

L1: Introduction to Decision Trees

PDF

L2: Implementation of Decision Trees

ZIP

Machine_learning_bagging

ZIP

Machine_learning_boosting

PDF

Machine_learning_Adaboost

PDF

Machine_learning_xg_boost

PDF

Machine_learning_Recommendation_Engines

PDF

Machine_learning_Gradient Boosting

PDF

Class Notes[DSCUS-6MAR24]

L2: Implementation of Decision Trees

ZIP

L4: Introduction to Concept of Random Forest

ZIP

L5: Introduction to Boosting

ZIP

Introduction_to_Extreme_Gradient_Boosting

ZIP

Introduction_to_Imbalanced_Machine_Learning_models

ZIP

Class Notes[DSCUK-APR24]

L1: Introduction to Decision Trees

ZIP

L1: Introduction to Decision Trees

ZIP

L3: Introduction to the Concept of Bagging

ZIP

Introduction_to_Random_Forest

ZIP

Introduction_to_Boosting

ZIP

Introduction to extreame radiant boosting

ZIP

Introduction_to_Imbalanced_Machine_Learning_models

ZIP

Another_copy_of_Recommender_Systems_Material

ZIP

Class Notes[DSCUS-JAN24+DSCUS-FEB24-1+DSCUS-FEB24-2+DSCUS-FEB24-3]

L1: Introduction to Decision Trees

ZIP

Machine_Learning_Introduction_to_the_Concept_of_Bagging

PDF

L3: Introduction to the Concept of Bagging

ZIP

Introduction_to_Boosting

ZIP

L7: Introduction to Imbalanced Machine Learning models

ZIP

Recommendation systems

PDF

Project Session

PDF

L8: Introduction to Recommendation Engines

ZIP

Class Notes[DSCMO-MAR24+DSCMO-May24]

L1: Introduction to Decision Trees

ZIP

L2: Implementation of Decision Trees

ZIP

L3: Introduction to the Concept of Bagging

ZIP

L4: Introduction to Concept of Random Forest

ZIP

Clustering_Day_1

ZIP

L2: Implementation of Decision Trees

ZIP

Class Notes[DSCEV-APR24]

L8: Introduction to Recommendation Engines

ZIP

L8: Introduction to Recommendation Engines

ZIP

Class Notes[DSCAF-JULY24+DSCAF-JUNE24]

Introduction to Decision Trees

PDF

PY 204 : Machine Learning

5 Exercises • 186 Learning Materials

Lecture 1: Modelling with Linear Regression

Simple Linear Regression

ZIP

Multiple Linear regression

ZIP

DPP

PPT

kc_house_data

Audio

Linear regression

PPT

Lecture 2: Regularisation Techniques

Regularization

ZIP

DPP

PDF

Lecture 3: Regularisation Techniques II

Regularization-2

ZIP

Regularization

PPT

practice questions regularization

PDF

Lecture 4: Modelling with Logistic Regression

Logistic regression

ZIP

Logistic Regression

PPT

DPP

PDF

Lecture 5: L5 Understanding other classification algorithm like KNN _ SVM

Understanding other classification algorithm like KNN _ SVM

PPT

Understanding other classification algorithm like KNN _ SVM

ZIP

DPP

PDF

Lecture 6: L6_ Advanced Model Evaluation Techniques

Evaluating Models _ Feature selection (classification _ Regression)

PPT

Evaluating models and feature selection sample code-3

ZIP

Naive bayes

PDF

DPP

PDF

Lecture7: Introduction to Clustering and K Means

Class 6 Introduction to Clustering and K Means

PDF

K-means

ZIP

DPP

PDF

Lecture 8: Advanced Clustering Techniques

Advanced Clustering Techniques

PDF

Hierarchical Clustering

ZIP

DPP

PDF

Assignment [ MBA-RBS-MAY24/MBA-RBS-FEB24-2]

Weekly Exercise on Logistic Regression - March2025

Assignment

Weekly_Exercise_on_Linear_Regression_-_March2025.pdf

Assignment

Weekly Exercise on Support Vector Machine (SVM) - March2025

Assignment

BI Tools and Dashboarding

2 Exercises • 29 Learning Materials

Learning Material

L1_Introduction to Tableau

PDF

L2_Data_Sourcing_and_Preparation

PDF

L3_charts and graphs

PDF

L3_charts_and_graphs_worksheets

ZIP

L4_Grouping Filtering and LOD

PDF

L4 LOD

ZIP

Sample Superstore

ZIP

Tableau Practice Questions (Superstore Dataset)

PDF

Practice Sets

Sample Superstore

ZIP

Tableau Practice Questions (Superstore Dataset)

PDF

Practice Assignment- I

ZIP

Practice Assignment - II

ZIP

Tableau Practice Questions Answers

ZIP

Assignment

Dashboarding Assignment I

Assignment

Dashboarding Assignment - II

Assignment

Tableau Project

Project

ZIP

Class Notes[MBA-RBS-Feb24 + MBA-RBS-MAY24]

Data visualization with tableau

ZIP

ALPHAMART’S SALES

ZIP

Data Filters(Data Segmentation)

ZIP

Data Filters(Data Segmentation)

ZIP

Class Notes[DSCEV-DEC23 + DSCUS-OCT23 + DSCUS-DEC23]

DATA VISUALIZATION WITH TABLEAU

ZIP

DATA VISUALIZATION

ZIP

Course Instructor

SkillArbitrage

197 Courses • 238309 Students